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Deep Learning of Seismograms

This is the diiP's tenth distinguished lecture, delivered this time by Dr. S. Mostafa Mousavi, frrom Google and Stanford. More information and materials are available on our website: https://u-paris.fr/diip/diip-seminars/ abstract: Seismology is the study of seismic waves to understand the sources of those waves – such as earthquakes, explosions, volcanic eruptions, glaciers, landslides, ocean waves, thunderstorms, etc.- and to infer the structure and properties of planetary interiors. The availability of large-scale labeled datasets and the suitability of deep neural networks for seismic data processing have pushed deep learning to the forefront of fundamental, long-standing research investigations in seismology. However, some aspects of applying deep-learning to seismology are likely to prove instructive for the geosciences more broadly. In my talk, I will present some of the recent progress in AI-based seismic monitoring and how they improve our understanding of Earth’s physical processes. bio: Mostafa Mousavi is a research scientist at Google and an Adjunct Professor at Stanford University. His research focuses on extracting insights about Earth and its physical processes from weak seismic signals through innovative methodological solutions. He is interested in pattern recognition in large sensor datasets and data-driven scientific discovery in earthquake seismology. He develops domain-aware algorithms, incorporating state-of-the-art techniques from signal processing, artificial intelligence, and statistics to extract scientifically valuable information from large-scale seismic datasets. His goal is to reach a deeper understanding of the information carried out by high-dimensional seismic data and use them to characterize source/path/site effects and understand the dynamics of the seismicity at local and regional scales. He received his Ph.D. from the University of Memphis in 2017 and was a postdoctoral fellowship at Stanford University from 2017 to 2019.