AI Applications for Earthquake Monitoring
Diverse algorithms have been developed for efficient earthquake signal detection and processing. These algorithms are becoming increasingly important as seismologists strive to extract as much insight as possible from exponentially increasing volumes of continuous seismic data. Convolutional and recurrent networks have each 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 our presentation, we demonstrate the performance of some of these tools applied to seismic signals. AI-based techniques have the potential to improve our monitoring ability and as a result understanding of earthquake processes.
Mostafa Mousavi received the M.S. degree in Risk Engineering from the University of Tehran, Tehran, Iran in 2010 and the M.S. and Ph.D. degrees in geophysics from University of Memphis, TN, USA in 2017. He is currently a Postdoctoral Research Fellow at Stanford University, CA, USA. He is the author of one book, 20 journals, and 3 conference papers. His research interests include machine learning/deep learning, signal processing, statistical seismology, and observational earthquake seismology. Dr. Mousavi is a fellow of the National Elite Foundation of Iran and a recipient of SEP/SEG award by ExxonMobil in 2014.