Scaling Geoscience with Modern AI: Synthetic Data and Agentic Workflows

Rapidly scaling subsurface understanding to meet global resource demands is currently hindered by labeled data scarcity and the complexity of integrating multi-modal datasets. This paper presents a framework leveraging two cutting-edge Artificial Intelligence (AI) paradigms to overcome these bottlenecks: generative synthetic data and agentic workflows. First, we utilize diffusion-based generative models to synthesize high-fidelity geophysical datasets, including 3D seismic cubes and Distributed Acoustic Sensing (DAS) records. This addresses the critical lack of labeled examples by producing realistic training data via conditioned generation. Second, we introduce Geoscience AI Agents—autonomous systems powered by large multimodal models capable of reasoning through complex, multi-step tasks. Unlike traditional single-task models, these agents integrate diverse inputs, such as geological reports and well logs, to automate sophisticated workflows including 3D geological modeling. We demonstrate that combining generative AI for data enrichment with agentic systems for automated interpretation offers a transformative path forward, significantly enhancing the accuracy and scalability of subsurface characterization.