at easy uh in my opinion to to really to specify which parameter which probability distribution uh is correct one so then uh the practical approach uh adopted in a psh and pth is to to to embed uh those information uh as in the logic tree so we can uh consider a range of combination of those models so uh here i considered uh three different uh time dependent distribution model uh three different coefficient variation model and in the three two different uh mountain model which i didn't discuss much today and then uh you can see that the the logic tree uh from the 18 different curves uh could be uh obtained as a red as a mean estimate and then that can be compared against the time independent model which is in purple and if the time is due say 100 years already have passed uh for the recurrence period of 100 years uh process then um the um the risk estimate uh would be much much significant so uh in that uh case we really need to do a minute yep uh clear we need to consider a right range of uh possibility as a part of this uh logic tree approach so uh this is my last uh slide so just the um uh summarize the time dependent uh hazard and risk model for script tsunami hazard uh are important especially for the long-term infrastructure risk management and then just risk finances so uh insurance industry the insurance industry those are important application and then the um i didn't talk much about the the segmentation but the other presenters have pointed out that segmentation of the fourth plane uh is an important aspect uh that is also applicable for uh the subduction aspect like tohoku and nankai so there's several uh applicable model uh in the literature um that can consider the multi-segment rupture uh in a time dependent way and then in all cases i would emphasize that the logic tree model needs to be taken into account and then the aftershock model can fit nicely uh although i didn't uh talk about this uh into this uh uh time dependent cap model uh framework and the last thing i think it might be important to point out uh might be um the time dependent model uh is really useful uh when we want to expand the horizon of the gist of risk modeling such as to consider the climate risk sea level rise yeah so in that kind of case uh integrated multi-hazard cascading compounding risk framework uh requires this uh time dependent model so uh thank you very much thank you katsu great presentation um i guess we're going to adapt the first session um uh discipline a year and we'll jump to the last talk and then we will gather again to uh put together um the qna um end of this session so our last speaker today is dr marlene nist she is a senior director at risk management solutions where she has worked for over 16 years she leads the earthquake source modeling team for rms earthquake models for the rms north america earthquake models release marlene over so the hazard component development and its validation marlene thank you so much um can you hear me um um my name is marlene nist as i was just introduced i work for risk management solutions um in the earthquake um source group um rms um has um has been working on development of catastrophe risk models for the mostly for the insurance industry but not only for the insurance industry um and uh to name a few models we just we have windstorm flood hurricane it's not just earthquake um for this talk i would like to focus a little bit on the components of our risk model um and uh just have one slide to just point that out but um how we model um the the you know what the risk framework looks like and then um i think i'll i'll just focus on um mostly on examples of um time dependent impact on a risk metric so first i would like to um outline a few um standard risk metrics that have already been discussed in previous discussion so i've been set up really well here i don't need to spend too much time on it um and then i would like to show uh the influence of time dependence um on average annual loss on ep with a few examples exceedance probability curve um in south francisco bay area and uh for istanbul turkey um and then also focus on um the comparison of the impact on risk by different time dependent models and how it really matters but time of time dependence you um you adopt and to be discussed in this presentation um i uh i would like to point out that um i will mostly talk about time dependent renewal models um i call we call a short term i think matt would call it medium term um and their implications for risk compared to the time independent long term models um we have some way of treating um aftershocks um and we definitely incorporate earthquake sequences um but um i will mostly be talking about these these time dependent renewal applied to year to year multi-year insurance contracts um where the time dependence is modeled as uh as a probability default so back is on our region um not real time we're short term aftershock activity earthquake forecast although that's definitely a challenge that we need to tackle um in the next uh the next five years or so so um this is the um the earthquake risk modeling framework as i have it the next sort of providing my mind where we have um the stochastic event set or simulated event periods that's what we are moving towards to and i'll have a little bit of of um information about that towards the end where we define a set of series set or series of earthquake events um by um looking at our source geometry their magnitude and their probability their frequency um and then um we um if we look at the um the impact on oven oven oven hazard model on an insurance portfolio we calculate the severity of shaking at each location in that portfolio due to this stochastic event set and the severity of shaking is then adjusted by taking the geotechnical component uh into account with which consists of the soil amplification landslide liquefaction um at that particular location in the portfolio and then what we call the vulnerability or the the building response component or um where we look at the the value of structures for life or infrastructure at the particular location and we we look at the mean damage due to that severity of shaking at the location and then finally in the risk quantification we we calculate the loss impact so we actually we'll move into the financial um part of the equation um so this has come up a few times before um each exceedance probability curve is an important metric that we use all the time when we look at um at at risk um it plots the probability of exceeding a particular loss level in a year so on the in the x axis we have loss and then on the y axis we have annual probability of exceedance it's not like the standard way I expressed it in a percentage but if you have zero percentage there is no um probability of exceeding that particular loss level in a year and when you have 100 obviously it's uh it's uh um high probability or it's first it's certain that you will access that um probably that level that level of loss um and so to build an EP curve for an insurance portfolio uh we calculate the loss at each location for each event and then we start building the curve by starting with the event with the highest loss moving off to the second highest loss combining the probabilities and then moving all the way up to the to the to the lowest loss and this is really uh important um parameter uh no return period losses are important parameters for um um um solvency issues and the management of portfolios and and oftentimes also tied into like certain insurance requirements uh depends a little bit on the country on the region but for instance in Canada um there is a capacity requirement of a one in 500 year year loss so if you want to do business in Canada you have to make sure that you have um that money in the bank and then every channel loss has come up a few times already I don't necessarily need to say too much about it but that's basically if you look at a portfolio you uh you take um um the loss of event one and calculated by its annual rate and then you you sum all the events within your portfolio to get the average annual loss on your portfolio um in general if you look at the exceeded probability curve you can sort of um say that the average annual loss is the the integral of the exceedance probability curve and then what we usually use it for is to uh to to you to determine the premium so insurance premiums depend very much on the average annual loss because it's in a way an identifiable risk drivers and in most regions the the average annual loss is mostly controlled by the more the moderate magnitude more frequent earthquakes that you can you know it's kind of sort of identifying this particular area of the of the bigger um this is the time um independence um contribution to average annual loss statewide for California um in the bar charts and what we see is that um that the largest contributions are indeed coming from this uh magnitude range um between 6.5 and and 8 mark so now I would like to move over to a few examples of um of of impact on a uh a l and e p um what we see is that um for instance here this is the same example of um of average annual loss in California in the front we see the time independent dark blue bar charts and in the back we see the the light blue is the time dependent um average annual loss contribution um by magnitude bin in California and for this we adopted the the the the bpt time dependent implementation by field and others in 2015 that morgan highlighted we did not adopt edas component of of that time dependent component so that is lacking um but what we see in general too is that statewide approximately depends a little bit on you know what line of business you look at or what region but in general the time dependent average annual loss uh compared to the time in that um average annual loss statewide is about 10 percent higher and what we see is that this there's a shift um in contribution to average annual loss from the 7.5 to 8 towards 6.5 to 7 and um how that works can be um sort of highlighted in this this map on the right where um we see um the in in red it this is basically um loss cost so loss cost is normalized average annual loss um for of time dependent um compared to time independent um and uh dark orange shows regions where the time dependent component is higher than the time independent component um light orange is where it's sort of similar and then like yellowish orange very light orange is where the time dependent component generates loss that is lower than time dependent and so um what you can actually see in the Bay Area is this um this shift from um from high your time dependence tied to the Hayward and Calaveras fault system to um lower time dependence tied to the 19th or the San Andreas fault system that just had a large magnitude event um where versus the Hayward and Calaveras fault system having um like high dependence like are considered to be due and and so you know premiums are in terms of premiums are set based on on this high level of granularity so it's important to take it into account um and then a completely different example is um the time dependence on average annual loss and an exceedance probability in in Turkey where um the the Northern Anatolia full zone is uh is considered to um to well it's it's it's it's it strikes a full zone in the northern part of the in Turkey um it's uh it's a kind of a classic example of um of um a series of events following each other in a short amount of time and um um this was this is a paper from um from uh the geological survey a while back but then there's been you know more updates um um since then but it sort of highlights really well um sorry highlights really well what's going on there and in general I think this has been um um identified by lots of other um um research projects as well as this gap under Istanbul where the Northern Anatolia full zone hasn't ruptured and where everywhere else it has ruptured and so um if we were to assume that that um well so and then so yeah on the left side we see the exceedance probability curve for a countrywide portfolio in Turkey but because the um the exposure at Istanbul is is really high and the risk is really high that you can basically say that even if you have a countrywide portfolio your risk is controlled by what's happening in Istanbul um and if you look at the exceedance probability curve or we have return period here and goes here then um we we take a look at the events that are in the tail these are megatrust earthquakes on the Hellenic subduction zone and uh very low probability large magnitude background sources under Istanbul with um return periods of you know in the thousands of years so very unlikely then we have a series of events that are multi-fold ruptures on the Northern Anatolia fold zone so rupturing more than and just single segments a fairly large magnitude events with also fairly low probability but then when we get into the return the hundreds of return periods we see that um Northern Anatolia fold events but within the sea of Marmara seismic gap start to pay to pay big role that that is this series of events and um in blue what we see is the time independent uh exceedance probability curve and in red we see the time dependent probability uh the exceedance probability curve um where we adopt like a very extreme form of um of time dependence with uh where we say that the event can happen within the next few years and then we see that um that first of all the time dependence average general loss can be uh about 40 percent higher for um just because Istanbul is such an important place and um in the return period between 10 and 10 and 200 so significant increases and and that turns out to be a really important factor because in order to if you were you would like to do business in Europe um you have to have a reserve requirement of of a 200 of the one in 200 year loss and so what kind of time dependent model you adopt for the Northern Anatolia fold zone is very important um and then Cascadia is a is a is another example of of where we should take into account we should take another look at what how time the different time dependent models impact risk there um we have um uh if you if you uh or for a time dependent model just for the megathrust um can be done in several ways we um if we look at the 10 000 year turbidite history that was um presented and published extensively by Goldfringer on 2012 and after that a few more studies after that that time as well we um we see that the 10 000 year just average over over a 10 000 year turbidite history that the megathrust event recurrence about 525 years uh the time since last event is 322 years that means that we are kind of a little bit over the middle of um of its recurrence and if we model time dependence with a simple average recurrence then it's not very impactful we see that in terms of return period uh loss we have maybe a few percent a l is a few percent higher if we model time dependence with a simple bbt average over 10 000 years however in the same uh studied by um Chris Goldfinger he also looked at the um at the the possibility that um we are um that's the the cascadia subduction zone model um ruptures in clusters and and so what they concluded was that um the probability that the cascadia subduction zone is currently within a temporal clusters is significantly higher than the probability of it being in a gap and so if we were to take uh and if you look at this last cluster um that um hasn't has an average recurrence of about 330 years with um um recurrence intervals varying between 180 and 500 years so that means that the average recurrence thank you that that means that the average recurrence is about is about the same as the time since uh since last event and so if we were to incorporate that extreme version of time dependence then suddenly um the uh the average annual loss goes up uh by almost a factor of 10 and the 500 year return period loss um is is about three times higher than uh than the time independent or time dependent dependence more at you know 10 000 average model and and so that is important for for Canada because as i mentioned um before there is a 500 year return period capacity requirement there um i have a quick example i hope i can um discuss that quickly this was kind of a nice um follow-up on on katsu's presentation where um we also looked at different models for time dependence in music in New Zealand especially for the Wellington and Ohio fold um and it was um um where we um took into account the uh inter event time distribution of um of events on the Wellington fold to come up with a better model than just the bpt and um oops yep what we did did there is um is basically try to see if we can come up with a uh a combination of bpt log normal and viable um that fits that inter event distribution um better than just the bpt and what we found was that especially for the Wellington and the Ohio fold for which the the data um are are you know it's not like enormous amount of data but there is a good good set of data data available and we found that um that a weighted approach matches the data better um that we find about a hundred a thousand year recurrence on the on the Wellington fold fold and 2300 on Ohio which is uh this is Ohio this is Wellington um and uh whereas um compared to the bpt those numbers are very different and so will definitely result in a very different risk profile for um for the the city of Wellington um and here's my last slide where um I'll I'll focus on the opportunities um don't necessarily need to discuss the challenges at this point you can maybe talk about that later um but um I think one of one of our opportunities and most one of the more exciting things that that we are working on these days is to come up with a better description of temporal behavior of earthquakes we really are moving towards simulated event periods rather than just static event sets what would allow for the implementation of more complex time dependent behavior like clustering and and damaging aftershocks thank you very much thank you so much um great talk we are jumping to the panel discussion for the second session and um I'd like to hear from the panel um there are some questions um answered already uh and I kind of want to revisit that we could either use some of it for the general discussions but I think there is one that is relevant to uh buildings and might be an interesting one to to cover but uh if I may I want to just start with a question that I um sort of has been wondering this large earthquake in 99 in Turkey 7.4 in August and two months later triggered quote-unquote which is a time dependent effect another 7.2 it's a huge damage and loss and by then we weren't quite talking about time dependent maybe um can you elaborate on that column stress was calculated how did it address was it enough what can we do for large earthquakes like this in places very vulnerable like Istanbul or other major cities yeah I know um definitely there there've been there there are a few Coulomb stress models around and uh you know part of that high probability on the on the on the segment of the Northern Veterinary Volt Zone that is basically just south of Istanbul um is is it's not just because it's a gap in the in the record it's also because of some of those Coulomb stress transfer models there's an interesting counter argument where there was an um a large earthquake on the segment just west of of the the the seismic gap and um that ruptured in the early early 20th century that may have ruptured much farther towards the east and that would have taken some of the stress away again so there's there's there's some debate about how long the rupture would be on the segment that hasn't ruptured yet under Istanbul but it's definitely a major concern yeah thank you um Torsten you know Trin which order here to go in terms of the questions this one is for Dr. Joshua and Dr. Wald as to the hysteresis in in the build-up structures and I found that very interesting and you know sort of thinking about uh the degree of reversible versus permanent deformation I wonder if you could clarify a bit as to to to what extent do you have to be near the so the design uh specs of the building to have that hysteresis effect right so I would have expected you need to have sort of nearly collapsed it to move into the ductile plastic part but that's probably not the case and how how far do you know and and what are the design goals that engineers go for in terms of you know in terms of this sort of hysteresis that's a that's a great question let me attempt to share some more light on that issue the examples that I provided uh are essentially looking at code codified structures they're essentially the structures which are built by engineers designed by engineers for certain threshold of code criteria and then the research community have taken those structures to evaluate what happens in a situation where you can have a significant aftershock creating an inelastic demand on the structure in essence what researchers have found that a well-designed structure if it has basically has not seen an even an yield acceleration demand on the structure then it really doesn't make a significant alteration of behavior in a post like a post-main shock situation uh 10 or 12 years ago including professor katsu here I looked at this problem carefully in looking at the main shock combination of main shock and main shock or main shock aftershocks sequences passing through a prototype structure and trying to understand to what level you're basically uh fiddling that elastic phase of the structure and trying to reach into the inelastic phase and once you go into an elastic phase how much cycles the structures are going through in order to deteriorate the strength and stiffness significantly such that it introduces a an alteration in the dynamic bearer of the structure which could be anywhere from period elongation or significance you know lack of stiffness in the structure to go back to the previous stage and that really depends upon the complexity of the second motion that you are exposing the structure to this research is basically evolving and again the the conundrum here is that usually seismologists say that aftershock a small event and you know expect smaller shaking that's generally true from seismologists perspective and the ground motion records perspective you never know where you are on the map including the best of the etas models you know the the way we forecast those points on the map for aftershock any of those can produce a significant inelastic demand on a structure specifically structures which are not uh the tile design you know and you have a large number of them in the rest of the world where you know you can you cannot expect a a cyclic behavior and structures coming back to normalcy they simply don't have that capacity to come back and those structures could be a significant significant problem in terms of the performance i hope i kind of give some yeah let me let me add one thing thurston that and that is that uh the engineered structures are typically elastic and they have this kind of behavior the world has you know buildings that collapse their brittle and they typically do so at just based on the strongest shaking level they can be precarious after they're damaged but brittle structures are the are the real culprit around the world and not the not the engineered structures just to follow up to that i saw some studies using gps in buildings for for response or behavior a long time ago i don't like i know it's now buildings especially important ones heavily equipped for monitoring but is gps still being used or relied on i could tackle that i mean there's a lot of structural health monitoring to typically expensive high-rise structures in a lot of them are in japan there's some in california and they're typically accelerometer based uh and the and the um the well monitor structures have accelerometers and enough floors that you can look at the displacement between the floors which is referred to as the inner story drift and that's the most important measurement of the damage to a structure and potential for collapse so you can do that with gps um on the on the roof but one of the tricks in tokyo or other big cities is having gps as a variety of floors to get the displacements at the very floor levels and you can do that by double integrating acceleration but it's hard to get gps in all places where you may have lots of shadows in a domestic environment or the urban environment okay great um donna yes thanks to all the speakers for your uh excellent talks um i have a very general question and that's uh it would be interested in your assessment of like what's the largest source of uncertainty in these um loss um estimations i mean we heard this morning about um advances in and challenges in um you know understanding uh time-dependent earthquake behavior but then you know adding in all the other factors that we've uh heard about this afternoon um i was just wondering where where within that space you see is the largest uncertainty and you know how do we move forward and trying to uh tackle those not it sorry i think all of the above but researchers have looked at this problem carefully in the loss modeling perspective and and they generally tend to agree that the ground motion uncertainties are the biggest contributor to the fluctuation in the losses that we calculate uh from any analysis having said that uh that problem in my mind is very well studied by seismologists thanks to them that we have a better handle on the uncertainty of the ground motion part um the same level of vigor and efforts still are not happening in the exposure and loss modeling world and many of the loss modeling uh modeling professionals are are basically working like marlene uh you know in the backside and it's definitely the research is happening in closed doors and not so much in the open uh environment and again those things probably would change because of the societal demands but again that level of uh interest definitely exists in terms of what those uncertainties mean in terms of the bottom line again the research by netfield and others highlight the issues and ask the pertinent questions uh about this this kind of modeling framework and again we need to do more before really we can answer such a question let me let me add add to that because it's um it's really vital if you if you have a shake map and you know this shaking right next to your structure you know at least the peak ground motions and that will then reduce you know the probabilistic part of the problem so having a good ground motion measurement uh is really important and you can use either the peak motion or the time series which is even more useful for doing us the structural analysis it and it really depends on whether your question is for one building or for the whole you know the loss for a given earthquake and when you start looking at the single building it's of course how well you know design of the building how well you know the response to the building and how well you get record the motion really close by for a for a whole city though it's it's much more challenging because the ground motions change but also you typically don't know the entire inventory you don't know what the buildings are made of how they're designed or or or the more challenging pace which is where people are at the time of the earthquake for that structure so those are really big limiting factors. All right thanks I'll be for indulging my very general questionnaire. There is one question I want to take before Jeff and Torsten um for Katzu uh how did you estimate losses offshore and what did it include? Um I wasn't sure what the question is offshore means. Well if if the um the chat maybe will um if you could explain the question a little more at the chat and uh in the meantime let's move to Jeff. Okay thanks um yeah I have a question that was inspired by Marlene's talk um and as you were talking about the annual average annual loss I can easily understand why that's a really important measure but then my brain's sort of wandered to well what's the worst annual loss and that's probably not well defined but how much dispersion uh how much can we actually quantify what the expected dispersion in annual losses would be um and and is that something we can actually do pretty accurately when we look at you know let's say actual annual losses uh over time or is that something that still needs a lot of work to uh to to get right? Um so we what we usually do is um we normalize average annual loss so we divide it by the um by by by the exposure so and then we can basically compare um average annual loss globally and and so we have some understanding um well we have you know especially in the high um um high exposure um countries that have also um a very lively insurance industry we have some understanding of what that average annual loss should be because you know um insurance companies tell us and and and so um countries like you know again we Japan um Italy where there's a lot of the earthquake insurance is or the insurance industry has a deep penetration as we say um the uh the the numbers and there's some countries in southern central america especially south america as well we can actually really make sure that we match we match the average annual loss and that's usually also while we are going through the development of model that is our uh basically the the sort of the metric that we use the most just make sure that we have it right um then the exceedance probability curve can look can be you know it it's it's relatively easy to um to just because of the huge uncertainties relatively easy to make sure that you're in the same the right order of magnitude for the uh an exceedance probability curve it's a little bit more complicated in that sense but we have a fairly good understanding and also it's also oftentimes it's fairly intuitive um what your you know the average annual loss the the annualized average annual the normalized average annual loss is called loss cost um what the loss cost values should should look like uh countrywide um and um yeah we we we and so we have a few a few benchmarks and then we we do a lot of um uh relative extrapolation you know of course if you know that the loss cost in this country is this much this one should be higher so uh yeah so we use that to to calibrate our models thanks okay thanks uh actually going back to katsu i think you're going to answer this question sure um i already answered in uh the chat uh but the um all the buildings are on the ground on the ground so nothing is on i mean you know offshore so but like if there is offshore like say wind turbine or like some sort of oil rigs or some something like that and if we have a fragility curve for that then we can calculate the tidal and then also the the wind and then also um the tsunami losses but then the potential of course shaking loss if there's a foundation built onto the the seabed so that that's possible but again my calculation for the calculations down for the houses yeah that makes sense thank you i'll move on to mark yeah actually i was gonna follow on that question a little bit myself um so maybe i missed this but for for hazards like you know a megathrust earthquake which can generate a tsunami that can you know impact people thousands of kilometers away if you think of the sumatra event right there were deaths well away from the epicenter i'm just wondering do these models take into account that wide range of potential losses um or are they really more focused locally so for my calculation uh all done all calculations are down for the local buildings but the um if we do the proper tsunami simulation like you know the instead of Cartesian coordinates we can do the spherical coordinates and then say yeah tohoku earthquake happened and then the wave goes across hawaii hawaii and in the california and for example like some sort of important vessels of boats uh you know harvard in the sunday ever for example then um you know some sort of current uh would you know happen in the in the port and that might damage those boats and then that kind of thing can be calculated so there have been a lot of kind of development in research and then also the application for the best so uh damage uh fragile curve so that's also an important aspect for the commercial perspective jessica hi so i have a the geologist question in this which is how much does data collection matter or how important is it in terms of mapping faults on the ground the paleo seismology record the some areas that we've seen today have these really you know amazing you know southern california records really great datasets but other areas seem like there's a big data gap um in that and how important is it um to be doing that kind of base level data collection or is that essentially within it seems to be within error in the models or kind of account for some other way of the models so i was going to but when we were talking about large sources of uncertainty that would would have been you know fairly close up for me like we don't know where the faults are not everywhere and uh you can you know be assumed that you know exactly what if what if if you know what if you assume that you where the faults are you know where the faults are then um then then you have to basically you know calculate everything else and of course there's uncertainty in all the other components if you don't know where the faults are you're missing a fault that's a huge source of uncertainty and so yeah um it's really important to know where the where the faults are and that california is is is in relatively good shape um but for instance countries like um well i mean there's lots of there's still a lot to be done on in terms of where the faults are yeah thank you well i think uh now it's time to move on to the general session i'd like to thank everybody's speakers a great session and um we can carry on and and sort of answer some of the qna um or maybe the audience or panel members jeff to you yeah thanks so our um the speakers from the first session should be joining the panel here um momentarily um and uh along with those from the second session so uh we'll have uh all of our speakers here and um what i wanted to start out with was actually uh sort of a little bit of a different kind of question we have uh people here on the panel from uh private companies from a private foundation from government agencies and uh academic institutions and i'd like to to throw out sort of the question about about career opportunities and career paths we've got uh uh more than a hundred people uh online uh here who have been uh watching uh these presentations and some of them are your early career scientists or students and so uh i'd like to to just hear particularly from those from the outside of the normal academic or at least the the academic environment or present academic environment um what what are some of the career opportunities and paths that uh young scientists interested in uh applications of of this kind of analysis uh might be able to take so maybe i'll throw this out to marlaine first and uh and then uh maybe richard and marco or or david and quicheor but i think others can can raise your hand and pipe up here yeah um so we there's a we have a large group of uh of model developers um within rms in the california and the london office um working on earthquake or um or you know any other type of natural catastrophe on the science and on the engineering side so we um we have opportunities i won't say a lot because these it's not like we have hundreds and hundreds of people we have you know like dozens of people um on on the model development side but it's it's very um so we have like i mean my team we have um people with the geology is seismology um um geophysics background and then there's this uh the the engineering side that has a has a large group of um of people with uh um masters and phd degrees usually uh from various technical universities um i think unfortunately this year we don't have an intern internship program but it varies from year to year and so there's always i always tell everyone just you know contact me at the beginning of the calendar year and then we can see where it goes unfortunately this year it's not going anywhere hopefully next year we'll we'll get that back up um and then we usually have a couple of people over the summer as interns um but there there are um uh definitely opportunities within model development where you would start as a as a a junior modeler um mostly um learning about the trade because it's it's not necessarily something that you would know before you join but then uh there it's it's i i think it's a there's the job itself is a nice combination of of um learning new things applying what you know and and and and really trying to be on the forefront of science and and and publishing so hopefully um we'll have a few opportunities this year but i usually post the jobs on on various our sciences like ssa and and scat channels so she spoke up there yeah thanks i think if any of our other speakers have things to add also just about what kind of preparation uh people would uh would uh want to to have to to to follow up these kinds of opportunities so just raise your hand if you're on our group here and then and then thanks and then i'll go also to our our panelists here uh in the room uh yeah so morgan i think you raised your hand um yeah at the us just for our work in pasadena we have um usually have student interns pay pay internships in the summer where we bring in undergraduates you can look for announcements on usa jobs in the february march time frame and then for uh graduate students who are graduating we have mental health postdoctoral opportunities they're advertised in the fall so that's our primary way of bringing in new researchers into the uss usually has been in hot postdocs and then the other just thing i want to mention is i mean you can enter from lots of different fields i came in from physics you can come in from another earth sciences engineering geology lots of different areas you can come into this you know seismic hazard analysis from but just take statistics an undergrad or a graduate student please take statistics it's extremely important for almost any other science related field thanks okay uh jessica i think you um yeah thanks jeff so jeff just asked us um a little bit and we just got a bit of an answer but i'm going to push on this a bit more that you know as somebody teaching in a science department apart from statistics what is it that you want to see um what are the training do you want to see from kind of the earth science geophysics realm um for people looking to go into this any you know any additional insight would would be appreciated um i would say programming um so um on a variety of levels both um so at gem we produce you know in addition to producing seismic hazard and risk models um we write software to do all the computations and um then there's a huge amount of uh data analysis tasks and um you know prototyping and so being familiar with with a variety of numerical programming uh techniques we pretty much just stick with python so so you know you don't need to be able to code up like huge thing you know finite element models in fortran or something like that but being able to have um a sort of diversity of programming skills from you know basic data analysis type stuff being able to um uh you know do statistical programming as well as um if you can contribute to the larger models and code bases that we have and having some facility with you know with writing functions and writing tests and you know making nice software rather than just having like a mess of scripts you know sitting in a folder and stuff like that um that's really helpful and and something that's i think pretty outside of what what most people are getting in a geoscience education um the people who come with them with it are generally self-taught and there's big gaps which is going to be you know with a field like psh there's going to always be gaps in people's educations but jessica can we go to uh oh sorry i was going to go to marco next marco and then david yes so in addition to what richard was saying i think it's very important to have knowledge of that analysis because we deal with a variety of of information and so it's very important to be able to handle that and most of all work with that in a quantitative sense um um of course uh it's since seismic cars and seismic risks are interdisciplinary it's important to have a good background in one area but also have a knowledge of other disciplines so if you are a geologist or a geophysicist a little bit of knowledge of the components that are more on the engineering side is important and vice versa that's very important because as we we also learned during the seminar that we had today it's very it's essential to work together to make sure that the engineers are working with the seismologists the seismologists are working with the geologists because that's the only way in which we can improve what we are currently doing so that's and so the students has to be aware that they need to be able to communicate with people with different profiles and be interested in learning many other things apart from the ones that have been learning during their regular curricula that's my and and be interested in in learning new things there is always time to learn new things i i personally think that i have still a lot to learn and i enjoy learning so that's the type of approach they should have thanks let's go to david next yeah i just could hear your question and to follow up on marco i think the multi disciplinary part is really key i i'm a geophysicist by training but what we're looking for and we've got some pretty exciting problems to work on is filling these gaps in these these areas that we identified as the limiting factors and i would say you know on the on a data set side of things there's all sorts of geospatial skills and mapping some of the newest technologies we have like building footprints for the entire planet now but we don't know what's in them but we we could actually use machine learning AI approaches to try to assign those structures based on what we know in different countries in terms of the inventories so geospatial dual engineering analysis that that's necessary to understand those structures but then a lot of the time and matt gersenberg we've got on this quite a bit is communicating what we're doing in terms of losses uncertainty and and very difficult you know post earthquake environment where we estimated the fatalities we need to communicate these and so we need people that are trained with the science but also have a sensitivity to what the user needs are to try to best get these out in a useful form and then just lastly we're really excited about expanding the importance of our products in the post earthquake environment to follow up later in time and be more useful for downstream decision-making and so we're going to be trying to take in ground truth information in SAR data all sorts of observations that are typically not used in our initial model development to try to update those in a in a recursive way as a function of time but the expectations are that these things get better and they get more precise and so people that can cross these disciplines are going to be really fundamental great so let me go to Donna next great I had a question that actually follows up on what Jessica was asking at the end of the last section and that's on the kind of opportunities and challenges of including more constraints on from paleo seismology in time-dependent earthquake models it's one of our most key constraints that addresses the shortcomings of the characteristic earthquake model and is a constraint that can help us with the multi-fault rupture scenarios that Morgan was describing and so I was just curious on your thoughts on that um the paleo seismic data yeah it does help if we have enough of it it's very difficult with only three or four events to put any constraint with the long-term rate of those large expenses especially if you don't know how big they are so that doesn't have a strong effect on the model is there are lots and lots of events um certainly the geologic slip rates that are collected they actually does have a stronger effect just on the the final that's just like how often the toll is you know the total the whole moment budget of the earthquake is goes into that so that's super influential and then the next level of influential I would say would just be like the connectivity not necessarily for faults that are well connected because it doesn't necessarily hazard the sun dependent on like exactly which earthquake it is because we don't know anyway but for a fault that is otherwise very isolated and would have no way to rupture with another fault unless there was a connector that could be discovered that would be very important because it would actually change the maximum magnitude of the rupture so unfortunately you have a lot of events it just doesn't impact the model that well the paleo seismic. Okay Richard you had a follow-up yeah I would say that um uh in my perspective so the main thing that I do is build fault source models for well all over the world um the paleo paleo seismology and and neotechonic slip rates geomorphic rates are incredibly valuable um and you're not going to with with one or two paleo seismic events you know like Morgan said you're not going to be able to really super well resolve questions of you know like what kind of recurrence distribution do we want to use and things like that but for so much of the world um uh virtually no you know you know one or five percent of the faults have even been investigated and so and when people investigate them obviously they like to go to the fastest structures these are the you know the important ones for understanding regional geodynamics and stuff but um what you know what you find is that you'll have you'll have an area that it's got you know seven faults and then and every you know there's 27 studies from one of the faults and no one has touched the others and if you're really trying to understand the geodynamics that might be fine you know what really happens is that there's a big argument and so you know so you want to write the paper that resolves the big argument but like from a hazard perspective that resolving that argument is great getting a better rate is great but you know not ignoring the vast um uh you know see a very obvious but unstudied structures is really important for helping us really understand what's going on in the system there are also things like um understanding in the past what kind of multi-fault ruptures have occurred and um so looking at splay faults you know trenching on splay faults and trenching on um you know potential connectors mapping of kind of distributed deformation zones in between uh principal faults um I think it's really important for for being able to beat down that uncertainties in areas that are poorly studied okay thanks so let's go to uh Ringen thanks um I guess maybe this is follow on to Marco and um uh Marco's uh comment about data so I guess when we talk about education or maybe having young generation uh learning this and learning statistics programming using the tool you're using geospatial analysis those are great but I think the main um backbone of the whole work is is is data like seismic data uh what are the um accuracy levels of seismic data that gets into psha either time dependent or independent um what are the location issues where the catalog was collected from what are the magnitude relationships that are contributing to the end results those are kind of in the educational level maybe something to also go in parallel and not just learning the code itself but really important how the logic trees are kind of put together and um the data is captured correctly the current recurrence intervals the completeness I know it's kind of more generic to to most of you here but uh people who are learning how to do this this is very crucial um I think to to kind of bring on thanks uh Morgan yeah I just wanted to follow up on that comment that I I agree and I think just even beyond like generals is statistical knowledge just the way to think like a scientist in a way that we even like the easiest person to fool is yourself and it's like not just learning like the basics of hypothesis testing here's how you do a p-value and all that but how to run our research program or to study a new phenomenon where you don't know the answer in a way that's like being faithful to like yeah not just the uncertainties of the problem but am I even thinking about it right am I yeah basically am I not fooling myself and like trying to come up with a good story for like this exciting research problem I'm trying to do am I actually being totally honest about what I know and what I don't know I think something you start learning as students and we're also working on it right because um the science is hard and yeah yeah that's a it's a great point it reminds me of a of a fine Richard Feynman quote that that the easiest person to fool is yourself so you know once you stop fooling yourself then you know that's that's that's the first step to to learning anything um let's see let's go to uh Torsten next uh I guess a question for all panelists but um perhaps inspired by by Matt's um description of the government responds as to mitigation in an impressively um you know uh in an impressively effective way perhaps but also um you know Katsu and Gora-san's um illustration where for his study region he showed that the uh taken tsunami has it into account really shifted the the lost curves dramatically and I suppose for for this particular setting we have a flat plane with pretty good building codes so there it was sort of fairly straightforward to say well some tsunami wall would have sort of theoretically helped but I wonder in terms of going from those explorations to having national and international um uh sort of return on investment considerations for mitigation where where do we are right where do we are in terms of using um physics or statistics based models in terms of saying where do where's the best bang for the buck in terms of reducing loss of life and loss of infrastructure I would say that at the global scale there's still a lot to do because uh when you start to um to enter into these arguments uh you you immediately filter out uh 90 percent of the countries that you have in the world unfortunately um very few countries in the world at the moment have a national seismic risk model for example and not just a seismic hazard model and for taking decisions like the ones that you were mentioning is essential to thinking in terms of risk not in terms of hazard unfortunately and most of all even for the other many many components that maybe you think are important when you calculate the hazard they get irrelevant or less relevant when you move when you shift from the hazard to the risk so and since the risk is emphasizing the application or is closer to application um having a strong link between the hazard and the risk is essential and I don't think that we are yet there for at least from my experience there are many efforts there are certainly countries that are trying to move forward in that direction but there's still a long path unfortunately okay thanks I'd like to go back to a question that was uh it was put in the q and a or the specific question was about us canada across border and that was answered in the q and a so people might be able to see that but I wanted to ask the question a little more broadly um when we look at at estimates that come even of well certainly of time dependent but even time independent um estimates um done for different regions or different countries um how well do those estimates blend uh at the borders um or not um well uh it varies a lot of course because uh because uh um uh clearly every country is taking is taking uh um different strategies in modeling the hazard uh different decisions in in accounting for the epistemic uncertainties and that uh clearly has an impact on on the pattern of the shaking expected shaking that you calculate but if you look at in general the the global hazard map that is indeed the result of the combination of 31 models and the corresponding maps and it's true that there are differences but the overall pattern I would say that is um relatively consistent between the different models clearly there is a lot to do to improve for example something that we've been discussing years ago when still was possible to travel without many problems um so during a meeting that was involving representatives from the USGS, GNS Science, Japan was for example to develop benchmarks for testing the ways in which we are building components of another model because that could help in um homogenizing and trying to standardize at least the the fundamental methodologies that we are using for building other models and that would go in the direction clearly of having models that are more and more homogeneous okay but um um as you said at the moment unfortunately we still have uh differences and and not just in terms of the model but also in terms of the information that is used to build the model because clearly go on sorry that's great no I'll just add that uh Mark would just cover the hazard topic when you think about loss and risk those differences are even larger when you think about how the the way buildings are designed historically as well as the way they are built you know depending upon the country's history and the construction and design practice those differences are even larger when you get to the loss and risk side okay thanks I next let me ask a question that came from the audience another one this comes from Gerald Bodden at NASA and uh he says that NASA will be producing a PSINSAR deformation map of North America uh the goal is to do all of North America um through using Sentinel-1 and NISAR data uh so this can be combined with uh GNSS data from uh the Earth Scope network of the Americas and will provide very detailed space geodetic spatial and temporal changes in the land surface so uh David had mentioned that INSAR data can be included in his approach but in general how can these sorts of data sets best be used to support and be integrated into earthquake risk assessment so a comprehensive statement yeah let me just clarify that the use that we were talking about is for post-event um situation awareness with change detection and that's that's the different beast so I'll let someone else answer that I've been working um with Tim Wright uh at um at Leeds Unincorporating um INSAR into um and they produce I think similar to the the PSINSAR strain fields that would be uh discussed here uh very high resolution broad scale deformation maps that I've been using to um uh incorporate into geodetic geologic geodetic block models in order to get fault slip rates and then from the fault slip rates then you have to go to um uh you know then you make hazard models and then you know and that gets transferred to risk so there's you know it's that's that's at the very beginning of the the sausage making process um but I think it's it's still pretty valuable um as long as you have have a way of incorporating geotasy into um understanding of earthquake rates um I've been using block models because I can you know cover a lot of ground and still have pretty high accuracy where the faults are um there are you know one could one could also use the methods um like that Peter Bird's been working on where you take a strain map and then assume every pixel is a seismic source but um it gets really difficult with those in order to properly account for um uh strain localization on geologic faults and you know and particularly when we get into all the things that like Morgan was talking about where you have multifault ruptures and um but you really want the slip rates on the faults to guide where you see those um those ruptures versus just having you know a raster where like you have a big earthquake in the cell you know and maybe not on that one and so um so there needs to be some sort of framework in order to incorporate the geotasy into a fault-based model um really understanding off-fault deformation through NSAR would be awesome um uh you know when combined with the fault model yeah thanks and Morgan mentioned earlier the importance of the moment budget uh and so on so in places that don't have as much let's say you know ground-based data uh if we had a very thorough strain map how how much does that improve our ability to uh to actually make an accurate assessment dramatically so um so for example since since apparently all of North America is being covered I've been working on models for both Canada and Mexico well and going through the whole U.S. and the U.S. part is covered pretty well but once you get north of the Canadian U.S. border you know the density of geotasy coverage really drops dramatically and there's some pretty big arguments on over whether there are even active faults in the BC mainland and um other than up in the McKinsey's and um you know and so NSAR in places like the the interior the Fraser River Valley Thompson area of the British of British Columbia would dramatically increase our um ability to to decide whether we even want to consider these giant mesozoic strike slip faults that run through there as potential earthquake sources okay thanks uh I'm going to do uh one more question from the Q&A here but I'm going to generalize it a little bit um is PSHA already old is this the framework moving forward we have to look for a different framework at some point to to uh to better bring in the sort of time-dependent uh problems or or is it more a matter of tweaking and refining uh and adding some enhancements to what we already have uh Morgan first um yeah I think like I mean after going through the user process yeah I I do wonder if there is a better way if it's not something like simulators for sure just speak just to give them to include everything we know has made this what started out as a simple model blossom into you know something you know extremely complicated to put in like all the multi-fault ruptures that we need we needed hundreds of thousands of ruptures and then we had an undetermined inverse inverse problem that we had to regularize in order to get you know some sort of sensible solutions out of because there's no way to constrain the rates of hundreds of thousands of ruptures we get things that are like we can't individually constrain rupture rates we can get magnitude distributions that are somewhat stable at least with this method but there's all this physics we're leading out that we're just having to add more and more like um statistical rules to like force it in there and so yeah I'm interested to see if perhaps one day simulators could take over this role if they didn't have so many um free parameters if there's a way we could like actually extract something sensible from the that we did before us um then I would be very excited about it uh Dave yeah uh Diego knows I always look for the elephant in the room and there is a there's another beast here that we're not touching and that is earthquake physics so if you have exact if you told me exact history of the faults in them in California I wouldn't know about the next earthquake because uh rupture dynamics can control the outcome and you know we've got places where earthquakes rupture into places that have no stress we've got places that are stressed that don't rupture when they have you know like Parkfield had these enormous high stress drop events right in the preparation zone and it was we know was ready to go and didn't didn't rupture um so we have a very big challenge in the time dependent component that that um requires bringing rupture physics into the antique equation even adding a whole set of new variables to morgan's already growing list but if those physics space like if we could somehow have a physics based simulator that you know I mean RSVCM gets some of it but like actually produce all of the statistical laws that we're putting in by hand now but they just came out emergent from the physics space similar that would be great because we would actually have less hand tuning to get accessible results which is my goal would be like less tuning less forcing forcing the models to do these things that we know it should match ideally we put in what we know and it would all written because we have put in the physics they would match all of the scaling laws that we know and the behavior that we see thanks I'll go to Marco and then Diego and then I'm going to have uh Jessica ask sort of our last new question so uh so Marco so to go back to the question of of PSHA and if PSHA is dead I I don't think it's dead actually it depends probably the way in which we think about PSHA but if I intend PSHA as a framework to integrate various information and so in in that sense I think that everything we've been discussing right now can enter into the construction of more advanced models for performing PSHA analysis in my opinion what you were saying morgan for example so the output of a simulator I see exactly as the output of a of a simulation or a stochastic event set that can be integrated also in a PSHA framework as well thanks Diego thanks Jeff um I guess what I take away from from this meeting is that yeah the time dependence is hard but there's good reasons to be hopeful that progress is being made but I want to ask a related question to this we were just discussing and it's something that quiche or you said when we met before this workshop for all the speakers to organize themselves and it was that the engineers don't want the hazard to change with time because then you have to change the building code with time and there's other applications for changing hazard with time might be difficult like Oklahoma sure you made a one-year hazard map which is now expired um so how do you deal but it can come back at some time in the future so how do downstream applications how do we expect them to deal with this time dependence well there is a sector that definitely has a lot of appetite for time dependence and it's the sector that is represented by Arlene so all their insurance and reinsurance sector is definitely very very interested in having time dependent models so generally work with a variety of communities and definitely the one that is working on in the insurance sector is the one that is asking more and more about time dependent models far more than the engineering sector for the critical facilities as we were trying to explain time dependence it's not that relevant because you look at periods of time in which time dependence is kind of kind of fading but in the insurance sector where where they look for renewals of the policies that is in the order of one year or a few years time dependence is essential yeah that sorry that's also why you know the moving to and I agree with but Morgan about that moving into a more simulated setting is is really important because that is as far as we can tell right now is like the the main solution to to you know to the problem of incorporating time dependence appropriately and but I agree it's it's not necessarily the level of of shaking it's about it's that when when that level of shaking is going to start okay key sure I think you're about to say something yeah I would just say that I think yes insurance sector is very much interested in this problem Marco but I think over the long history of this earthquake problem as we all have learned looking at earthquakes looking at damage I think we have broadly told the community or the consumer of our research that you know this PSHA helps you to design buildings for future you think about earthquakes as you know this kind of beast which will produce strong shaking it will it can bring buildings down things like that but since the time dependent has evolved the thinking has evolved over the last 10 15 years predominantly I would say based on the Canterbury earthquake sequence which really surprised a lot of people and professionals in the community about the impact of you know this main shock and aftershock and how it could influence the the downstream users of these products what I would say is that collectively we need to educate people now back again in terms of earthquakes don't know this dependent and independent thing they just happen it's just a hazard you just need to deal with and you need to basically communicate to the users and when I'm saying users I also include general public you know there is a large effort goes on when big earthquake happens people try to communicate this four shocks and then they call it you know main shock and then aftershock like Morgan was suggesting it's pretty confusing to common people like what exactly it means you know and if you think about like engineering community also engineers have believed and relied on seismologists and geophysicists to give us the best science available on how to think about earthquakes and shaking so engineers can design their buildings but this this this education need to basically have to happen at accelerated pace if we really need to think about this time dependent issue in a more holistic sense. Okay so we do have a number of other questions in the Q&A we're unfortunately not going to be able to get to all of them but one sort of final question topic so I'll turn this over to Jessica. Thanks Jeff so I had a question for everyone and particularly Mark Marker and Richard which is funding sources for this type of work you know I understand that some of this is coming from insurance and other users of these products but in thinking about how we generate science that is open source and accessible to many people to use what are the other sources to fund this this type of work and if you guys can comment on how you see this moving forward from that perspective. So I think that the open source component of it is really important and the biggest way to lower costs is to to simply make things public and you know and not only and both that means public data as well as public software and not something that maybe you know the source is available but you're not allowed to modify it but you know but having both software licenses and data licenses as well as practices that allow people to collaborate and allow people to to share and build on on what's out there so both open SHA that the USGS uses for California and as well as their new software that's and their previous software for the rest of the US those are those are all open source and they're very you know easy to build on software that Gem builds is is also open source and we have tons of users all over the world who are able to collaborate and and and and work on things much more cheaply than they would otherwise I think and able to you know to add two things so that everyone receives the benefits of of the work that individuals put in. In in terms of funding sources for pure research and I don't know you know that that's still just very you know government and institutionally driven Gem is supported it's a public private consortium where we have some we have a lot of mostly government sponsors different governments throughout the world and as well as private sponsors such as you know what's all of the other panelists are you know involved with Gem in some way but but you know there's it can be very difficult for Gem to work with groups in the US National Science Foundation hasn't been interested in providing us with with funds because Gem's based in Italy and so so sort of those sort of institutional barriers on the research side or funding barriers on the research side that deal with you know who's receiving funds and what country can can inhibit international multidisciplinary research so I but I don't know what other like pots of money may be available that we can tap into other thoughts from our speakers all right well we're we're almost to the very end of our time we've had the the the final item on the agenda I'll pass this on to Torsten who is he got the job of of summarizing our afternoon in four minutes we've heard a remarkable beautiful and diverse set of topics as to the time dependence of seismic hazard and I'd like to thank all the participants all the speakers for their contributions it's clear that some of the questions that are still challenging us are old ones what is the degree of the validity of the seismic cycle how do faults link up what do we do with the faults we don't know about but what's new I think is that we're seeing the insights from those long-standing questions really to make their way into our probabilistic descriptions of hazard and recognizing that the time dependence is a universal problem and there is really no steady state on any of the time scales considered and one fault might be characteristic in one time it might be clustered in another and and it's certainly not your grandmas seismic hazard assessment and it's clear that the the problem is a is a is a global one that goes across boundaries really in every sense of of the word and it's clear that it cannot just be purely statistical or purely physical in terms of the modeling and it's clear that it cannot be a national effort it has to be a global one an international one and it's clear that it cannot just be a geophysical one geological one we've heard you know from implications from rock mechanics and to financial products and and we need to address this issue with with global ramifications from in a collaborative approach you know crossing academia to NGOs to to the private sector to the government and and I think the the opportunities are clear in terms of bridging these traditional boundaries it's not just seismic hazard assessment versus mitigations those two are clearly linked and they're they're they're real opportunities in that time dependence because the system is coupled and the physical models that we use to improve the statistical description have to work across time scales and so understanding the present day state of a fault zone in terms of its implications for hazard requires understanding the physics of how fault works and those physics have to be the same in the long-term evolution so there are real opportunities to to make headway here and there are opportunities in terms of solid earth contributing here in terms of enhancing the training for students so that they they make for better contributions in the future enhancing not just the understanding of statistics but also of of programming of data analysis and and building those models that are able to factor in the amazing new constraints that we have to get at the state such as from geodetic observations on the verticals on the horizontals and to make headway on the integration and so I think those are exciting times and the challenges are clear and some of the answers will hopefully lead to a better understanding of hazard for the global community so I'd like to thank all the speakers again and all the contributions and the material from this session will be available on the national academy's website and that's it for today so thanks everybody