Intelligent Processing of 3D Geophysical Data
3D geophysical data (e.g., seismic volumes) are characterized by their massive scale, low signal-to-noise ratios, and highly complex features. These properties pose significant bottlenecks for traditional data processing and interpretation. My research focuses on exploring and applying robust deep learning models (such as 3D-CNNs, Transformers, and Generative Models) to enable high-precision, automated analysis of these data volumes. The goal is to more accurately identify subsurface structures and invert physical parameters, overcoming long-standing challenges in applying effective AI to large-scale 3D geophysical datasets.
Insightful Applications of Cutting-Edge AI in Geophysics
I am interested in creatively adapting cutting-edge AI methodologies to solve fundamental geophysical problems. This includes, but is not limited to: applying advanced generative models (e.g., VAEs, GANs, Diffusion Models) for subsurface model generation and geophysical data reconstruction; developing multi-modal frameworks to fuse disparate datasets (such as seismic, well-log, and geological data); leveraging Physics-Informed Neural Networks (PINNs) for solving complex forward and inverse problems; and ensuring model robustness and interpretability through Explainable AI (XAI). I am committed to unlocking the potential of AI to provide novel, insightful perspectives for Geoscience.