Making phase-picking neural networks more consistent and interpretable
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Abstract
Improving the interpretability of phase‐picking neural networks remains an important task to facilitate their deployment to routine, real‐time seismic monitoring. The popular phase‐picking neural networks published in the literature lack interpretability because their output prediction scores do not necessarily correspond with the reliability of phase picks and can even be highly inconsistent depending on how we window the waveform data. Here, we show that systematically shifting the waveforms during training and using an antialiasing filter within the neural network architecture can substantially improve the consistency of the output prediction scores and can even make them scale with the signal‐to‐noise ratios of the waveforms. We demonstrate the improvements by applying these approaches to a commonly used phase‐picking neural network architecture and using waveform data from the 2019 Ridgecrest earthquake sequence.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Making phase-picking neural networks more consistent and interpretable |
| Series title | The Seismic Record |
| DOI | 10.1785/0320230054 |
| Volume | 4 |
| Issue | 1 |
| Publication Date | March 06, 2024 |
| Year Published | 2024 |
| Language | English |
| Publisher | Seismological Society of America |
| Contributing office(s) | Geologic Hazards Science Center - Seismology / Geomagnetism |
| Description | 9 p. |
| First page | 72 |
| Last page | 80 |