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 sout...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 Phase Net,we develop an ML-based earthquake location model called Phase Loc,to conduct real-time monitoring of the local seismicity.The 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 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.Phase Loc 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.展开更多
A brief overview of the state-of-the-art in the field of earthquake study and forecasting is presented in this paper. We analyze the principles of the methods of determining the coordinates of earthquake focuses by me...A brief overview of the state-of-the-art in the field of earthquake study and forecasting is presented in this paper. We analyze the principles of the methods of determining the coordinates of earthquake focuses by means of ground seismic stations. We demonstrate that those methods cannot be used in the system for monitoring of the beginning of the earthquake preparation process (in the network of RNM ASP stations). As we know, the earthquake preparation process is accompanied by spreading noisy seismic-acoustic signals. Theoretically, the system for monitoring of the beginning of the earthquake preparation process is based on the technologies for seismic-acoustic signal processing-Robust Noise Monitoring (RNM). Noise characteristics determined by RNM technologies indicate the beginning of anomalous seismic processes (ASP) and, consequently, the possibility of ASP monitoring. Considering that the seismic-acoustic signal can be represented as the sum of the useful signal and noise, we present the technologies for determining noise characteristics. It is demonstrated in the paper that a change in the estimate of the cross-correlation function between the useful signal and the noise, noise variance and the value of noise correlation determine the beginning of ASP. One RNM ASP station determines the beginning of ASP within a radius of about 500 km. Determining the location of an expected earthquake requires a network of RNM ASP stations. We analyze the results of noise technology-based monitoring of anomalous seismic processes performed from July 2010 to June 2015 on nine seismic-acoustic stations built at the head of 10 m, 200 m, 300 m and 1400 - 5000 m deep wells. Based on the results of the experimental data obtained in the period covering over three years, an intelligent system has been built, which allows for identifying the location of the zone of an earthquake, using the combinations of time of change in the estimate of the correlation function between the useful signal and the noise of the seismic-acoustic information received from different stations 10 - 20 hours before the earthquake. In the long term, the system can be used by seismologists as a tool for determining the location of the zone of an expected earthquake.展开更多
基金the financial support of the National Key R&D Program of China(2021YFC3000701)the China Seismic Experimental Site in Sichuan-Yunnan(CSES-SY)。
文摘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 Phase Net,we develop an ML-based earthquake location model called Phase Loc,to conduct real-time monitoring of the local seismicity.The 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 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.Phase Loc 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.
文摘A brief overview of the state-of-the-art in the field of earthquake study and forecasting is presented in this paper. We analyze the principles of the methods of determining the coordinates of earthquake focuses by means of ground seismic stations. We demonstrate that those methods cannot be used in the system for monitoring of the beginning of the earthquake preparation process (in the network of RNM ASP stations). As we know, the earthquake preparation process is accompanied by spreading noisy seismic-acoustic signals. Theoretically, the system for monitoring of the beginning of the earthquake preparation process is based on the technologies for seismic-acoustic signal processing-Robust Noise Monitoring (RNM). Noise characteristics determined by RNM technologies indicate the beginning of anomalous seismic processes (ASP) and, consequently, the possibility of ASP monitoring. Considering that the seismic-acoustic signal can be represented as the sum of the useful signal and noise, we present the technologies for determining noise characteristics. It is demonstrated in the paper that a change in the estimate of the cross-correlation function between the useful signal and the noise, noise variance and the value of noise correlation determine the beginning of ASP. One RNM ASP station determines the beginning of ASP within a radius of about 500 km. Determining the location of an expected earthquake requires a network of RNM ASP stations. We analyze the results of noise technology-based monitoring of anomalous seismic processes performed from July 2010 to June 2015 on nine seismic-acoustic stations built at the head of 10 m, 200 m, 300 m and 1400 - 5000 m deep wells. Based on the results of the experimental data obtained in the period covering over three years, an intelligent system has been built, which allows for identifying the location of the zone of an earthquake, using the combinations of time of change in the estimate of the correlation function between the useful signal and the noise of the seismic-acoustic information received from different stations 10 - 20 hours before the earthquake. In the long term, the system can be used by seismologists as a tool for determining the location of the zone of an expected earthquake.
