Speaker: Zhixiang Guo

Date: Thursday 8th of January 2026, 1:15pm

Abstract:

In recent years, deep learning has achieved transformative progress across many domains, with foundation models demonstrating remarkable generalization under few-shot and even zero-shot settings. However, training such models from scratch is often impractical in geophysics due to limited labeled data and constrained computational resources. To address this, we investigate an efficient adaptation strategy that fine-tunes foundation models pretrained in other domains for geophysical tasks, aiming for a cost-effective pathway to leverage large models. Within our  adaptation framework, the adapted models consistently exhibit stronger generalization and improved performance over conventional deep learning baselines on multiple small, labeled datasets. These findings provide new insights for deploying foundation models in geophysics. This seminar also surveys recent advances in deep learning for seismic interpretation and inversion, and reports my ongoing progress on diffusion-based implicit structural modeling jointly constrained by faults and horizons.