气候变化领域集成服务门户(CCPortal): SeismoGen: Seismic Waveform Synthesis Using GAN With Application to Seismic Data Augmentation

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DOI	10.1029/2020JB020077
	SeismoGen: Seismic Waveform Synthesis Using GAN With Application to Seismic Data Augmentation
	Wang T.; Trugman D.; Lin Y.
发表日期	2021
ISSN	21699313
卷号	126 期号:4
英文摘要	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. Published 2021. This article is a U.S. Government work and is in the public domain in the USA.
英文关键词	data augmentation, earthquake classifier; generative adversarial networks; neural network; seismic waveform
语种	英语
来源期刊	Journal of Geophysical Research: Solid Earth
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/187246
作者单位	Geophysics Group, Earth and Environment Science Division, Los Alamos National Laboratory, Los Alamos, NM, United States; School of Information Sciences, University of Pittsburgh, Pittsburgh, PA, United States; Jackson School of Geosciences, The University of Texas at Austin, Austin, TX, United States
推荐引用方式 GB/T 7714	Wang T.,Trugman D.,Lin Y.. SeismoGen: Seismic Waveform Synthesis Using GAN With Application to Seismic Data Augmentation[J],2021,126(4).
APA	Wang T.,Trugman D.,&Lin Y..(2021).SeismoGen: Seismic Waveform Synthesis Using GAN With Application to Seismic Data Augmentation.Journal of Geophysical Research: Solid Earth,126(4).
MLA	Wang T.,et al."SeismoGen: Seismic Waveform Synthesis Using GAN With Application to Seismic Data Augmentation".Journal of Geophysical Research: Solid Earth 126.4(2021).