Detecting and locating aftershocks for the 2020 MW 6.5 Stanley, Idaho earthquake using convolutional neural networks
Abstract
Our study is to build an aftershock catalog with a low magnitude of completeness for the 2020 MW 6.5 Stanley, Idaho earthquake. This is challenging because of low signal to noise ratio for recorded seismograms. Therefore, we apply convolutional neural networks (CNNs) and use 2-D time-frequency feature maps as inputs for aftershock detection. Another trained CNN is used to automatically pick P-wave arrival times, which are then used in both nonlinear and double-difference earthquake location algorithms. Our new one-month-long catalog has 4,644 events and a completeness magnitude Mc=1.9, which has over 7 times more events and 0.9 lower Mc than the current USGS-NEIC catalog. The distribution and expansion of these aftershocks improve the resolution of two NNW trending faults with different dip angles, providing further support for a central step-over region that changed the earthquake rupture trajectory and induced sustained seismicity.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.S42D0186L