SeismoGen: Seismic Waveform Synthesis Using GAN With Application to Seismic Data Augmentation

Journal Article · 16 April 2021 · Journal of Geophysical Research. Solid Earth

DOI: https://doi.org/10.1029/2020JB020077 · OSTI ID:1779934

Wang, Tiantong ^[1]; Trugman, Daniel  ^[2]; Lin, Youzuo  ^[3]

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1. Geophysics Group Earth and Environment Science Division Los Alamos National Laboratory Los Alamos NM USA, School of Information Sciences University of Pittsburgh Pittsburgh PA USA
2. Jackson School of Geosciences The University of Texas at Austin Austin TX USA
3. Geophysics Group Earth and Environment Science Division Los Alamos National Laboratory Los Alamos NM USA

Abstract Detecting earthquake arrivals within seismic time series can be a challenging task. Visual, human detection has long been considered the gold standard but requires intensive manual labor that scales poorly to large data sets. In recent years, automatic detection methods based on machine learning have been developed to improve the accuracy and efficiency. However, the accuracy of those methods relies on access to a sufficient amount of high‐quality labeled training data, often tens of thousands of records or more. We aim to resolve this dilemma by answering two questions: (1) provided with a limited amount of reliable labeled data, can we use them to generate additional, realistic synthetic waveform data? and (2) can we use those synthetic data to further enrich the training set through data augmentation, thereby enhancing detection algorithms? To address these questions, we use a generative adversarial network (GAN), a type of machine learning model which has shown supreme capability in generating high‐quality synthetic samples in multiple domains. Once trained, our GAN model is capable of producing realistic seismic waveforms of multiple labels (noise and event classes). Applied to real Earth seismic data sets in Oklahoma, we show that data augmentation from our GAN‐generated synthetic waveforms can be used to improve earthquake detection algorithms in instances when only small amounts of labeled training data are available.

• Sponsoring Organization: USDOE
• OSTI ID: 1779934
• Journal Information: Journal of Geophysical Research. Solid Earth, Journal Name: Journal of Geophysical Research. Solid Earth Vol. 126 Journal Issue: 4; ISSN 2169-9313
• Publisher: American Geophysical Union (AGU)Copyright Statement
• Country of Publication: United States
• Language: English