I'm a research scientist at the Carnegie Geophysical Lab. And I'm Smeyn Zerug, research director with Slumberjee. Hi, I'm John McGahey. I'm president of Mirage of Science, consulting and software company in Earth modeling in the mining business. Hi, I'm Paul Johnson. I'm at Los Alamos National Laboratory Geophysics and Geoscience in general. Hello, I'm Dan Connell, vice president of business development and technology with Consol Energy and a member of this committee. Good morning, I'm John Sirac, I'm a professor in geosciences at Virginia Tech. Yeah, good morning, my name is Steve Enders, I'm currently department head for geology and geological engineering at the Color School of Mines, but moving into a leadership role as director of their frontiers initiatives. I'm Lance Waller in the department of biostatistics and bioinformatics in the School of Public Health at Emory University and a member of the board of math sciences and analytics. I'm John Holm, professor of geosciences at Virginia Tech. I'm Russell Hewitt, I'm a computational scientist and professor of mathematics at Virginia Tech. I'm Deb Wixon, I'm a senior program officer with the board on earth sciences and resources here at the academy. I'm Carmen Agaritas, I'm an extension associate professor at the University of Kentucky and a member of the committee. I'm Dorothy Merritt, geologist, professor at Franklin and Marshall College and a member of this committee. I'm Joel Renner, geologist, retired from the Idaho National Laboratory and now I'm a sole member of the committee. Good morning Jerry Meis, I'm an environmental regulatory and litigation practitioner here in D.C. and a member of the committee. Good morning everyone and welcome, I'm Elizabeth Ada and I'm with the National Academy staff. Good morning, I'm Bill VanderMeer, I'm a project officer with the geothermal technologies office, DOE. I'm Elizabeth Metcalf, I'm with the geothermal technologies office in the U.S. Department of Energy. Hi, Sean Ports also with DOE in the geothermal technologies office. Zach Ruhn, fellow with the geothermal technologies office. Chris Moses, director of geology, geophysics and geochemistry science center in Denver with the USGS. Harvey Thurlison, state geologist of Minnesota. Thomas Halsey, chief computational scientist at ExxonMobil. Tyler Cloughcorn, program officer here at the academies for the board on mathematical sciences and analytics. Laila Howard, intern for American Geosciences Institute. Christopher Keane, director of geosciences profession in higher education at the American Geosciences Institute. I'm Jackie Richards, I'm also an intern at the American Geosciences Institute. I'm Sophie Hansen and also an intern at American Geosciences Institute. I'm Sarah Riker, I'm the USGS acting associate director for energy and minerals. Good morning, my name is Darren Damiani, I'm the carbon storage program manager for the USGE office of fossil energy. How do you? I'm Kelly Rose, I'm a geology geodata scientist with the National Energy Technology Lab. Hello, I'm Grant Rommel, senior fellow for geological environmental systems at the National Energy Technology Laboratory. Jennifer Bauer, geodata scientist at the National Energy Technology Laboratory. I'm Dave Cole, professor of school of sciences at Ohio State University. Okay, well we have a really great attendance here and do we have some folks online as well? Do we? Okay, we're not going to, we're going to try to remember to keep them in mind and so we again remember to use your mics, but also they'll have a way to flag us I guess if they have some questions or input or four things like that. So that's good. Well, so I talked a little bit about the committee being a convening space and that's kind of what we do, but one of our key roles is to be an incubator of idea. And so when we do, so twice a year we have, for those of many of you are familiar with our meetings, but some may not be. So we typically meet two times a year in the spring and in the fall and we look for a topical idea to be the focus of our meeting to engage this Earth Resources community and hopefully we're finding ideas that are, there are, there's some component that would be useful to advance the understanding of or to carry forward. And the purpose of the meeting then is to try to sort through that to see where that might lead in terms of other types of national academies work. That might, might delve into that topic in more detail. So in some of these, so at the end of our meetings we generate, we are, our committee generates a little one, you know, page or page and a half document that we kind of keep our catalog of ideas. And this, this topic of, started as just subsurface data a few years ago, which was more focused on the, you know, all the variety of data sets that were out there. And, and how to access them, which I think that, that issue, perhaps is still, still out there. But it's kind of evolved into the whole area of machine learning of you have all these data, these data sets are all in different kinds of, of quality and and there's, and how do you really make, how do you turn that into a value. And so, so I think, so we're continuing to, to develop that idea, I think today is going to be a very, I'm very much looking forward to it to advance the understanding of the aspects of this topic. I'll share just so, so just before we get into the agenda, let me just share. So, so our day goes this, this kind of information gathering synthesis goes through kind of mid afternoon and then we take a break and then we're going to invite everybody that is wishes to come back together for, for the last session, which is really the session that in that is okay from a national academy standpoint, what can we do with this, how can we, how can we take this forward and are there folks that are interested so in, in, so, so I share that now so as we work through the day you kind of kind of jot down some ideas so you're ready to come back for that last session and, and help us figure out and how, how we can work together as a community to, to really take this topic forward for that with the National Academy so I guess with, with that I will, I will turn this the, the turn this over to Dan and who's going to be our, our moderator for the keynote and first session. Thanks very much Jim and again good morning to all of you and thank you for joining us here today on behalf of the entire committee. As, as mentioned I'm Dan Connell I work for Consol Energy out of Pittsburgh. We are primarily a coal mining company so certainly understanding subsurface data and innovative ways in which we can use it is very much of interest to us from an industrial perspective, looking forward to learning a lot here today and I think we're going to kick things off in a very strong fashion with the keynote speaker and then the first panel that we, we have assembled. We're going to kind of seamlessly flow from the keynote into that first panel and then hopefully just before lunch, conclude with with a discussion type format so if you have questions that are specific very specific to any one of the talks will allow a minute or two after each talk to get those taken care of but otherwise I think our intent is to have a more interactive panel discussion, following, following all of the, all of the presentations. Our keynote today, we're very fortunate to have called Johnson join us from from Los Alamos National Laboratory. On our industrial panel, we have John McGahey from ERA Geosciences who's going to bring us kind of a mining and mineral exploration perspective on opportunities in subsurface data and machine learning. Simone Zerug who's going to bring us kind of the oil and gas E&P perspective from from his work at Schlumberger and then Shauna Morrison who I think is going to give us a very interesting perspective from from her work at the Carnegie Science Geophysical kind of at the interaction intersection of Earth space and life sciences that she describes it in her bio so should be a rich discussion with with a lot of perspectives and I think what we're what we're hoping to do here. And, and Paula is going to lead the way and kicking this off is really better understand what does all of this mean I mean you hear buzzwords machine learning big data artificial intelligence. tend to think boy these are these are solutions to just about any problem. And I think there are tremendous opportunities to solve a lot of questions with with the data and computing technologies that are that are rapidly developing. But getting an understanding of what all this means specifically in the context of subsurface data, where the true opportunities lie and where where maybe there are questions that these are not the best solutions for. The key part of the discussion today. Also, I think in particular with some some industrial perspectives. We hope to dig into the questions around data sharing and publicly available data and and some of the limitations just surrounding getting data into the right format and making it available for use. In machine learning and AI applications and maybe where the where progress has been made and where there are still challenges to be to be solved in that area. And then finally, you know, I think all of the speakers are going to be able to share with you some some real world success stories and maybe some real world challenge stories about application of these technologies and where there may still be gaps that require further technology development that the academies can can consider so with that introduction setting the stage. Like to welcome Paul Johnson to kick things off today. Hi good morning. Really an honor to be in this building I've never been here before and quit on inspiring To move on my presentation. The goal here is is not to offend everybody, but I think I'll succeed in doing that. Meaning the it's really geared at people who know very little to nothing about artificial intelligence flash machine learning. And so for those I hope that this is something you'll learn something from and for those that are experts will just bear with me, knowing that there are people in the room that are really not very familiar with these topics. So with that I'll go ahead. I'll start with a brief introduction and background to machine learning, maybe a little more than brief. I'll talk a bit about I was going to talk about supervised learning applied to laboratory and fault physics that some of the work we're doing but you know I've elected to throw that out because of lack of time. So that's something that I can talk you about offline or you can read about in in articles that includes cascadia, but we'll go from kind of the overview and introduction to machine learning to the perspective and path to the future. So there are been quite a number of technological shifts over the last 100 or so years that have been revolutionary in Geoscience has had enormous impact. This is a list that I put together it's probably not comprehensive in fact I am sure it's not comprehensive. But if we look back to a little over 100 years ago, we were learning about the age of the earth from radiometric dating. The magnet the magnetometer that was invented in the 1930s and then towed behind battleships etc during World War two had enormous impact later and we'll get to that. Spacecraft and satellite in the early 60s, you know we have the we had tremendous number of interesting and important discoveries the origin of the KT extinction. Eventually GPS especially Earth imagery which is sort of the first thing that came out like Landsat for example many of you remember that these data are still with us. Then World War two just stepping back oceanic research vessels as I mentioned these were the vessels pulling the magnetometers behind them and discovering that there were magnetic stripes in the basalt in the ocean. And therefore that led to the proof that plate tectonics was the theory plate tectonics was correct. That was sort of the the final straw in terms of the information needed to prove it so that was huge. Then we look forward to widely available computers in the 1980s. I know I got my first desktop in the early 1980s and I was thrilled and it changed everything I did. The invention of the World Wide Web in the late 80s early 90s energy technology advances such as horizontal drilling in the 90s that is completely revolutionized this country in terms of energy extraction and energy independence it's truly remarkable. Fast computers starting in the 80s dramatic advances in waveform inversion important to people in this room. Large scale simulation etc. And then by the early 90s or so we had GPS and INSAR and those also dramatically have changed how we view the earth our ability to image both primarily fault. Quick movements in the earth quick displacements such as a fault slow displacements are harder but can be done. And gaming and GPUs for the present and the importance they have in terms of artificial intelligence and machine learning. And then at present I don't know if you can read that but you know the confluence of machine learning big data has and super fast computers has led us to where we are today and the focus of this meeting today. So just you know I won't go into details of the scales of our problems and again this is probably an incomplete list but you know we look at very very quick scale phenomena such as earthquake faulting. We have longer scale kinds of phenomena where we have fluid and gas extraction leakage fluid flow and reservoirs over much longer time scales. We have induced earthquakes again on short time scales we have volcano eruptions on fairly on time scales that can bridge a lot of different time scales. Earth deformation due to tectonic and anthropogenic forcing. These are indeed long term effects and important to us in terms of understanding what's going on underground when we do extraction or injection of fluids. And fault flow slips was very important in regards to the tectonics of the earth. So let's see I'll try to actually point to here so I'm pointing over there and many of you can't see so I'll do that. So machine learning let's go to some background. So this is thanks to Rich Baraniak at Rice University. I love this image. So I mentioned the confluence of big data, big computers and deep architectures really that's where we are today. In the last five years it has just exploded in terms of the evolution of the deep architectures the last seven years or so the computers are just getting bigger. The big data is getting massively bigger very rapidly. So as everybody in this room knows there's tremendous hyperbole and tremendous promise at the same time. The data availability has increased in by at least 100 by 1000 fold and key algorithms have improved 10 fold to 100 fold. Hardware speed has improved by at least 100 fold in the last two decades alone. And this is a fairly profound statistic that 90% of the digital data in the world today has been created in the past two years alone. That's just remarkable. And well there's the singularity and where does that lead us? There's a lot of discussion at government levels inside in scientific circles about what's going to happen? How is society going to deal with this? How are countries going to deal with this sort of new frontier which we may be facing? And none of us know how this is going to pan out but we have to think hard about it and it's really important. At the same time you know that people worry right now that computers will get too smart and take over the world.  And if you think about your experiences phoning your doctor, phoning Verizon, phoning AT&T, phoning an airline, what is it like? You're stuck in some infinite loop where you rarely get to talk to a person. And these are driven by programmers but these are computers. So that's where we are today in fact in everyday life for many of the many of the advances that have been made. So this is also a figure I stole from Rich Peroniak at Rice, the hype cycle for emerging technologies. This is as of 2018. So on the bottom axis we have time and on the y-axis we have expectations. And so this is typical of the hype cycle. This is not just associated with what's going on today with artificial intelligence and machine learning. But we see that we're somewhere right in here right now in particular deep learning. We're possibly at the cliff here and possibly very soon at the trough of disillusionment. You know this is just bound to happen right? It's been so hyped and so overhyped this whole domain that it's got to happen because not all of our expectations are going to be met. Some will, some won't. There'll be a lot of bad work out there and there'll be a lot of good work out there. It'll be hard to tease out what is good and what is bad by your average person. But following that is the slope of enlightenment and then the plateau of productivity where we hope we get sooner than later. So that's what we might be able to expect over the next decade. I just saw this yesterday. I was on the airplane coming from France. I was reading Le Monde, sort of the New York Times of Paris. And I found this, I was reading the science section and I found this interesting article on machine learning. And this is just the numbers of individuals working in these areas in different countries. So you can see the US is, in terms of pure numbers, if we can believe these numbers, well advanced when we compare to even China, which really surprised me, the UK, then Canada, Japan, Australia. And then you see Germany, France, India, and Italy here. So that just sort of made me feel somewhat comforted the fact that we have so many people working in this area. It doesn't mean we're better than other people. We have a lot more people working in the area and that's got to help us. So let's get into a little bit of the details. Classically trained as scientists were domain experts. We know a certain domain and the scientific method means we pose a problem and we hypothesize a solution and we look for a means to get there. And it may just be as simple as doing Fourier analysis or something like that as an example that will take you from that hypothesis and the data sets you're looking at to some solution and interpretation. In the world of machine learning, we're looking through the machine learning lens, you're posing similar questions, but they tend to be harder questions. That is to say you may have some data set that you're trying to map to some variable of interest. In our case, for example, we might be studying seismic data and we might be asking the question is what can it tell us about fault displacement at the Earth's surface. There's no simple mapping as it turns out. And that's where machine learning comes in. So basically you're building a function in this space. This function is f of x, we'll call it, and this function is building using training data. It's building itself so that it tries to determine whether or not it can make such a mapping. So it handles much, much more complex problems and can explore a function space that is vast. The function space being maybe it's frequency effects, maybe it's the moments and signals, the variants or kurtosis, maybe it's some chemical signatures, but it's combining all of these in some simultaneous manner and iterating on that to build this model to see if it can make the mapping. And that's the great power of machine learning for especially supervised learning problems that you can solve problems you could never hope to solve using before we had machine learning. At the same time, you can get yourself into a lot of trouble and we'll come back to that. But that's the general idea that is really the difference between what we've done classically in science and what we're doing today in science. This function space I talked about is here and the function space where we live as researchers is somewhere in this red circle. And the function space that the machine learning algorithms can explore is this much larger space and getting bigger all the time. So this is conceptually what we're talking about in terms of the function space. So let's talk a bit about supervised learning. Not all there's unsupervised and supervised learning sort of broad categories. And the idea here is that you have an input data set and here I'm using acoustical or seismic data or some just some time series signal that you've got. And as I mentioned earlier, you're interested in some quantity of interest. This could be some chemical marker. This could be some physical effect on a fault. This could be some permeability of an underground reservoir. This could be displacement in that underground reservoir. That's the quantity of interest and you're trying to determine if the signal or signals you're looking at can make that mapping by building this f of x function. One does this not directly but by building what is known as a feature space and that feature space is comprised of statistics of the signal you're looking at. And so the operations that are developed or built inside the f of x are based on these features. So that's a very, very rough and sort of naive introduction to how this works but it's right. It kind of captures how this is done. The quantity of interest is known as the label. That's the jargon of machine learning. The input data is the input data and the machine learning algorithm is this function that I've just described. So there's also other kinds of unsupervised learning and we'll get to sort of the array of machine learning approaches fairly soon. But in unsupervised learning the idea is you don't know anything about the data. You have this data set and you really don't know necessarily what to do with it or you want to see what an algorithm can find out about it. What is contained in there? Is there information in there that you're not aware of? So for example, so in this case the computers learn to teach themselves and they model an underlying structure if it's there or distribution in the data if it's there or that's the hope. There is no correct answer and there is no teacher and that's the idea. The algorithms are truly left to discover interesting structure in the data. And a simple example is classification or clustering. So for example, many of you people in the room have probably used something like K-means clustering and that's an example of this kind of unsupervised learning. So deep neural networks can be used for both unsupervised and supervised learning. Here was a really fun example published in about 2012. So this is now quite old, but this was a huge advance. So Google's artificial brain learned to find cat videos. So they looked at some very large, they used a very large number of computers. They never told the computer during the training, this is a cat. So they didn't use labeled data themselves. Jeff Dean, a Google fellow, blah, blah, blah. But the cat videos were in many cases labeled online. The idea that you throw a ton of data at the algorithm and the software automatically learns it from the data. So ultimately it did what we did, it started to find cats. So for deep learning or unsupervised and supervised applications, they're much, much more powerful than traditional machine learning algorithms. They're not always what we should turn to because they're opaque. You do not know what they did to get to the solution or if you do, you have to work very, very hard at it. So if you care about how you got to the solution, these aren't necessarily the best approaches to take. There are other approaches that are transparent where you can tease out exactly how you got to this, how the F of X got to the solution. But there are many applications where we just don't care. And I like to think of our telephone. When we hear a song on the radio, we want to ask Siri or Shazam, what is that song? Well, we don't care that the neural network, how the neural network gets there. We just want the name of the song and who the artist is. So it's really application dependent. So there are details about neural networks in deep, et cetera, but we won't go into that just for the sake of time. So I'll call this the zoo of techniques that machine learning is comprised of. There are many, many, many. So when you hear the word machine learning or artificial intelligence, it's really an array of many, many different kinds of approaches one can use. And herein lies the risk or the challenge, which is to say, which one do you use? And I encourage anybody in the room that's doing hands-on work, and those of you who are supervising people that are doing hands-on work, to think very, very hard about this very problem. Because if you run off using the wrong approach to solve a problem and you might get an answer that looks realistic or you think correct, and it could be absolutely wrong and you won't know that. So here's where the domain expert, that's us, and the machine learning expert, that's some of us, come together. The best way in our experience at my lab is we bring together people that are really well versed in machine learning. We have the domain experts who are asking the question and showing the machine learning experts the data, and they're working together for a solution. So that's how you address this problem of trying to decide which algorithms you're going to use, algorithm or algorithms. So let's talk a little bit about data approaches. Elizabeth asked me to, this is one of the topics she wanted me to hit. So, you know, I would say most data is appropriate. I think there are probably people in this room that can think of data that are just simply not appropriate. Data quality is absolutely key. So if you throw bad data at a machine learning algorithm, it's not going to be any better than throwing bad data at a domain expert doing some sort of analysis. It doesn't improve the quality, although there are ways to improve quality if you're focused just on that question. This idea of label data for unsupervised learning is truly fundamental. So you have to have good, reliable, labeled data sets. And that means that you've had to have somebody go in, an individual, almost certainly an individual go in and identify what something is as an expert in the area to create those labeled data. So if you have poor labeled data, just to say you have 80%, 20% of your data is bad, that could lead to serious problems. So machine learning is appropriate when you're working in an unknown and unexpected function space that must be explored. That is to say these bigger problems where you're looking for a way to get to, to make this mapping we've talked about in supervised learning, for instance, but you don't know how to get there, that's where machine learning really excels. When are other approaches appropriate? Well, simple questions may require simple approaches, okay? I mean, this is not a simple answer and this is not a simple question in itself. But if you can just take an FFT or do a chemical analysis and get the results you want, that's probably enough. If you're trying to tease out more information from the data sets you have, that's where machine learning comes in. It may be there's more information contained in there than you thought, which is what we're discovering and the work we're doing. And that's been truly remarkable. It's a data-driven learning experience of the physics of the problems we're working on. So what topics can machine learning help answer in GS science? Nearly a re-topic in GS sciences applications. I don't think off the top of my head I can think of a single problem that doesn't. This is just a list of some. There are examples here of GS science applications that include geologic mapping on that top row from Anya reading and her associates in Tasmania. There's simulation work by degrees, et cetera, in terms of improving simulation and doing simulations in entirely new ways without doing tomography necessarily. The tomographic model is being built inside the machine learning algorithm. Inversion, this is work by Martinda Hoop and Gupta and others. This is tomography in radically different ways using also deep learning. And discovery, this is some of our work where we're discovering new physics by looking at signals in new ways based on the machine learning approach we're using. When does machine learning fail? Well, there's nuisance variation in images.  So these kinds of variations and one can imagine even in satellite imagery this can lead you into problems. So the non-stationary data, what that means is that you have an evolving system where the data you collect today is system dependent for that instant in time and tomorrow or next year or 10 years from now that system has evolved so that the data are different because the physics of the system are different. And you have to know that about the system before you start throwing machine learning at this kind of tool at these problems once again, if you try to throw machine learning to a non-stationary problem you can get yourself into real trouble, although again there are ways to deal with it if you're dealing with people, working with people that know what they're doing. So the randomness or entropy, you cannot learn a pattern that does not exist, okay? So you throw data at a problem, you're trying to learn a pattern and maybe there is no pattern, but you work hard enough at it that you get a pattern. So again, a note of caution. A lack of training data, this is really an important problem and why machine learning can fail. If you don't have enough data to learn on for many of these approaches then you can't solve the problem. So that's a very important thing to know as you go into applying these tools. Overfitting, this is a chronic and classic problem in machine learning that you overfit. And this is again something that you want to work with a domain expert, excuse me, a machine learning expert to help you with. So that's the bottom line there. And so in terms of going to the future, these are things that we're involved in. There are workshops for the NeuroLogical, the NeurIPS meeting as it's called. This is the biggest meeting in machine learning annually. Next year it's in Vancouver. It's very hard to get into this meeting. Last year registration opened up and 11 minutes later it was closed. 9,000 people attended and registration opened up in the middle of the night and of course in some countries. So imagine what that's like getting in. But this is an amazing meeting and everybody there is under 35. It's really amazing for somebody like me, my age. So special sessions, you know, these are happening all over the place now but we know they're going on at AGU, the Society of America, SEGIEE. We know that there's focused industry meetings as well. Today we host a meeting. We had the second one this year on machine learning and solid earth geoscience. That was in Santa Fe last March. The first meeting was the year before in Santa Fe in February and the next one will also be in March next year in Santa Fe. Please come if you're interested. And this is both basic research and application to industry. Also there are contests. This is going to really help drive this field forward. One thing that we did in collaboration with Laura Pirak-Nolte at Purdue and with funding from the Office of Science, which allowed us to post a competition. And it just closed this last week. We had one of the largest responses they've had in the history of Kaggle. I think in the end we had about 5,000 competitors playing. Right now they're deciding on the winners. And then the idea is, what do we do with that information? We start to try to collaborate with the very best teams. They seem to be interested in geoscience. They're probably not geoscientists. How do we engage them? That was the whole idea behind this. And so in conclusion, sort of how do we proceed? But before I say that, I just want to make one really important point. Machine learning is a set of tools. That's what it is. This is the new tool in our toolbox. It's going to enable us. It is enabling us to do new things. And it's going to enable us to do new things in the future. Sometimes profound. We're already seeing that. So that's the thing to keep in mind. In itself, it's for the people that are developing it. That's not what we do. We're the users. We're not developing new algorithms. So it's something that we need to impart on the young people that we're working with today, that this is something they have to learn. They have to learn this because in five years everybody will be using these tools. So they've got to do it now. And that's part of this conclusion. Do I have time to wrap this up or should I just stop, Elizabeth? Okay. Sorry, Daniel. So the idea here is kind of to, how do we move to the future here? So we, science and education, I kind of just mentioned the educational aspect. And conference and workshops, I mentioned that. Joint work with machine learning experts. That's this portion in here. So we've talked about that. The new machine learning architectures. This is something that we can contribute to, but most of us will not be doing these kinds of development. There's some people that straddle these two domains, but they're quite rare. And then, these are really important in my mind to open access, open source software, open data. This is very difficult for industry. You know, how can they share data without giving away something they don't want to give away? Even in the world of open research, getting hold of data that we're publicly funded can be sometimes very challenging. But at the same time, there are a lot of data clearing houses out there where you can download massive amounts of geophysical data easily. Iris is an example for seismic data. You can get great satellite data for gravity. You can get insider data, et cetera. Then benchmark data sets. This is related to the competition, or you just simply have a benchmark. This has taken place in many fields, including industry. Industry has kind of led this aspect of science in the fact that you post benchmark data sets. People compete to see if they can produce the known result without knowing the result in advance. So these are going to advance our field in terms of our understanding of the solid earth in general and applying this new suite of tools. Thank you very much. Thank you very much, Paul, for that excellent introduction to our day-to-day. I think you really established a framework to put us all on the same page for the ensuing discussion. We're going to, I think, segue right into the panel now. And Paul will be participating in our panel discussion at the conclusion of the session, so certainly can take questions at that time. Kicking things off, kind of, as I said, with the mining and minerals exploration perspective, is John McGahey. John? Yeah, thank you. It's a pleasure for me to be here as well. I'm not sure where we are on the hype cycle in the mining business, I would say, somewhere near the top. There's a lot of action at conferences around machine learning, a lot of sessions that are very well attended. Most of the work to my mind isn't that interesting yet, but I suppose that will come in time. We've been using machine learning in a couple of mining-specific applications for a few years, and that's what I want to share with you today, sort of how we think about that, how we put data together for that, what the challenges are, and where I think we have further opportunities in the future. The two specific applications I'll be talking about are mineral exploration, one, and the second one, geotechnical engineering. In geotechnical engineering, specifically looking at the possibility of forecasting geohazards of various types. So this is a conceptual picture of what we're trying to achieve in mineral exploration, usually now in 3D, sometimes still in 2D, but this equation as shown here is very much the f of x equation that Paul showed in his presentation. In this case, we're trying to combine different data sets that are coming from different really sub-domains within the geosciences. We're looking at geology, geochemistry, geophysics. We tend to have a low number of data sets. We might have a few tens of data sets if we're lucky. Those data sets have many variables within them. And the game here really is to combine all that data somehow into a function with which we can predict the probability of an ore deposit occurring somewhere in the subsurface. And the picture in 3D is really a picture of that, those cells or voxels are cells that have been determined to have a high probability of mineral deposit occurrence.  So this is very much the supervised learning approach that Paul talked about, the label here to use that same jargon would be the target. And the way this is approached is to look at areas where we understand where deposits occur and look at the data sets that have been gathered around those deposits and use machine learning to try and build this function so that we can apply it in new areas. We'll switch now just conceptually to the geotechnical problem. And I'll be going back and forth a little bit because one of the things I want to do here is to show that even those specific problems are quite different from each other and have different audiences within the mining industry. How we go about setting things up is actually quite similar. So the geohazard problem looks like this. We again have this f of x that we want to solve. In this case, it's a four-dimensional problem. The hazard is very much a function of time as well as space. Things are changing in a mine all the time. The data sets are changing. Many of the data sets are a function of time. Many of them, like geology, are not a function of time. But many of them are. Stresses are changing. Excavation. Geometries are changing. Micro seismic data, which is routinely collected, is changing. Lots of things are changing. And the hazards, of course, are evolving as well. So we have to be able to reduce these hazard maps, as we call them, regularly in time. For us, that means typically daily, in some cases in coal mining, maybe more like hourly. When we're modeling hazard, we model it not throughout three-dimensional space, but we model it on the rock interface, because it's the rock interface where the failure occurs. So if you have a landslide that's occurring on a slope, if you have a rock burst in a mining tunnel as pictured here, that's happening on the rock interface in the tunnel. So the challenge, I mean, the benefits surely are immense. In terms of mineral exploration, discovery rates have plummeted, thinking being that the more near-surface obvious deposits have been largely found, and now we have to search deep or under cover, which is difficult. The problem itself, though, is that we have to identify the location of an ore deposit at the core of a very complex natural system. So these systems are the things that we're trying to sort out really with machine learning. And that, I think, is where a lot of the current challenges are, because the way machine learning is typically applied to mineral exploration today, when you see the talks at conferences, et cetera, is that machine learning is really looking through the space to try to assess everywhere in that XYZ space, are you an ore deposit? Is this cell or is this point an ore deposit? When that really is probably not the most appropriate question, because we know the ore deposit is small relative to the system in which it sits, and detection of the system itself and understanding where you are in the system is actually a much more important problem. So this really gets to my sort of broadest point today, which is how these problems are set up for machine learning is the greatest determinant of success or failure, much more important than the machine learning algorithm itself. Just a note without going into details on how we now approach the problem in mineral exploration, as this is a cartoon, it's a cross-section of the earth, the ore deposit is A, and those zones or domains around it, B, C, D, E, are zones of alteration. So these zones together comprise the mineral system, which if you're lucky, will be kilometers to tens of kilometers across, and that's what we're trying to detect. We're trying to sort out where we are in that system in such a way that we can vector towards its core, and that's how we approach these now. And in fact, we use a mixture of unsupervised and supervised learning to do this. Paul talked about that distinction. In unsupervised learning, you don't have labeled data or training data, and we use unsupervised learning to bring data sets in to try to understand structures within those data sets that will determine groupings of data that we can assess as being likely representative of the alteration domains. So we use unsupervised learning, in other words, to create the labels A, B, C, D, and E that we then use for the supervised learning, and then we train the system to recognize those domains. So if you have new data, if you're in the new area, you can have a better chance of success by not asking the question of new data, is this new data correlating to an ore deposit at this location, but is this new data within a mineralized system? And if so, is it proximal, medial, or distal to the core? And in the best circumstances, you may get an indication of the targeting vector towards its center. Switching back to geo-hazard, again, the parallels are strong. We're trying to identify the location, and in this case, timing of a phenomena, the hazard, which can be of many different types. It could be a roof fall. It could be a slope failure. It could be a rock burst. These all result from very common systems with both natural and engineered components. It can be applied to the purely natural slopes as well, but for us in mining, there's a combination of the natural geology with the engineering components on top of it. We have to assemble evidence from the interpretation, and this is key, of numerous sparse, heterogeneous, time-dependent, non-colocated data. As we say, our problem is not so much big data as it is messy data. We have data that is not only messy in the way described on that slide, but it's messy in terms of a lot of its analog. It's typically a mess when it comes to us, and a lot of the work has to be done up front on the data. Again, the challenge for us is much more the problem set up in the machine learning algorithm itself. And the conceptual framework for the geo-hazard looks something like this, and I use this drawing for a reason because this is how data typically comes to us, and this is what I mean by messy data when people are showing us on the mine site when excavation geometries occurred, as often as not get something still in 2019 done with a colored pencil. And we have to do a lot of work on that data to get it into shape so that we can start to apply these techniques. This is from one of the deepest mines in Canada. You can see 7,680, that means 7,680 feet below surface. And the way we approach this, this is looking for rockburst hazard, is also indicated conceptually by those red dots because what we do is we somehow have to digitize the supporting structure on which we're trying to compute the probability of the hazard. And we typically just digitize center lines along the tunnels, typically a meter or two, spacing between those points. And what we then have to do is we have to get all of the data that we think may correlate to the hazard onto those points, and that is the toughest thing. This is the so-called feature engineering. It's the generation of the variables or features that you're going to correlate to the labeled data or the training data, which is the experience of the hazard. In this case, we have this typical grab bag of different data types. Forgive the acronym there. Our QD is rock qualities, which fracture density more or less. But we have many, many different data types, typically a few tens of data types. I want to make the point again that our biggest problem in my view is how we get all of this data together onto these data supports that we want to correlate to the thing we're looking for, whether that's a mineral deposit or a geohazard or something else. And it's just a huge outstanding problem. It's a problem that existed before the advent of machine learning and the problem just has a brighter light on it with the advent of machine learning because we want our data to be good and we're correlating all of these different variables at all of these different points of the target. But how well do we understand what those variable values are at those points? And we are always moving from sparse heterogeneous data into these models. So we may go from a magnetic airborne magnetic data set as shown in upper left to a 3D grid of magnetic susceptibility as shown in the upper right through an inversion function which typically incorporates smoothness constraints as a mathematical convenience and on the geological side we'll go from map data or drill hole data typically now through implicit modeling routines into creating a model of the subsurface which is the exact opposite of that. It's looking at a geological model of discontinuities and formational contacts and structures the assumption being that properties in the earth are not smoothly distributed or discontinuous which is actually probably more correct but in the geophysics we're still working with smooth models. So these sort of inconsistent assumptions in the modeling are actually the biggest bugbear I think in the successful application of things like machine learning because we're trying to correlate things that we don't control well as an input. Getting into the machine learning mechanics I won't do other than to show this data fusion table as we call it and others call it as well and this is the representation that we have to get to for all of these problems whether it's mineral exploration or to hazard or anything else this is a flat ASCII file typically a big CSV file in which we have rows of variables and then we have that labeled data on the right which could be a hazard or it could be an alteration domain and a system on and those observations those rows could be every point of a 3D grid or there could be every digitized point along the mine tunnels but in the end we typically would have hundreds of thousands to millions or even tens of millions of rows and a few tens of columns and then with the machine learning algorithm whether it's neural networks or logistic regression or whatever the algorithm is that we apply we're trying to understand the patterns amongst the variables in the rows with the occurrence of the what I just put here is the X or an O the X being the thing we're looking for the mineral deposit perhaps or the O hazard and the O is where it's not occurring I do want to make the point here though that for our domain at least in these two application problems we definitely want other algorithms so Paul talked about the opaqueness of deep neural networks versus the transparency of some other algorithms we want the transparency and that's because when we show the final results the mine engineers or the geologists want to understand why we're saying some spot in the earth is more probably deserving of a drill hole target or enhanced support in the mine than another spot they really want to understand what went into that so in machine learning in general we'll take a table like this and then we'll put it into data space where we'll look at where those X's and O's line up with respect to the variables and if I just show it for two variables so two columns of that file I would get the X's and O's plotting somewhere and then what I think any machine learning algorithm is trying to do in principle and they all go about this different ways is they're trying to find areas of the X's in that data space so of course in practice the number of dimensions in the data space equals the number of features or variables so it can be a very tough problem but at the end of the day this is what they're trying to do and some of them work exactly as shown in the slide, they'll do a brute force search through the data space and look for so-called rules which are bounded intervals of variables where that described where you have more training data and then we turn that into a result which is typically a coded heat map in this case this is mineral exploration this is a regional scale this is actually from Australia the long axis is 80 kilometers so it's truly regional and warm colors are more likely to be sitting at the core of a mineralized system in that interpretation the training data are the black dots so you see that the training data are not all that numerous geohazard we also give the people on the mine a heat map this is looking at a deep underground mine, you see the tunnels and the stoves from which the ore is extracted and the red areas are those that are more hazardous and actually you can see here with the red areas being sort of semi-linear these are actually following faults that are cutting through the area so solution requirements I think at least for the domains we're looking at are subdomains in mining although the particular mechanics or methods of AI or machine learning are not unimportant I think the focus needs to be elsewhere the focus needs to be on how the geoscience problem is set up for AI and that is where the deep domain knowledge and a domain specific supporting framework is required for tackling these problems that's something that we've been working on over the last four or five years building a system this is really driven by the needs on the mines if we're doing geohazard we need to be doing updates to the hazard estimations say every 24 hours and that means we need to be actively taking in data from all of these different sources convergence stations and micro seismic data stress data when it's available there's a different kind of data into one data warehouse where that data fusion table can be constructed people can get at it they can see hazard updates they can get reports emailed to them it has to be a system that works autonomously people on the mine aren't going to sit there with complex software and add a desktop and run it and this is sort of a little bit of what that looks like in a flow diagram where we have data coming in typically from file folders or other databases on the mine connected live to this data warehouse where that data fusion table is built spatial computations if they're required can be done on the servers so in other words proximity to a fault or micro seismic event density those sorts of things are computed autonomously as new data comes in the machine learning is not done on the system though the machine learning is still done by experts offline but the so called rules or relationships are encoded back in so they can the people on the mine can get updates so in conclusion that f of x equation that Paul talked about earlier can be can be constructed and it can be solved and we're doing that in those two two different domains it requires integrated multidisciplinary 3D and 4D modeling that's probably the hardest part of it it requires compilation of that data and the model components into a data fusion table connecting to the statistical machine learning applications and then finally if you're going to do it in anything like real time you need an integrated data management model update and analysis application to handle the flow of data and reporting that's it, thank you very much thanks very much for that John we'll move along now to Samayne bringing us kind of the EMP oil and gas perspective I'll just start by thanking the organizers for having invited me to speak on this topic it's quite an honor to do it before this audience so my talk would be somewhat semi-technical I've read carefully the motivational text for this meeting and I figured actually it's better to have a helicopter view with helicopter view of certain aspects so I'll talk about the context within the industry right now and so far as the problematic that we're looking at I'll touch upon data and I'll spend some time actually on data because there is no machine learning successful machine learning without the 80% that the guys will spend doing the data curation and I'll speak of the environment if you'd like and I'll highlight that that Slumberger is doing something tangible about it because what I could read between the lines and the again motivational text is that it's almost there's a question of what is the oil and gas industry doing about it what it's contributing so I'd like just to say that as far as Slumberger is concerned we are bringing in some tangible and viable solutions that we hope would extend beyond the industry users I'll do, I will do some machine learning and presenting some examples that kind of give us a promising and encouraging results on the adoption but also highlight the pitfalls, the risk and the challenges and I'll conclude with some remarks so in so far as the the context if you'd like I think there is literally a digital transformation going on in the oil industry right now in the oil and gas industry and one can think of two fronts if you'd like it the leveraging of convergence and the advanced progress in a number of enabling technologies around sensors computing power, a high performance computing to the cloud of the edge analytics, robots and also communication or connectivity leveraging of that towards two fronts if you'd like that somewhat competing for resources and funding one of them is concerns us which is how subsurface data can be leveraged within a particular let's see environment along with machine learning to so it can inform decision making to what's happening on the surface in particular but also to environmental monitoring and exploring to accelerate or accelerate the discovery in the exploration process the other front is leveraging of these technologies actually to bring about gains in the operational efficiencies and which can translate into gains for the bottom line this is quite important for the oil and gas industry given the context, the business context that it has been in for the past few years and the reason I'm highlighting this is simply because it is competing in terms of resources and funding right so I heard from Jim that the question of types of data subsurface has been discussed in the past so I'm going to go rather fast on it but I'll share with you the type of data that we deal with very briefly I mean in terms of images and other types and in terms of images we're dealing with data coming from multiple scales quite a spectrum there running from the nanometer scale where we do microscopy on thin sections all the way actually to the right hand side here the extreme where we look at low frequency electromagnetic probing like control source or seismic in fact that gives quite a total resolution and large extent of what the basin of the reservoirs are and if I were to focus on some of the key technologies that the teams that I work with are focusing on and those are images and data obtained through lowering devices in boreholes as well as shining ultrasound or EM waves at the surface the picture is shown here in fact that is a whole panoply of physical measurements that are used listing NMR electric density and all the way to measurements that as you lower the frequency tend to see deeper into the formation at tens of meters perhaps and then at hundreds of kilometers as well and this is extracting physical parameters that speak to the static but also the dynamic properties of rock formations okay data comes in terms of time series like production and flow liquid flow logs also as depth images sorry depth logs in terms of the physical parameters of physical properties of the rock expressed as a function of depth geological crops are often used and we go on field trips and actually we get to see analogs through the art crops that we keep in our minds and we use actually in the interpretation of what we see through physical measurements lowered in the borehole at least but not last the text actually that is in reports and interpretation reports this is extremely useful I'll come back to it so this is just to say actually that the data that we deal with in the oil and gas industry is encompasses multiple physics that needs to be integrated and those are multiple scales and there is a diversity in the character also right I'm going to go fast again on this data because I'm sure many of you if you have said that this meetings here in this building you must have discussed perhaps to a great detail but just to say that the type of data that we deal with the subsurface data one couple of things to think of is that it's extremely heterogeneous but it's rich in properties and it's not big data it's somewhat sparse sparse and it's low volume but also sparse if you'd like in the physical properties that it bring one can think of seismic as being big data it's not and it's full just actually reminded us of what the what we as humanity generate and the internet is a lot more feel like many orders of magnitude bigger than let's see the largest marine seismic acquisition job that could go on about a hundred of terabytes or so and when it gets to the seismic let's not forget actually that it gets reduced to sometimes a couple of gigabytes of use for that through an image of the subsurface and I'm showing here or perhaps tens of gigabytes the other components that I'd like to also highlight is well logs so in terms of volumes it's minimal miniscule few gigabytes per well we do have out there millions of logs actually but in few areas like the US and the North Sea but if you go outside of these regions it's really is a great deal of sparsity owing to the fact that you have wells that are quite sparse or sampling in sparse manner a field and for most of these wells the logs that are available tend to really give us some basic physical properties okay I want to highlight now the elephant in the room which is everybody talks about when it gets to data on machine learning which is that it takes a long time to create the data to the ETL extract, transform and load and that we need to keep it in mind all the time so here it's for the seismic it takes literally sometimes a year to go from the raw data all the way to a cube let's see that describes parts of the Gulf Mexico for well logs you would ask a petrophysicist for instance and they would tell you that at least 80% of their time is spent just preparing the data before they start the integration process and also some interpretation around it using whatever tools they have and that has to do with a variety of of factors that I'm sure you have you have a discuss here when you discuss with the data that is the poor quality of some of these the diversity of quality is used sometimes to the calibration of sensors that are used there are problems with calibration also often missing data in these logs an example for instance if you have a well with some washouts then the sonic slowness doesn't really doesn't get established and you have a missing velocity in that particular interval for instance depth matching is another thing we acquire physical property data by lowering these devices but we often do them through multiple passes in the same well and so we're using a cable let's say while drilling and that leads actually to mismatch in the various curves that you need to put on right depth that's something quite outstanding to get right just want to go back to what I highlighted one input of the data that you deal with this text and it's extremely valuable in the sense that it captures the context of whatever it's interpreting whatever is describing in terms of quantities and that would like to mine it if you'd like to extract it because to some extent that's the label that we're after a label that you can use in doing supervised learning there are plenty of inconsistencies and I think John just showed actually one of them if you like how people annotate and how they how do you digitize that and capture it so it presents really significant challenges so I set a slide here just to oppose and just again remind everybody and I'm sure many of you are aware that this is in the words actually of Michael Stonebreaker just two days ago actually I attended the annual meeting of the corporate alliance program that they have at MIT in the computer science and artificial intelligence lab Michael is or professor Stonebreaker is a touring awardee who works in the in the relational database research so two days ago he said actually the data curation problem is really the 800 pound gorilla and speaking to representatives who are about 80 corporates that are part of that alliance he said you're really out to put your best people on it so I am part actually of team scientists who have been tackling data and using machine learning over the past few years right to what Paul mentioned to complement our arsenal of deterministic approaches that for that case would use to interpret subsurface data and try to build solutions that can be used by people in the operations if you'd like to support the decision taken and accessing the data at a scalable fashion and asking the question whether it's structure enough actually to start using machine and AI and it is a big, big challenge at the end of the day what we kept asking for is what is highlighted here in blue is like we'd like actually to work in an environment and the line if you like and the line by software architecture and high performance system to really orchestrate the access to the data the access to algorithms that interact with the data but also operate on it visualization is part of them but through a user friendly experience that still caters to the requirement of the business and the regulators and here what I mean is there are plenty of aspects that we need to keep in mind serious one security, accessibility and environment that allows for collaboration between various stakeholders as well as scalability that's what we end up actually wishing for and it's something that really propagated within slumberj for the past several years and the company transformed itself to try to provide a solution of the sort working with the best vision for technology partners out there and we ended up actually going again to follow all to some place that we deserted in the 80s when we used to have an artificial intelligence lab so we went back about five years ago and created a software technology center there we ended up also transforming and looking really at software technology from a different point of view the outcome is an environment in what we call the data ecosystem that kind of speak to these challenges that I just highlighted and it's highlighted in this slide here what I want to highlight in terms of attributes particularly that it's an environment that allows users to collaborate and have access to tools they can bring their own tools they can bring their data the environment is open and extensible and perhaps I can expand on that at the panel discussion I'd like to highlight two examples actually that we are as a user my teams are quite excited about this we're two examples that highlight what can be achieved in an environment like this first one has just actually been rolled out three days ago at the EAGE conference that happened in Europe and it's focusing on allowing operators that are exploring to significantly accelerate the discovery of hydrocarbons through screening and ranking of exploration opportunities by accessing seismic data and data of the sort at the basin level as well as well-bored data that is available from Slonderger but also from additional providers that are providers and finding the tools to cure the data to process the seismic as well as bring in their own AI tools to operate on it within an environment that allows for productivity and for efficiency so one example actually that going to the cloud and using this Delphi environment has helped us is we've managed to take a typical processing job substantial processing jobs that in the past would take us about 12 months reduce it to two months actually through the use of high performance computing in the cloud but also a number of machine learning that tend to remove the remove the manual tasks excuse me. On the well-bored interpretation I have a movie here actually that described that things also in place and this is really more of a demo for let's see a task where a user would like to highlight go to a particular area of interest and highlight particular well with the interest of finding if you like tops of the formations and do a correlation across multiple wells so this is an environment that allows this as I said as a user my teams are quite excited because we don't want to do this within our own on our desktop or even accessing the cloud but having the data on our own premise so these examples so I want to now move on to machine learning and share with you a bit some of the again a helicopter view some of the lessons learned and I think Paul touched upon it John as well so I'm not going to repeat what they said but I'll highlight actually what we see as if you like scientists in the service company embracing machine learning and complimenting our deterministic approach where we see these machine learning could bring in terms of if you'd like of value to us so I would say that it brings through that initially automation if you'd like of algorithmic solutions it brings efficiencies not just actually in the interpretation algorithm but also in the curation itself which is again the 800 pound gorilla in the room we actually seeing also the machine number of techniques to help us integrate across physics and across scales and do more effectively the interpretation reducing the non uniqueness and the uncertainty if you'd like that we would deal with not integrating the data I think we gain insight so the data and Paul highlighted this quite well for problems that are difficult to approach with deterministic approaches gain insight in the data that as a domain expert for instance you could lead to a better approach to resolving the problem we're also looking at augmenting the capabilities of our experts and non experts if you'd like through approaches such as causal graphical models or causal networks to bring in some cognitive if you'd like somewhat mimicking the reasoning let's see workflow that a number of experts will have and this is not to say that we aspire to remove the expert out of the loop I don't think we're far, far away from that but rather help them actually in conducting more interpretation jobs so they can focus on the tough questions opposed to the laborious and perhaps simple questions natural language processing is also something that we are using and definitely encountering a variety of challenges but we making some headways if you'd like an extraction of value out of the reports that I said are quite challenging and in particular to bring what we do often is generate modeling if you'd like of the subsurface and we'd like to label that teach an algorithm through what they have seen in other interpretation reports for instance and through analogs to the data that we're looking at whether geology or let's see a log put a label of geological or bring a geological context to that inferred model so I also want to highlight actually the way we organize ourselves perhaps that hasn't been addressed by other speakers the way we approach now the use of machine learning and the leveraging of data is really through we try to discipline ourselves first to find that there is indeed a business outcome to it a business question to address and not do machine learning for the sake of machine learning one thing that we've also have been increasingly doing over the past few years is really see partnerships with clients that bring the expression of that use case business use case but also data and domain if you'd like context of that data sometimes the data is already curated sometimes we work together to do it and the teams that we have internally tend to be just like Paul highlighted tend to be made out of domain experts and data scientists that's a recipe that seem to have been working so far challenges and risks I think there is a long list there I will just highlight to bias and interpretability what I mean by bias is that it's almost on a daily basis that as we attend conference or as we look at our own projects where we see actually what we are doing with machine learning often supervised learning is interpolating within the dimensional space that the data represents and that's where the bias is meaning you have a model a learning model that is biased to what you ingested the data and cannot be extrapolate the interpretability is something that is becoming quite important that is in our industry and I think ourselves as well as physicists and engineers we're not ready to accept an inference or prediction provided by a black box we'd like to understand how it came to that and that's very true also for the clients or our customers especially when the decision is critical they'd like to be at ease understanding how that algorithm has come about to that prediction the way we are addressing it really is through great deal of injection of our domain knowledge and physics and if I had time I would go actually through a variety of ways how do we do that I'll just highlight with one which is transfer learning where we simply train on synthetic data coming from a particular measurement and we have simulation of those measurements within the formation rock formation context we generate data we train a neural network for instance and then we do transfer learning on real data and there are a variety of how to do that that we have done I just also want to highlight that there are also challenges in the deployment of machine learning products or services that we are just scratching if you'd like the surface of that's the challenge and the risk and you have a model you start inferring if you'd like or giving you results and you put it in a production line well if it's being used by experts the expert is also just seeing and correcting you'd like to take advantage of that correction put it back retrain the model how do you do it if you'd like that's the way without all the sudden figure now that your system is not capable of that the other thing to keep in mind is robustness these are the in particular adversarial effect two examples very quickly of successful machine learning in this case is supervised machine learning that on a cube of seismic data the problem here is really to pick the boundaries of salt objects like salt body objects what this has helped us is reduce the time that it takes to do the salt takes from 80 days to literally few hours after having trained doing after having done supervised machine learning with people that have picked on a particular slices of this cube and then do supervised learning on the rest so this is one successful like project that we've had it does take few hours to do that but still about two weeks to QC and sometimes adjust things right the second one that I'd like to highlight is generative modeling for really spitting out the 3D stratigraphy there we use generative adversarial networks that have seen actually quite a successful I'm showing here is what paper by NVIDIA in particular that generates beautifully looking people that are not really that don't exist in the world and simply by teaching by feeding this again a large number of beautifully looking but real people if it's like so we figured if instead of teaching the GAN how to spit out beautiful faces how about let's say stratigraphy from cubes and that's what we ended up doing actually so this is an example of spitting out turbidite channel levy systems in various realizations one thing that we've did actually is we wanted to spit out realizations that also honor the data that we have and in this case it's in this case let's see we have 10 wells and the wells tell you where the sand is and where the shell is and that's kind of discrimination between a channel and levy so you end up spitting out a number of realizations that still honor the sparse data that you may have okay let me conclude with remarks here so I think the first one is just the feel like the content the industry is in it's really focusing on bringing performance efficiency through these adoption of enabling digital technologies right and not only is it looking at the leveraging the subsurface data but also at what happens in the surface and try to bring efficiencies to that on the subsurface data itself I think it's significant challenges lied towards making it readily available if you'd like for ML at a large scale and I'm not talking about project by project where people spend and focus on it but something that can be done in production mode it does require as I said comprehensive approach more of an ecosystem framework that allows you to do a number of things and still meet the requirements that are surrounding you and what I highlighted here I hope you allow me you have a lot actually to say that Trumdege has done something that we hope is tangible and viable and that could be a particular solution for the whole industry machine learning opportunities and challenges abound there are early successes that really encourage us to continue in this in this direction if you'd like where domain and physics are key enablers to where these successes I would say though that our experience so far has led us to believe that what we call AI or deep learning if you'd like is still not mature to tackle some of the complex problems that we deal with complexity here has to do with on one hand the complexity and the environmental variables and in the data that we deal in what is expressed in and second also the complexity and the interpretation and the reasoning that we go about reducing that to some answers that's what I mean by lack of maturity as I highlighted there are partnerships across the industry to accelerate data projects to learn how to do these things inefficiently lastly I would just highlight actually and I think Paul mentioned he went to a conference where the majority were below 35 years old so what I've seen actually now is that in particular Trumdege there is a great deal of enthusiasm that is injected in our industry and also in my company in particular which is attracting young generation of engineers and scientists that are quite at ease with the digital and eager to make an impact on a century old industries and that's really refreshing to learn all about this digital enabling company and sorry technologies and work with folks actually that teach us something with that thank you thank you very much Samane and last but not least we'll have Shauna Morrison with the Carnegie Science Geophysical Laboratory share her perspectives great it was it was really encouraging to see how everyone seems to be converging on some of the same problems and opportunities I don't you know we didn't really coordinate that so it's really encouraging to see that that's just naturally happening so thank you all for inviting me here I'm really excited today to have the opportunity to share with you some of my work and some of the work of my colleagues as well as some of my ideas about the needs and opportunities in data-driven discovery system much of the work you're going to see here today is part of the tech sponsored deep time data infrastructure project we were tasked with a goal of better understanding the co-evolution of the geosphere and biosphere from a data-driven perspective so we were going for a more holistic approach to answering questions that require multiple domains to get at these things so this ended about a year ago and the next step is the 4D initiative so this is a deep time data-driven discovery we're specifically thinking about the evolution of planetary systems but essentially what we're doing is we brought together a large network of over 200 different scientists across earth space life and data science I've been mentioned many times that it's very important that we have domain scientists working with data scientists and I could not agree more with that so we're looking to do that but also to bring in different fields of science so we can answer these bigger questions that if we stay stove piped in our own realms we will not be able to answer so I'd like to just take a moment and explain to you why I'm really interested in machine learning and in data science I'm a mineralogist and a crystallographer I spent a lot of my PhD in the lab so doing very very technical things nothing to do with data science but what I've seen over the past 10 years is my field of mineralogy go from a very descriptive field into a much more predictive field so usually in mineralogy we find minerals in the ground we make them in our lab and we characterize them and we report them however over the past decade or so we started to be able to do things like predict the number of missing mineral species I'm going to show you in a bit we're working on algorithms that can predict the location of mineral species that we didn't know were there before which I think is particularly of interest to this group today and we're also able to start thinking about ideas like is the diversity and distribution of minerals that we see on Earth's surface a planetary scale biosignature not going to get into that today but if anyone's interested I'd be happy to talk about that and of course we are relating mineralogy to a lot of other things and of course that's tectonics and supercontinents mineral resources and we're even parlaying some of these techniques that we're developing into paleo biology applications so I just want to run through some of the data resources quickly I'm going to give you a couple of sort of case studies little vignettes of projects that we've been working on and then I'm going to talk about some of the needs and opportunities that we're facing in data driven discovery so the data resources we have the rough project so this is a huge mineral library and database housed at the University of Arizona it contains chemical, spectral and diffraction data there are about 5,500 mineral species known today this project has characterized about 3,500 of them across about 10,000 samples so pretty big data resource also as a part of the rough project we have the mineral evolution database and this we were talking about data curation and I'm going to talk about that again in a minute this takes a really long time so what you're seeing here is a countless hours of work this of course was started by Bob Hazen at Carnegie Geophysical Lab and Josh Golden at the University of Arizona is a graduate student who's been helping and really leading the initiative of going to the primary literature, reading the paper and extracting mineral location age information and the broader geologic context and populating the databases all been done by hand he's been working on this for I think we're approaching 8 years now so this is when you talk about data curation being one of the main issues I really agree with that so currently we have nearly a million mineral locality pairs and over 160,000 of those have age data so it's actually a pretty huge database we work closely with Jolene Ralph at mendat.org I assume many of you are familiar with it this is a mineral locality database which is a big wealth of information about 95% of it is from the primary literature and about 5% of it is crowdsourced so I really like this as a flexibility to be able to add it but about 95% comes from the literature so we have in this he has over a million mineral locality pairs we also heavily rely on Kirsten Leonard she has a number of databases through her work with IEDA and this is Earth Chem this is a huge wealth of geochemical whole rock information she's also I work in planetary applications and so she's also developed MoonDB and we're now working with NASA to develop the astro-materials database where we can actually start bringing together all of the kind of planetary materials Paleobiodb is a huge database on paleontological work a lot of it has been done by Shannon Peters who has a number of other interesting applications including Geodeepdive where he's using machine reading of PDF to try to deal with some of these problems that Samayn was just mentioning so as I said I'm a mineralogist and a crystallographer so I like to think about problems from a mineralogical perspective so I want to give you some context for the stories that I'm about to tell you the case studies the mineralogical frameworks that I like to approach problems with are things like mineral evolution and mineral ecology these are fields that have been pioneered by Bob Hazen at Carnegie and mineral evolution is simply focusing on the changes in Earth's mineralogy through deep time and these come about through the changes in the combination of chemical and physical and sometimes biological processes that are different at every stage of planetary evolution so this can give us kind of a temporal framework with which to think about things we can go to the mineral evolution database and we can actually look at how these minerals change through time so here on the y-axis we have age on the x-axis we have the number of copper mineral locality pairs this is the number of occurrences that these copper minerals have they are colored according to their oxidation state so the reduced copper 1 plus is in blue and the more oxidized copper 2 plus mineral phases are in green and there are a couple things that you can notice here first let me just zoom in after 2.5 billion years ago which was the great oxidation event when we started to have the rise of atmospheric oxygen in our atmosphere we start to see a shift in the ratio of the more oxidized green copper phases to the more reduced blue copper phases we're always going to have reducing conditions so we're always going to be creating reduced copper phases but we start to see that there is an increase in the ratio of oxidized material after the great oxidation event so this can be thought of as a proxy for looking at the oxidation of our atmosphere among other things bioavailability of certain elements to the environment also a couple other things to notice here the first of which is that we see pulses of mineralization associated with supercontinent assemblies and so you'll see there's a little pulse with each of these you might notice that Rodinia is a little bit shorter it's not quite on trend with the others and that's not only true in copper that's true in most of the mineral species that you look at and my colleague Chail Yu and Simone Ronyan have been working on trying to characterize exactly why that is it's also true in the geochemical data so that's where these big data resources can become really powerful for recognizing issues and then for digging in and trying to figure out what those are we had no idea when we looked at this that this was going to be the case if anyone's interested I actually have all these skyline diagrams for all the elements so now let's talk a bit about mineral ecology mineral ecology takes more of a spatial stance so here we're looking at the diversity and distribution of minerals on on Earth's surface today it follows an L and Re a large number of rare events distribution in which most minerals are rare occurring at only one or two but definitely fewer than five localities on Earth's surface and there are a few that are very common things like quartz and stars what we can do with this trend is we can actually model it so we can model the frequency distribution and we can generate an accumulation curve so that's what you're seeing here on the left and with the accumulation curve we can predict the number of missing mineral species and I'm kind of glossing over this quickly but if you're more interested in the math we can go into that later but in 2016 we predicted that we're 145 missing carbon minerals and this sparks the carbon mineral challenge that's been led by Dan Hummer at Southern Illinois University and and as of last week 30 new carbon minerals have been found a number of which were predicted in this paper so we're really starting to see we can actually make some predictions here which is pretty exciting so I'm really interested in mineralogical frameworks because as I said they provide a spatial and temporal context for asking questions in Earth and Planetary Science and so now I will talk about and these are questions like what do the Earth's earliest environments look like how can we de-convolve and characterize complex geo biosignatures are there biosignatures in the diversity and distribution of minerals I mentioned that earlier and can we predict the location of minerals deposits in environments on Earth's surface so now here's where we really started partnering with data scientists so we worked with Peter Fox's group and Marshall Maugh's group at a Rensblair Polytechnic in New York some of these spaces are familiar to some of you in this room and so we partnered with them to try to start better understanding our data so the first sort of vignette I'd like to give you is our foray into network analysis so just to orient you our networks are mineral networks each node here represents a mineral species each link between them represents a co-occurrence it means they occur at the same locality on Earth's surface and these come together to form a network of mineral co-occurrence and this could be on an element or a deposit type or a planet things like that so the first example I'd like to show you is copper minerals we're going to stick in the vein of copper for a bit and each one of these nodes represents a distinct copper mineral their color according to their composition specifically the presence or absence of sulfur and oxygen and their size according to their frequency of occurrence so you can kind of think of that as abundance although it's not exactly abundance they are linked if they occur together and the strengths of that link the length of it is inversely proportional to their frequency of co-occurrence so if two things are close together that means they occur together more often if two things are farther apart then they occur together less often and so you'll probably notice a couple of things that pop out at you right away and that is that we see without coding anything to do with chemistry in this network the only thing we've coded into this network is co-occurrence we see that there's strong chemical segregation here so we see we have sulfides tend to stick with sulfides oxide carbonates tend to stick together and sulfates tend to stick together as well and what this means is that we actually have a chemical trend line going through the topology of this network and that was somewhat unexpected for us when we started delving into this I think it's the oxygen fugacity you can see sulfur fugacity and it turns out that we can find a lot of these trend lines throughout different types of networks which is really exciting for us so here we're looking at carbon mineral bipartite network so this one's slightly different we have two different types of nodes the colored nodes represent carbon minerals they're colored according to their age of first occurrence and their size according to frequency of occurrence similar to abundance and the black nodes are their localities so here we're seeing a snapshot of the relationship between mineral, carbon minerals and the localities that they format on Earth there are a lot of things to say here and this may be an image of a planetary scale biosignature ask me about that later but what I'd really like to point out is that we actually see there's an embedded timeline here so red is the oldest age moving into blue being the youngest and you see if you start in this sort of vase here at the bottom this vase we have the most common minerals here in the center happen to be the oldest and we move up into the orange going younger into the yellow and out into the blue radiating out so we have essentially this embedded timeline mineral networks aren't the only place so we found embedded timelines we've also found that in paleobiology networks so here we're looking at fauna through time so they're colored we have Cambrian fauna in blue, Paleozoic in red and modern fauna in black and as you would expect with a biologically evolving system especially we have a timeline from ancient into modern but what we noticed here is that we have these pinch points and each of these pinch points are actually associated with a mass extinction event so it's really interesting we could see this in the network topology it's you want to start to see the things you already know in your visualizations and in your algorithms before you can really trust what you're getting out of it that you didn't know is valid right so we were really excited to see this and so then we went to a different network this is my colleague Drew Massenzi's work and we went to the trilobite network and we see an embedded timeline here going from the oldest red into the into the youngest blue but what we found is that we have this pinch point here and this did not correspond to a previously known mass extinction event so my colleague was really puzzled and he went to the literature and really dug in and found that a number of years ago someone had hypothesized that perhaps there was a master funnel turnover but really didn't dig much into that and so he was able to dig into that and indeed determined that there was a master funnel turnover event at this time so it was really encouraging to see that we can actually discover things using these networks we are exploring different ways of looking at these networks so here we're using a virtual reality network this is the last AGU and you can actually explore this network with it around you it's a pretty cool experience I was a bit skeptical at first and thought I was just kind of a gimmicky thing but once I played with it I think there might actually be some uses so we're still really developing that so just a couple other projects to mention the first is a fantastic software so if we're talking about subsurface data we're really interested in understanding what are the drivers of mineralization through deep time so we've teamed up with the University of Sydney's EarthBite team they have a number of amazing pieces of software if you're not familiar with them please go to their website but they're an open source paleo tectonic reconstruction software so you can look at geodynamic models and paleo tectonic models and you can overlay your features on top of them so in this case I'm showing copper and uranium mineralization through time but this could be really any parameter that's affected by tectonics or geodynamics so if you're interested in mineralization like some of you are in this room this is a great thing if you're interested in oil and gas I'm sure there are applications here as well so this is a sort of new partnership that we hope to push forward I'd also like to mention affinity analysis I'm particularly excited about this and we're aiming to predict the location of a mineral or a deposit or a geologic environment some of the folks today have already hit on models like this affinity analysis is a recommender system that characterizes multi-dimensional co-occurrence relationships and creates probabilistic models for future or currently unknown occurrences this is exactly what Amazon does when it gives you recommendations for what you would like to purchase based on what you've already purchased it has a huge user database on which it can train its model and then it can make predictions based on your input data and so we have mineralogical co-occurrence data and that's what we're initially basing our model on but just like Amazon where you have user data if you know my demographics you can do a better job of predicting me we can also start to include geologic information in our models but right now we're just starting out looking at mineral co-occurrence and what we hope to be able to do this is mathematically a big problem but what we hope to be able to do is predict the location of you could think about an individual mineral species you could think about a mineral assemblage or a set of characteristics that typifies a deposit and this doesn't have to be there's gold in the ground this could be vegetation on the surface that happens to signify a certain kind of geochemistry geologic settings or planetary environments and we haven't gotten this quite up and running yet manpower is a problem people are trying to do phd's and stuff so trying to get it done is a bit of a struggle but we have actually implemented a pair-wise version of this recommender system on Mendat and basically you know affinity analysis you would look at all the correlations across the board in this case we just looked at pair-wise correlations and tried to make a prediction of where we could find an unknown mineral species and when we did this we were able to predict that at Cooks Peak District in New Mexico that there should be a new location of Wolfenight and turns out there was so a collector actually went out there for us and found this and confirmed so even with a very pared down not even you know just basic statistical method we were able to make a prediction so I think once we scale up to the full version we'll be able to do a lot I'm really excited about that so now I'd like to just briefly talk about some of the hurdles in data-driven discovery the first is creating interoperable you know fair data resources this is a huge huge problem a lot of data is dark this is definitely true in geology also true in planetary science there's also a general issue of lack of funding this is not true in industry right you can fund what you think is important in academia it's very true you know we have earth cube which generated a big framework for housing data but it's very hard to get funding to actually generate these data resources and there's also just a general lack of incentive to make your data open you know we don't get tenure or funding or really anything based on making our data open and available but it can take a significant amount of effort so I think we need to start thinking about the ways in which we can incentivize that you know people think about data journals and data DOIs and things like this and in the case of industry showing them the benefits of making their data open and of sharing their data amongst each other Australia is a great example of this where their mining data is much more open so I think the problems are different in industry and academia it's really fundamentally the same issue small sparse data sets man this is really really true in any sample based science and so you know using machine learning on small sparse data sets can be really really tough developing user friendly machine learning interfaces a lot of us did not come up learning how to code the learning curve for coding I will tell you it's very steep having learned only in graduate school it is very steep and often our needs are very specific and very custom so it's hard to develop these platforms that are kind of universal but now let's talk about the opportunities which I actually think are exactly the same things that I just listed as the hurdles so we have an incredible opportunity to create these interoperable data resources and a lot of organizations are taking advantage of this deep time digital initiative mainly coming out of China with the Chinese National Science Foundation is aiming to bring to get to essentially make a Google geology I mean they really want to bring together everything and this doesn't mean going to the data manually and putting it all together this means linking existing databases this means hooking up with organizations and researchers that already have these databases and finding ways to bring them together I think this also makes an amazing opportunity for collaboration cross disciplinary cross organizational collaboration that wouldn't have happened otherwise we also have the opportunity to develop new natural language processing and machine learning machine reading applications so I mentioned Shannon Peters at the University of Wisconsin he's been working heavily on this a geo deep dive is his algorithm definitely let me know if you'd like a link to that he's been he has millions and millions of pdf that he has access to that he can actually run his natural language processing and then some parsing scripts to actually extract information so people are making some some headway here and I think this is an amazing amazing opportunity and we also there is going to be some manual curation right I mean especially at this stage NLP is not quite there yet machine reading is not quite there yet but I think that that is actually an opportunity to train students I think they learn when they read papers and developing the mineral evolution database I've seen how many how many students have essentially become geologists through that process of having to read the literature and extract that information small sparse data we have the opportunity to modify existing machine learning algorithms and to develop new statistics and new math for understanding how we can get what we want out of these small data sets and once we do that that may give us access to problems that we wouldn't have had access to otherwise because we couldn't answer them with our traditional methods but it also can limit cost you know if we're thinking about going to an asteroid or you know drilling in deep water Gulf of Mexico that's costly and so if we can answer our questions with smaller data sets and learn how to do that in a robust way then we certainly can save a lot of money for ourselves and we have the opportunity to develop more user friendly interfaces and in doing this we can actually help train that next generation to become more literate in coding which I think is really important and we're also going to be giving upcoming students and the upcoming workforce a really strong foundation in statistics and math which I think is really important so in conclusion I think that we have a great opportunity to bring together our data resources and to leverage all of the work that's been done by data scientists and to partner with data scientists to begin answering some of these bigger tougher questions that we can't answer on our own so with that I'd like to thank you all for listening and thank you for all the sponsors and my many collaborators Thanks very much Donna. Dan if we could let's make sure we provide some a little bit of time for some discussion here so we'll kind of delay lunch until 15 okay. Sounds great and we've certainly covered a lot of ground and gotten some great perspectives so let's open it up for Q&A and if you would if you have a question please use the microphone Be brave and ask the first question just to anyone who wants to take about showing our teams maybe the most appropriate few what's the national security implications of this and particularly when you look at parts of the supply chain for certain minerals related to batteries and Chinese efforts to monopolize lithium graphite cobalt what's the flow of information between the global community in China are they ahead in certain areas like those minerals Yeah so this is a big issue and this is a big topic of conversation as we move forward and are creating partnerships in other countries that have these kind of strategic interests a lot of these data resources are open and are accessible and you know you we have our scientific purposes but they can be used for other purposes so certainly when you're partnering you know when you're when you have data that isn't open and it's sensitive and is marked as sensitive you know by the federal government or you know the agency USGS what have you you have to be really careful about the access that they're given are they ahead of us China is ahead of us in certain aspects of trading data they certainly are investing in it significantly more than say the US or Europe they really you know the National Science Foundation is just behind it they're having this huge initiative they want to bring everything together and there's it really is first for scientific exploration but certainly when you bring together these large data resources there could be other implications so I think that you have to read carefully and be thoughtful moving forward yeah it's a hard question so thanks thanks to all the speakers great material a lot of overlap a lot of you know independently nice things the the role of data curation I think there's a couple of things I would be interested in the thoughts from people are one okay it's really important and that's that's good but some of the examples we hear here and other places are graduate students doing this or so I think part of it is to elevate the data curation from a task that has to get done before the data science begins to being something that someone can be expert in and very good it's not a job that solved it's part of the pipeline and so the examples here and examples I've seen in other places I see some of this some of the curation and other places that interoperability so putting like the 800 pound gorilla quote I think was great put your best people on this the idea of tidy data idea of making it easy to work with it's not going to be one database to rule them all but it's going to be an idea of how do you live with data from different places some of it's big and some of it's small some of it's good some of it's bad so I like the idea of the DOIs for data and the DOIs on literature about what you did with your data the stuff we bury in supplemental information and publication now is often critical or reproducing this so I like some more thoughts on that I you know I I'm all in it's really important but I'd like to see where you know you see this being elevated or where it should be so I'll take a first shot at that I think one of the solutions to that well we're in a stage of transition I think everybody in the room realizes that in terms of how data are how they are kept and how they present presented in publication and accessibility to both public and private sector and so they're growing pains here and that's one of the points we're discussing here but but private companies are taking this on and making money at it you know there are I could think of many examples we have a couple in Santa Fe in fact near Los Alamos companies are doing exactly that their their their whole job their whole livelihood depends on downloading certain kinds of data getting it into a format that's consistent and then selling it and so that's probably a really good solution because there's a motivation there a money-making motivation and without I think without companies like that it's not going to happen or it's not going to happen nearly as quickly so as long as there's money in it it's great but if you're a company that's one thing you might be willing to purchase the data but if you're a basic research scientist that's another issue can you afford it and if you can't afford it what do you do so that's another side of the problem that I don't think is a simple solution at present some of these companies are working with scientists and providing data and platforms for free at the moment and that's really good but I think their end game of course is to get you hooked to be a user on their platform and familiar with their data and then want to buy it so I mean that's just savvy business but I think if you're working on basic research funds that may present a challenge in the future but in short I think private industry is taking up a lot of this I could add something to that as well clearly as you said Paul we're in a time of transition but in our work with mining companies who typically store their data in Excel spreadsheets or on paper the fact of accepting a solution based on machine learning and doing the data integration properly drives them to change that because they have to take say the blasting records or the support installation records out of the filing cabinets and get it digitized and that is as people say 80 to 90% of the job in any of these exercises but once they see the value of that and get on that path then it you know these the machine learning is really driving that change but it is slow for sure Just a few things I fully agree there is no clear cut solution to this it's evolving on how best to do it what I would highlight is within Slumberj what we hope is that leverage what any particular individual does for the benefit of the rest against I mean if it's a user from within the industry against payment for it you should like and that requires doing it within a particular environment what we call an ecosystem we should like that it's not outside of what you do to access the data to collaborate with others to bring your own AI into it or your own algorithms and make sure that it's secure and so I think with the event of what we are proposing internally but also outside in terms of data ecosystem these questions could be pondered and practice and then perhaps an effective solution would be would rise up and it would be at least in the domains that we deal with domain of data that we deal with yes this is kind of an oil and gas question so we've had decades of attempts to standardize data formats for subsurface data rescue ML with ML but we're still in a situation in 2019 where most of our subsurface data is native to particular applications often imprisoned in those applications not searchable outside the applications and often in proprietary formats it's been interesting in the last couple it's been interesting in the last couple of years to see the Delphi initiative take hold at Slumberj and of course there are also non-commercial versions with some of the same goals such as OSDU what do you think different at this time and what do you think our chances of success at addressing this industry problem are? So a few elements of an answer here I haven't really spent time to absorb the extent of the capabilities of Delphi but one thing that I would say is that I think the comment that you made and the problematic that you pose have been heard internally and the comment about Delphi or Delphi being open is really addressing that speaks to that is that the hope is that openness in terms of the format that we're suggesting we're recommending to the industry will be adopted by all so there is a standardization if you'd like in the way data is approach or data creation is approach so I hope this so what I mean by openness here is really the foundational code that relates to data format and how data I feel like gets ingested is made open source and would be made visible should like to all interested parties that would like to use such an environment and bring their own data onto that system in a secure manner and so they only have access to it if you'd like and others I hope I'm providing some answers there on the openness if you'd like of such an environment I didn't hear come out too much in any of the talks it's sort of underpinning you know there's emphasis on open data, fair data and we've seen huge strides even in the last few years in that realm where the at least in the academic and the government research realm people are starting to understand the power of publishing their data and I agree with Shana where she said it's a huge effort but there's a middle ground that doesn't often get discussed and I think there's a huge opportunity with this community to maybe even find a way to help bridge that which is anonymized data that you saw in some of the graphics that were up there when you have a massive data set you're not interrogating a single point anymore you're looking at the holes to try to inform something you're specifically going after but finding a way to get folks that are concerned about their data for legitimate reasons proprietary poorly structured whatever to get them to maybe share it in an anonymized way with end users that have very specific needs there's not a great resource there yet for that bridge and I'm wondering what the committee thinks about the opportunity there or possible ways to make that bridge happen more quickly because I think that could accelerate people's comfort with data going open and getting more and more data out there in the machine learning AI outcomes that could happen as a result accelerate it so I mean on the anonymization front what do we need to do to help make that build trust you know resources to make it happen it seems like a response for industry we're not doing research actually on how best to analyze or address if you'd like the privacy issue but we're following closely what's being done in academia that's why perhaps I was looking at perhaps I have an answer right now the immediate solution is really the provision of an environment that is secure and private so only the users and that data only those who are authorized to access the data have that access and I guarantee that there is security around the confidentiality but again I know of a multitude of research efforts in academia that tries to address this on how to go through a black box that would anonymize the data and make it make it usable by a larger community without revealing any aspects or attributes that relate to privacy so we're following that very closely and to the extent that it becomes viable from a business point of view we will adopt it as well so just the comment about 800 pound gorilla in the room and it's the data quality, data integrity data accessibility and put your best people on it I'm not sure I agree because it seems to me it's not so much put your best people on it put the right people on it because why your best people are not going to have the mindset I don't think to really address those big data issues and so it seems to me it's more a matter of find the right people and empower them and give them the resources to address those issues and having said that it seems to me that kind of within academia and within government research then the question is how do you reward those people appropriately because the system at least as I am acquainted with it is not geared towards elevating those people and appropriately rewarding them I think this really comes back to what you just mentioned we need to make a space for these people and an incentive for these people there are people that enjoy doing this and there are people that this is really what they want to do on a daily and that's not me so I understand what you're talking about sometimes not everyone who's a scientist or researcher is going to be interested in curating data but we need to stop forcing people like me who don't want to curate a database curate a database and find those people that want to do it and we need to make that job for them not just push it off on graduate students and undergraduate students make that job for them, pay them well for it and really incentivize the position that comes from a higher level I think within companies that needs to be coming down from the top within academic research that needs to be coming from NSF that needs to be coming from NASA that needs to be coming from high up and giving us the funding and the space to make that happen because at this point that structure doesn't exist but I completely agree just to follow on comment I really agree with what you said Donna and funding agencies will have to allow us at least in doing basic research and applied research funds to support people to do exactly this it has to be written into grants and it has to be understood by funding agencies that this is part of what you're paying for if you've got that then you can hire people to do it you don't have to do it yourself you don't have to have graduate students etc to do it and it's just a question of convincing our funding agencies but I think some of them are in the room I think they get this so it's a question of time so that you and I no longer have to work on those problems and the other point you made about getting some sort of reward other than your paycheck in other words published DOI so that you have references to your data so when people search on Google Scholar they find your name and they see citations because of the data you curated I do agree with this I agree with you that you ought to put the right people and by right here you may need domain knowledge as well to create the data properly so the way I envisage I mean I mentioned that we're teams of scientists trying to leverage subsurface data and develop solutions that help in the decision making right now we're leaving it to a number of digital center teams around the world to do that curation but I'm pretty sure actually as we start working with this curated data at a scale we will find actually that it may not have been curated properly and we will get engaged in that and put our good people let's turn to say how about doing machine learning and AI on it because it's a sentimental problem to get right why simply because if you don't get it right then your learn model with if you'd like not be the appropriate one not be effective and if you put like it says the adage and if you put garbage in you get garbage out so at some point it has to be recognized as the gorilla I think it's not well recognized I believe people tend to rush if you'd like to doing machine learning and AI around around problems that are attractive taking the assumption that it's data is there and what structures are so and I think emphasizing this will help perhaps will help address it better just a quick question this sort of goes follows on Paul's comments here we're talking we're talking about machine learning we're talking about the subsurface but the subsurface also interacts with other systems as well and so there's atmospheric systems surface systems you know industrial systems economics and so and I guess where I'm going with that thinking about sort of the funding entities and how they're organized to me seems way too narrow to be thinking of this it doesn't simply in the context of funding that might be coming safe from you know whether senate funding or DOE funding or such is that there's perhaps some room to basically sort of elevate it a little bit start thinking about broader digital applications that really are cross cutting and not just in the subsurface which which you know in some cases for example start connecting up digital applications in the subsurface surface with digital twin applications and industrial systems which is what a lot of which the oil and gas industry is doing right now you know so I'm not sure there's a question here but to me it's just a suggestion that I think big rather than think little a question like from the perspective of an undergraduate like I know shawna mentioned like a lot of people don't even in the geosciences learn how to code until they're graduate students so like what are the types of skills that you guys think are really necessary for undergraduates in the geoscience to like be learning and like what kinds of learning opportunities should universities be making for these students like is it just learning how to code as an undergrad is it taking classes in algorithmic thinking like what do you guys think are like really necessary for us going forward yeah so I think that certainly not just learning to code right learning to think about data science problems so I think really applied classes but classes where you're actually thinking about problems and where you're given a strong underpinning and foundation in in data science thinking and statistics and in yeah and of course encoding I mean coding is part of the integral part of the tool I think that and I think when we do this what we're going to end up with is kind of what you were talking about these people who know how to do data science who know how to do data curation who understand those things are important but also know how to use a microscope and know how to look at a thin section and you know know how to think about stratigraphy and things like that so I think that we at universities we have the opportunity to kind of train you know what what we need to do this data curation problem and people if we just give these problems to data scientists who don't understand anything about our data it's hard it's hard for them to get out what what we need and so we need people that kind of bridge that gap and so I think I think yeah data science general data science courses foundations and statistics understanding what happens when you're dealing with you know big numbers and things like that and when the numbers become small to be robust things like that so yeah I just have one quick comment I really impressed with things I've seen here in the subsurface I've worked in the subsurface for many years what I find is is one of the big secrets that all all companies do this with new people is they bring them in they throw them in Utah some place in the near shore setting let them see the near shore environment philosophy all the things that's in the near shore because that's where your porosity is you get sandstones and beaches and all these kinds of things point bars and fluvial systems but then as the years go by you learn to build information when you're working in oil and gas you learn to build and understand where the oil and gas is in each given setting that's where it is in the conventional setting it's in the near shore and the point bar system and things like that now as we get more the unconventional things and I've gone around the world and looked at unconventional resources and what I see in the unconventional world it's just what you had a little start model for and that is the turbidites what operate in unconventional resources you can go to primary invasions and you can see stuff coming back to New Mexico coming off into the Delaware bases and it's turbidite you can drill a well over here and a well over here on this side and you can see similarities in exactly that moment in time I mean these are real turbidites and you can pick law characters and correlate them for miles and miles in there so if you understand turbidites you get a turbidite model for the Delaware base the whole system you're onto something so the thing here is you have to learn the basics first as a young person you've got to learn how to crawl first you've got to get this information in your head if somebody wakes you up in the middle of the night and tells you to talk about point bar deposition then you need to talk about point bars you need to be able to calculate point bar geometries and sizes and equations for that all kinds of things but the main thing is the South China Sea when I go to the South China Sea I understand turbidite deposition I know on those high off to the southwest all that sediment came off into the South China Sea so I know where to focus and this turbidite detailed models like you show those turbidite nasobosum channels and all these things that's what you want to focus on after you get a little skin on your bones get some skin on your bones get the different models and take this kind of information and stretch it just as far as you can and that's where you want to go thanks John we have one question here then we're going to take one online and then I think break for lunch so just a quick comment from about four questions ago there are archivists it is a whole job for curating large groups not librarians but archivists but it is a whole job so that's something that can be twisted into a useful direction what I'm curious is if through your program managers or your colleagues in the domain sciences or your engineers or even management if you have encountered any sort of barriers, impediments or skepticism with respect to accepting the results of the techniques that you're applying we're all here because we're enthusiastic about what can be done what sort of resistance are you facing are you overcoming this so as I mentioned in my presentation for the work we do in the domains we work in mining the geologists and the engineers need to understand how the data flowed through the process to the prediction so even if you can get better prediction statistics out of a deep neural network the technique is opaque it's not going to be acceptable at least in our industry we need transparency they want to see each of the input data what the levels they were at and they want to apply their own domain understanding so they can make a decision on how to act on that prediction yeah I think in academia in geology specifically we have a lot of pushback and resistance and I think part of it is because we're not taught this when we go through undergrad or even grad school so I think it's very unfamiliar and so people are justifiably skeptical because they don't understand these algorithms they don't understand what you're actually doing so I think there's an appropriate level of skepticism it's interesting because I work across domains you don't have that skepticism in astronomy because they understand how to use machine learning they have to by nature of their data this is also true in biology you don't have the skepticism in biology when we show them our machine learning they kind of pat us on the head and tell us that's cute so it's very domain specific and in part it's because geology with the exception of seismology geophysicists where you have a lot of data we generally have a very small amount of data and so our data hasn't necessitated us moving forward in that way so I think we there's a healthy level of skepticism that I encounter in research just that so my experience has been that was a great deal of skepticism several years ago and few years ago but that's receding now in terms of resistance it's always with us and in my opinion it's healthy to resist just to jump to conclusions and you'd like to see whether there are other ways of evaluating that, of gaining confidence and what is this so it's that's has been our experience alright and a question online Elizabeth there's a question Eric could you put up the graph it's actually to John with regard to that graph in your model the question is I was wondering if he could talk a bit more about the process to generate the model in this particular graph sure so this model is is created using a class of geological modeling techniques that are called implicit modeling and that's been a kind of revolution in the last decade or so in the geological modeling applications that are available both oil and gas and mining the difference between explicit the older way and implicit the newer way is in explicit modeling you're sort of manually stitching together usually with a mouse digitizing control lines and things like that to build these wire frames that are the fault or contact surfaces and in implicit modeling you are generating a field throughout the 3d volume in color on that horizontal slice and isosurfaces through that field are the derived contacts and fault surfaces and the derivation of that function through that 3d field from which you're extracting the isosurfaces is done from the point geological information so what you're seeing in that picture there are structural markers so strikes and dips of structural observations which can be off contact as well as on contact and the markers the well markers or drill point intersections on the contacts that's how the function is derived in 3d and then the surfaces are extracted so that's what implicit modeling is thank you alright let's have another round of applause for our keynote panelists and I will hand it back over to Elizabeth thanks again to the wonderful set of speakers this morning that was really really really good really appreciate your input and all the questions too we have another chunk of time in the afternoon to do the same with another great set of panelists in between now and then I've heard some tummy rumbles so it's a good time to break for lunch everyone is invited to join us for lunch we have a rather large group here which is fabulous we have space for everyone to eat at a table we encourage networking interactions and so forth so I would encourage you if you'd like to use other parts of the building or if you dare step outside because there are some tables also in the patio in the front of the building it is warm for those of you who aren't from the dc area it's warm out there today take the time to interact with one another do your lunch I'm making a creative decision here because the networking and conversation is really an important part of what we do and we're a large group we can't get you all through the line and so forth in a reasonable amount of time so let's make sure and to those of you online as well we're going to start at 1.15 sharply so if you're not here in the room you will miss the opening talks we'll abbreviate the introductions and really get to the speakers right away and talk with the panelists afterwards so please help yourselves to lunch interact and engage and thanks again for the great enthusiasm for our talks today and machine learning for subsurface data from somewhat different sectors in the oil and gas business etc this afternoon we would like to pick up where they left off basically the same sort of thing but we're looking at rims of research and research to application approaches which we now what we now basically what we're going to do is where are we now what's that GPS where are we what do we want to go with this what's the thing we want to do with the research and that's the same thing we've been talking about in this one so right now I had to plan to do a little bit more in the front but in the interest of time I think what we'll do is let these people tell their stories so the first speaker will be Grant Bromenthal and Kelly Rose going to work into this if I'm not mistaken Grant will discuss some of his approaches with machine learning and how they capture value from these approaches he will talk possibly about some neural network things and how they use those but that's what we've been talking about all morning so I'm just going to leave it alone and let Grant and Kelly take this alright thank you thank you John and thanks to the academies again for inviting us to come here and talk it's always a great opportunity to be able to be here and something that you don't forget so I'm going to talk a little bit about where in FE and in NTL we've been thinking about using and I put the title of this is Science and Form Machine Learning because that's a particular emphasis of what we're what we're trying to do so machine learning we think of as a data driven approach and that's a lot of the foc