Geoscience meets Generative AI

When Geoscience Meets Generative AI and Large Language Models: Foundations, Trends, and Future Challenges.

I found this recent paper by Hadid et al (2024) helped spark a few ideas for me on how we may apply large Language Models (LLM) to real world problems which include geological information and related disciplines. Explainability and trustworthiness of these approaches is critical.

Abstract below:

“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 sustain ability, 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 promise 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.”

Paper here: https://arxiv.org/pdf/2402.03349.pdf