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Machine learning model generates realistic seismic waveforms – Los Alamos reporter
LANL News
A new machine learning model that generates realistic seismic waveforms will reduce manual work and improve seismic detection, according to a study recently published in JGR Solid Earth.
“To verify the effectiveness of our generative model, we applied it to seismic field data collected in Oklahoma,” said Yuzu Lin, computational scientist in the Geophysics Group at Los Alamos National Laboratory and lead investigator for the project. “Through a series of qualitative and quantitative tests and criteria, we have seen that our model can generate high-quality artificial waveforms and improve machine learning-based earthquake detection algorithms.”
Detecting earthquakes quickly and accurately can be a challenging task. Visual discovery by people has long been considered the gold standard, but it requires extensive manual work that does not lend itself to large data sets. In recent years, automatic detection methods based on machine learning have improved the accuracy and efficiency of data collection; However, the accuracy of these methods depends on accessing a large amount of high-quality, titled training data, often tens of thousands of records or more.
To solve this data dilemma, the research team developed SeismoGen based on the Generative Adversarial Network (GAN), a kind of deep generative model that can generate high-quality synthetic samples in multiple domains. In other words, deep generative models train machines to do things and create new data that can pass as real.
Once trained, the SeismoGen model is able to produce realistic seismic waveforms for multiple posters. When applied to real Earth seismic data sets in Oklahoma, the team saw that augmenting data from the artificial waveforms generated by SeismoGen could be used to improve earthquake detection algorithms in situations where only small amounts of labeled training data are available.
Paper: SeismoGen: Synthesis of Seismic Waveform Using GAN with Application to Augmentation of Seismic Data, Tiantong Wang, Daniel Trugman and Youzuo Lin. Published in JGR Solid Earth. April, Volume 126, Issue 4, e2020JB020077, 2021. DOI: 10.1029 / 2020JB020077
Funding: Research was supported by the Center for Space and Earth Sciences and the Laboratory-Oriented Research and Development Program under Project No. 20210542MFR at Los Alamos National Laboratory.
About Los Alamos National Laboratory (www.lanl.gov)
Los Alamos National Laboratory, a multidisciplinary research institution working in strategic science on behalf of national security, is administered by Triad, a public service-oriented national security science organization that is equally owned by its three founding members: The Battelle Memorial Institute (Battelle), Texas The A&M University System (TAMUS), and the University of California (UC) Governors of the Department of Energy’s National Nuclear Security Administration.
Los Alamos promotes national security by ensuring the safety and reliability of the US nuclear stockpile, developing technologies to reduce threats from weapons of mass destruction, and resolving issues related to energy, the environment, infrastructure, health, and global security concerns.
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