Deep Learning Models Augment Analyst Decisions for Event Discrimination

Linville, Lisa; Pankow, Kristine; Draelos, Timothy

doi:10.1029/2018GL081119

Deep Learning Models Augment Analyst Decisions for Event Discrimination

Journal Article · Sun Feb 24 23:00:00 EST 2019 · Geophysical Research Letters

DOI:https://doi.org/10.1029/2018GL081119· OSTI ID:1496986

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University of Utah Seismograph Stations University of Utah, Salt Lake City, UT (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Long-term seismic monitoring networks are well positioned to leverage advances in machine learning because of the abundance of labeled training data that curated event catalogs provide. In this study, we explore the use of Convolutional and Recurrent Neural Networks to accomplish discrimination of explosive and tectonic sources for local distances. Using a 5-year event catalog generated by the University of Utah Seismograph Stations we train models to produce automated event labels using 90 s event spectrograms from 3-component and single-channel sensors. Both network architectures are able to replicate analyst labels above 98%. Most commonly, model error is the result of label error (70% of cases). Accounting for mislabeled events (~1% of the catalog) model accuracy for both models increases to above 99%. Finally, classification accuracy remains above 98% for shallow tectonic events, indicating that spectral characteristics controlled by event depth do not play a dominant role in event discrimination.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC04-94AL85000; NA0003525

OSTI ID:: 1496986

Alternate ID(s):: OSTI ID: 1504780

Report Number(s):: SAND--2019-1319J; 672351

Journal Information:: Geophysical Research Letters, Journal Name: Geophysical Research Letters Journal Issue: 7 Vol. 46; ISSN 0094-8276

Publisher:: American Geophysical UnionCopyright Statement

Country of Publication:: United States

Language:: English

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