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Moho topography yields insights into the evolution of the lithosphere and the strength of the lower crust. The Moho reflected phase (PmP) samples this key boundary and may be used in concert with the first arriving P phase to infer crustal thickness. The densely sampled station coverage of distributed acoustic sensing arrays allows for the observation of PmP at fine-scale intervals over many kilometers with individual events. We use PmP recorded by a 100-km-long fiber that traverses a path between Ridgecrest, CA and Barstow, CA to explore Moho variability in Southern California. With hundreds of well-recorded events, we verify that PmP is observable and develop a technique to identify and pick P-PmP differential times with high confidence. We use these observations to constrain Moho depth throughout Southern California, and we find that short-wavelength variability in crustal thickness is abundant, with sharp changes across the Garlock Fault and Coso Volcanic Field.
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Deep Neural Networks for Creating Reliable PmP Database With a Case Study in Southern California
Abstract Recent progresses in artificial intelligence and machine learning make it possible to automatically identify seismic phases from exponentially growing seismic data. Despite some exciting successes in automatic picking of the first P‐ and S‐wave arrivals, auto‐identification of later seismic phases such as the Moho‐reflected PmP waves remains a significant challenge in matching the performance of experienced analysts. The main difficulty of machine‐identifying PmP waves is that the identifiable PmP waves are rare, making the problem of identifying the PmP waves from a massive seismic database inherently unbalanced. In this work, by utilizing a high‐quality PmP data set (10,192 manual picks) in southern California, we develop PmPNet, a deep‐neural‐network‐based algorithm to automatically identify PmP waves efficiently; by doing so, we accelerate the process of identifying the PmP waves. PmPNet applies similar techniques in the machine learning community to address the unbalancement of PmP datasets. The architecture of PmPNet is a residual neural network (ResNet)‐autoencoder with additional predictor block, where encoder, decoder, and predictor are equipped with ResNet connection. We conduct systematic research with field data, concluding that PmPNet can efficiently achieve high precision and high recall simultaneously to automatically identify PmP waves from a massive seismic database. Applying the pre‐trained PmPNet to the seismic database from January 1990 to December 1999 in southern California, we obtain nearly twice more PmP picks than the original PmP data set, providing valuable data for other studies such as mapping the topography of the Moho discontinuity and imaging the lower crust structures of southern California.
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- PAR ID:
- 10369432
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Solid Earth
- Volume:
- 127
- Issue:
- 4
- ISSN:
- 2169-9313
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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