Automatic identification of mantle seismic phases using a Convolutional Neural Network

Garcia, J.A., Waszek, L., Tauzin, B., and Schmerr, N. (2021) Automatic identification of mantle seismic phases using a Convolutional Neural Network. Geophysical Research Letters, 48 (18). e2020GL091658.

[img] PDF (Published Version) - Published Version
Restricted to Repository staff only

View at Publisher Website:


Typical seismic waveform data sets comprise hundreds of thousands to millions of records. Compilation is performed by time-consuming handpicking of phase arrival times, or signal processing algorithms such as cross-correlation. The latter generally underperform compared to handpicking. However, differences in picking methods creates variations in models and interpretation of Earth's structure. Here, we exploit the pattern recognition capabilities of Convolutional Neural Networks (CNN). Using a large handpicked data set, we train a CNN model to identify the seismic shear phase SS. This accelerates, automates, and makes consistent data compilation, a task usually completed by visual inspection and influenced by scientists' choices. The CNN model is employed to identify precursors to SS generated by mantle discontinuities. It identifies precursors in stacked and individual seismograms, producing new measurements of the mantle transition zone with quality comparable to handpicked data. This rapid acquisition of high-quality observations has implications for automation of future seismic tomography studies.

Item ID: 69439
Item Type: Article (Research - C1)
ISSN: 1944-8007
Keywords: Seismology, Convolutional Neural Networks, body waves, mantle
Copyright Information: © 2021. American Geophysical Union. All Rights Reserved
Funders: National Science Foundation (NSF), Australian Research Council (ARC), European Union Horizon 2020 (EU H2020)
Projects and Grants: NSF EAR-1853662, NSF EAR-1661985, ARC Discovery Early Career Research Award DE170100329, NSF EAR-1361325, EU Marie Sklodowska-Curie grant agreement 793824
Research Data:
Date Deposited: 28 Sep 2021 23:01
FoR Codes: 37 EARTH SCIENCES > 3706 Geophysics > 370609 Seismology and seismic exploration @ 50%
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461104 Neural networks @ 50%
SEO Codes: 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280107 Expanding knowledge in the earth sciences @ 50%
28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 50%
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page