Seismic networks have consistently improved across extensive temporal and spatial scales, enhancing our capability to record subtle shaking signals of the Earth. The vast seismic archive poses a challenge for efficient data analysis, but also presents an opportunity to uncover many hidden signals from small earthquakes, anthropogenic activities, and various geological processes. Recent developments in deep learning have significantly improved earthquake monitoring, enabling the rapid and accurate detection of many more small earthquakes with better-constrained source parameters. I will share our work on developing a deep-learning-based earthquake monitoring workflow using both seismic networks and distributed acoustic sensing. We applied the new workflow to study complex earthquake sequences, including tectonic, induced, and volcanic earthquakes.
Bio
Dr. Weiqiang Zhu is an Assistant Professor in the department of Earth and Planetary Sciences at the University of California, Berkeley. His research aims to understand earthquake physics and statistics by applying cutting-edge artificial intelligence and scientific computing methods to gain new insights from large seismic datasets.
Location:
373 Cory Hall