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An envelope-based machine learning workflow for locating earthquakes in the southern Sichuan Basin

Earthquake Research Advances 2024年第2期4卷 45-54页

作者： Kang Wang Jie Zhang Ji Zhang Zhangyu Wang Ziyu Li School of Earth and Space Sciences University of Science and Technology of ChinaHefei230026China

The development of machine learning technology enables more robust real-time earthquake monitoring through automated implementations. However, the application of machine learning to earthquake location problems faces challenges in regions with limited available training data. To address the issues of sparse event distribution and inaccurate ground truth in historical seismic datasets, we expand the training dataset by using a large number of synthetic envelopes that closely resemble real data and build an earthquake location model named ENVloc. We propose an envelope-based machine learning workflow for simultaneously determining earthquake location and origin time. The method eliminates the need for phase picking and avoids the accumulation of location errors resulting from inaccurate picking results. In practical application, ENVloc is applied to several data intercepted at different starting points. We take the starting point of the time window corresponding to the highest prediction probability value as the origin time and save the predicted result as the earthquake location. We apply ENVloc to observed data acquired in the southern Sichuan Basin, China, between September 2018 and March 2019. The results show that the average difference with the catalog in latitude, longitude, depth, and origin time is 0.02°,0.02°, 2 km, and 1.25 s, respectively. These suggest that our envelope-based method provides an efficient and robust way to locate earthquakes without phase picking, and can be used in earthquake monitoring in near-real time.

关键词： Waveform envelope Earthquake location local seismicity Synthetic data Sparse stations

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Monitoring seismicity in the southern Sichuan Basin using a machine learning workflow

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Earthquake Research Advances 2024年第1期4卷 59-66页

作者： Kang Wang Jie Zhang Ji Zhang Zhangyu Wang Huiyu Zhu School of Earth and Space Sciences University of Science and Technology of ChinaHefei230026China

Monitoring seismicity in real time provides significant benefits for timely earthquake warning and *** this study,we propose an automatic workflow based on machine learning(ML)to monitor seismicity in the southern Sichuan Basin of *** workflow includes coherent event detection,phase picking,and earthquake location using three-component data from a seismic *** combining Phase Net,we develop an ML-based earthquake location model called Phase Loc,to conduct real-time monitoring of the local *** approach allows us to use synthetic samples covering the entire study area to train Phase Loc,addressing the problems of insufficient data samples,imbalanced data distribution,and unreliable labels when training with observed *** apply the trained model to observed data recorded in the southern Sichuan Basin,China,between September 2018 and March *** results show that the average differences in latitude,longitude,and depth are 5.7 km,6.1 km,and 2 km,respectively,compared to the reference *** Loc combines all available phase information to make fast and reliable predictions,even if only a few phases are detected and *** proposed workflow may help real-time seismic monitoring in other regions as well.

关键词： Earthquake monitoring Machine learning local seismicity Gaussian waveform Sparse stations

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