Subsurface Anatomy

Subsurface anatomy research focuses on developing advanced methods for characterizing and monitoring underground structures and processes, primarily driven by needs in carbon capture and storage, geothermal energy, and resource extraction. Current research heavily utilizes machine learning, particularly deep learning architectures like convolutional neural networks and reinforcement learning, coupled with Bayesian inference and other probabilistic techniques, to analyze diverse subsurface data (e.g., seismic, gravity, well logs, drill core images). These improved analytical capabilities enhance our understanding of subsurface heterogeneity, improve reservoir modeling, and enable more efficient and safer subsurface operations, ultimately contributing to advancements in energy production and environmental remediation.