Rapid Characterization of the July 2019 Ridgecrest, California, Earthquake Sequence From Raw Seismic Data Using Machine‐Learning Phase Picker
Min Liu, Miao Zhang, Weiqiang Zhu, William L. Ellsworth, Hongyi Li
Abstract
Abstract The two principle earthquakes of the July 2019 Ridgecrest, California, earthquake sequence, M W 6.4 and 7.1, and their immediate foreshocks and thousands of aftershocks present a challenging environment for rapid analysis and characterization of this sequence as it unfolded. In this study, we analyze the first 6 days of the sequence using continuous data from available seismic networks to detect and locate earthquakes associated with the earthquake sequence. We build a high‐precision earthquake catalog using a deep‐neural‐network‐based picker—PhaseNet and a sequential earthquake association and location workflow. Without prior information, we automatically detect and locate more than twice as many earthquakes as the routine catalog. Our high‐precision earthquake catalog reveals detailed spatiotemporal evolution of the earthquake sequence and clearly defines multiple faults activated during the sequence. Our study demonstrates that it is possible to characterize earthquake sequences from raw seismic data using a well‐trained machine‐learning picker and our workflow.