[S-SS05] Innovative data analysis methods for characterization of seismicity

[SSS05-01] Deep Learning for Earthquake Monitoring

★Invited Papers

*Weiqiang Zhu¹, S. Mostafa Mousavi¹, Kai Sheng Tai², Yongsoo Park¹, Yen Joe Tan¹, William Ellsworth¹, Gregory C. Beroza¹ (1.Department of Geophysics, Stanford University, 2.Computer Science Department, Stanford University)

Seismic networks are continuously recording seismic data around the world. These data contain rich information about earthquake processes, but traditional auto-processing algorithms can not fully extract this information. Fortunately, the existence of large manually labeled data sets provides us an excellent opportunity for developing deep-learning algorithms for earthquake monitoring. Deep learning is an effective, data-driven way to build a nonlinear map from a high-dimensional input distribution to a target distribution of interest. In this work, we present some of our recent efforts in developing deep-learning models to denoise, detect, pick, cluster, and associated earthquakes. These new algorithms and rapidly evolving deep-learning techniques can improve earthquake catalog generation and thus provide a much clearer and more detailed picture of earthquake processes.