When Geoscience Meets Generative AI and Large Language Models:
  Foundations, Trends, and Future Challenges
Abdenour Hadid Tanujit Chakraborty Daniel Busby
Generative Artificial Intelligence (GAI) represents an emerging field that
promises the creation of synthetic data and outputs in different modalities.
GAI has recently shown impressive results across a large spectrum of
applications ranging from biology, medicine, education, legislation, computer
science, and finance. As one strives for enhanced safety, efficiency, and
sustainability, generative AI indeed emerges as a key differentiator and
promises a paradigm shift in the field. This paper explores the potential
applications of generative AI and large language models in geoscience. The
recent developments in the field of machine learning and deep learning have
enabled the generative model's utility for tackling diverse prediction
problems, simulation, and multi-criteria decision-making challenges related to
geoscience and Earth system dynamics. This survey discusses several GAI models
that have been used in geoscience comprising generative adversarial networks
(GANs), physics-informed neural networks (PINNs), and generative pre-trained
transformer (GPT)-based structures. These tools have helped the geoscience
community in several applications, including (but not limited to) data
generation/augmentation, super-resolution, panchromatic sharpening, haze
removal, restoration, and land surface changing. Some challenges still remain
such as ensuring physical interpretation, nefarious use cases, and
trustworthiness. Beyond that, GAI models show promises to the geoscience
community, especially with the support to climate change, urban science,
atmospheric science, marine science, and planetary science through their
extraordinary ability to data-driven modeling and uncertainty quantification.