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

Abstract

Monitoring seismicity in real time provides significant benefits for timely earthquake warning and analyses. In this study, we propose an automatic workflow based on machine learning (ML) to monitor seismicity in the southern Sichuan Basin of China. This workflow includes coherent event detection, phase picking, and earthquake location using three-component data from a seismic network. By combining PhaseNet, we develop an ML-based earthquake location model called PhaseLoc, to conduct real-time monitoring of the local seismicity. The approach allows us to use synthetic samples covering the entire study area to train PhaseLoc, addressing the problems of insufficient data samples, imbalanced data distribution, and unreliable labels when training with observed data. We apply the trained model to observed data recorded in the southern Sichuan Basin, China, between September 2018 and March 2019. The 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 catalog. PhaseLoc combines all available phase information to make fast and reliable predictions, even if only a few phases are detected and picked. The proposed workflow may help real-time seismic monitoring in other regions as well.

Keywords

• Earthquake monitoring
• Machine learning
• Local seismicity
• Gaussian waveform
• Sparse stations