MIT Earth Resources Laboratory presents Dr. Mostafa Mousavi, Research Scientist at Stanford, on "Artificial Intelligence for Earthquake Monitoring."

"Diverse models have been developed for efficient earthquake signal processing and characterization. These AI-based models are becoming increasingly important as seismologists strive to extract as much insight as possible from exponentially increasing volumes of continuous seismic data. Deep neural networks have been shown to be promising tools for this. We have developed a number of deep learning tools for more efficient processing and characterizing of earthquake signals. In my presentation, I demonstrate the performance of some of these tools applied to seismic data. AI-based techniques have the potential to improve our monitoring ability and as a result understanding of earthquake processes and hazards."

Mostafa Mousavi’s research is at the interface of earthquake and exploration seismology and an interdisciplinary blend of seismology, statistics, and computer science. He has developed statistical signal processing methods for seismic signal denoising and decomposition, used machine learning for earthquake signal detection and characterization, studied crustal and upper mantel attenuation, studied the seismic hazard assessment of induced seismicity, and performed statistical analyses of spatio-temporal patterns of seismicity. He received his Ph.D. from the University of Memphis in 2017, followed by a postdoctoral fellowship at Stanford University (2017-2019).