Seismic arrival-time picking on distributed acoustic sensing data using semi-supervised learning
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1.
California Institute of Technology
Abstract
Distributed Acoustic Sensing (DAS) is an emerging technology for earthquake monitoring and subsurface imaging. However, its distinct characteristics, such as unknown ground coupling and high noise level, pose challenges to signal processing. Existing machine learning models optimized for conventional seismic data struggle with DAS data due to its ultra-dense spatial sampling and limited manual labels. We introduce a semi-supervised learning approach to address the phase-picking task of DAS data. We use the pre-trained PhaseNet model to generate noisy labels of P/S arrivals in DAS data and apply the Gaussian mixture model phase association (GaMMA) method to refine these noisy labels and build training datasets. We develop PhaseNet-DAS, a deep learning model designed to process 2D spatio-temporal DAS data to achieve accurate phase picking and efficient earthquake detection. Our study demonstrates a method to develop deep learning models for DAS data, unlocking the potential of integrating DAS in enhancing earthquake monitoring.
Copyright and License
© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Acknowledgement
We would like to thank the reviewers and editors for their insightful comments and constructive suggestions. We would like to thank James Atterholt for his assistance in building the training dataset. We would like to thank James Atterholt, Qiushi Zhai, Yan Yang, and Jiaqi Fang for their constructive discussions. We would also like to thank the California Broadband Cooperative for fiber access for the Distributed Acoustic Sensing array used in this experiment. We would like to thank OptaSense for the support provided for this calibration experiment. In particular, we thank Martin Karrenbach, Victor Yartsev, and Vlad Bogdanov. This study is funded by the Gordon Moore Foundation (Z.Z.), the National Science Foundation (NSF) through the Faculty Early Career Development (CAREER) award number 1848166 (Z.Z.), and the United States Geological Survey Earthquake Hazards Program award number G22AP00067 (Z.Z.).
Contributions
W.Z. developed and implemented the algorithm, conducted the experiments and analysis. E.B., Z.R., and Z.Z. co-designed the study. J.L. conducted the picking time error analysis. J.L. and J.Y. built the DAS dataset and tested the model. Z.R. and Z.Z. advised the project. All authors contributed to writing and editing the manuscript.
Data Availability
The example dataset of the Ridgecrest north cable is available at: https://doi.org/10.57967/hf/0962. These examples are extracted from the public Ridgecrest DAS dataset hosted under the SCEDC Earthquake Data AWS Public Dataset (https://scedc.caltech.edu/data/getstarted-pds.html). The other DAS datasets used for training and testing are available upon request from Zhongwen Zhan (zwzhan@caltech.edu).
Code Availability
The pre-trained model of PhaseNet is available at https://ai4eps.github.io/PhaseNet/. The model of GaMMA is available at https://ai4eps.github.io/GaMMA/. The source code and pre-trained model of PhaseNet-DAS is available at https://ai4eps.github.io/EQNet/89.
Conflict of Interest
The authors declare no competing interests.
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Additional details
- PMCID
- PMC10713581
- Gordon and Betty Moore Foundation
- EAR-1848166
- National Science Foundation
- G22AP00067
- United States Geological Survey
- Caltech groups
- Division of Geological and Planetary Sciences, Seismological Laboratory