A. Inbal
J. Searcy
David R. Shelly
R. Bürgmann
A. M. Thomas
2021
<div class="article-section__content en main"><p>Low-frequency earthquakes are a seismic manifestation of slow fault slip. Their emergent onsets, low amplitudes, and unique frequency characteristics make these events difficult to detect in continuous seismic data. Here, we train a convolutional neural network to detect low-frequency earthquakes near Parkfield, CA using the catalog of Shelly (2017),<span> </span><a class="linkBehavior" href="https://doi.org/10.1002/2017jb014047" data-mce-href="https://doi.org/10.1002/2017jb014047">https://doi.org/10.1002/2017jb014047</a><span> </span>as training data. We explore how varying model size and targets influence the performance of the resulting network. Our preferred network has a peak accuracy of 85% and can reliably pick low-frequency earthquake (LFE) S-wave arrival times on single station records. We demonstrate the abilities of the network using data from permanent and temporary stations near Parkfield, and show that it detects new LFEs that are not part of the Shelly (2017),<span> </span><a class="linkBehavior" href="https://doi.org/10.1002/2017jb014047" data-mce-href="https://doi.org/10.1002/2017jb014047">https://doi.org/10.1002/2017jb014047</a><span> </span>catalog. Overall, machine-learning approaches show great promise for identifying additional low-frequency earthquake sources. The technique is fast, generalizable, and does not require sources to repeat.</p></div>
application/pdf
10.1029/2021GL093157
en
American Geophysical Union
Identification of low-frequency earthquakes on the San Andreas fault with deep learning
article