Making Phase-Picking Neural Networks More Consistent and Interpretable Article Swipe

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Yongsoo Park , Brent Delbridge , D. R. Shelly ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.1785/0320230054

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.

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Interpretability Computer science Waveform Artificial neural network Filter (signal processing) Artificial intelligence Reliability (semiconductor) Consistency (knowledge bases) Machine learning Task (project management) Phase (matter) SIGNAL (programming language) Data mining Pattern recognition (psychology) Engineering Computer vision Power (physics) Telecommunications Physics Organic chemistry Programming language Chemistry Radar Systems engineering Quantum mechanics

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Type: article
Language: en
Landing Page: https://doi.org/10.1785/0320230054
PDF: https://pubs.geoscienceworld.org/ssa/tsr/article-pdf/4/1/72/6284745/tsr-2023054.1.pdf
OA Status: diamond
Cited By: 3
References: 26
Related Works: 10
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DOI: https://doi.org/10.1785/0320230054

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Title: Making Phase-Picking Neural Networks More Consistent and Interpretable

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Language: en

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Publication year: 2024

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Publication date: 2024-01-01

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Authors: Yongsoo Park, Brent Delbridge, D. R. Shelly

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Landing page: https://doi.org/10.1785/0320230054

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Concepts: Interpretability, Computer science, Waveform, Artificial neural network, Filter (signal processing), Artificial intelligence, Reliability (semiconductor), Consistency (knowledge bases), Machine learning, Task (project management), Phase (matter), SIGNAL (programming language), Data mining, Pattern recognition (psychology), Engineering, Computer vision, Power (physics), Telecommunications, Physics, Organic chemistry, Programming language, Chemistry, Radar, Systems engineering, Quantum mechanics

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Cited by: 3

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