Introduction to this special section: Generative and physics-informed AI

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Integrating advanced artificial intelligence (AI) into geoscience represents a pivotal moment, redefining how we approach exploration and interpretation of the earth's subsurface. Generative AI methods, such as large language models (LLMs), diffusion models, and physics-informed learning, offer new ways to simulate, invert, and interpret seismic data. LLMs are increasingly used in various seismic tasks ranging from interpolation and denoising to direct inversion for subsurface properties. Promising attempts have been made to develop foundational models that treat poststack seismic data like natural images. Prestack causality-aware and spatially aware foundational models have not yet been explored extensively. Diffusion models that draw samples from a learned distribution enhance data sets by generating synthetic subsurface models, filling data gaps, and creating plausible scenarios that support testing and validation. Physics-informed AI bridges the gap between empirical machine-learning approaches and traditional physics-based methodologies. Agentic AI — an emergent field leveraging the autonomous capabilities of LLMs for geophysical tasks — further expands the geophysicist's toolkit for seismic processing and workflow automation.