Zhengfa Bi

I am a postdoctoral researcher in the Earth and Environmental Sciences Area (EESA) at Lawrence Berkeley National Laboratory, working on the project “Collection of Microearthquake (MEQ) Data for Mitigating, Characterizing, and Understanding Induced Seismicity for Optimizing the Performance of Enhanced Geothermal Systems (EGS).”
My research focuses on imaging and monitoring subsurface seismic velocity structures and stress evolution using continuous seismic recordings from geothermal fields. A key objective is to characterize in situ stress conditions, which play a critical role in controlling fracture distribution, aperture, and permeability, and thus directly influence reservoir performance.
In this context, I develop and apply machine learning approaches for seismicity forecasting, seismic imaging, inversion, and data interpretation. My work integrates data-driven methods with geophysical principles to better understand induced seismic processes and improve the predictability and management of geothermal systems. I also explore the application of these methods to broader geophysical datasets.

Area of Interest

• Geophysics
• Knowledge Representation and Machine Learning
• Seismology and Seismic Exploration

Education

• PhD|University of Science and Technology of China, Hefei, China|2023