Injection-induced earthquakes on complex fault zones of the Raton Basin illuminated by machine-learning phase picker and dense nodal array

Wang, Ruijia; Schmandt, Brandon; Zhang, Miao; Glasgow, M. E.; Kiser, E.; Rysanek, Sarah; Stairs, Ryan

doi:10.1002/essoar.10503048.1

Cited by 1 publication

(1 citation statement)

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“…In the seismology community, there have been several recent works studying the importance of the model generalization (Chai et al., 2020; Liu et al., 2020; Mousavi et al., 2020; Park et al., 2020; K. Wang, Ellsworth, & Beroza, 2020; R. Wang, Schmandt, et al., 2020). As demonstrated in most of these recent studies, DNN‐based models yield some generalization ability that would allow for a model being trained on one data set to then be applied to a different study region.…”

Section: Introductionmentioning

confidence: 99%

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

2021

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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.

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