Poster #236, Seismology
2019 Ridgecrest earthquake sequence: RAPID seismic deployment and a new aftershock catalog based on machine learning
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Poster Presentation
2021 SCEC Annual Meeting, Poster #236, SCEC Contribution #11585
es. Then we associate them and determine location of events, first absolute and then relative relocation. Our catalog contains 66% more earthquakes compared to the catalog produced by the Southern California Seismic Network. We use our catalog to train the machine learning algorithm. We use a convolutional neural network to detect events and recurrent neural network to pick phases using this algorithm. Association and location are similar to the previous stage. This new earthquake catalog based on machine-learning algorithm contains 97,855 events detected and located during the three weeks of data analyzed so far. The level of detection is similar to Ross et al., 2019, and 73% higher than Shelly, 2020, when compared to the same time period. Our catalog generally shows similar features as compared to other catalogs, but appears to be more clustered. There are, however, some remarkable differences in details. For example, we observe interesting differences in pattern of earthquake distribution near the peak slip patch, and southeastern end of the main fault. We plan to update our catalog including data from RAPID deployment, and expect to observe more details of the structure and aftershock dynamics.
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