In this paper, a template matching and location method, which has been rapidly adopted in microseismic research in recent years, is applied to laboratory acoustic emission(AE) monitoring. First, we used traditional me...In this paper, a template matching and location method, which has been rapidly adopted in microseismic research in recent years, is applied to laboratory acoustic emission(AE) monitoring. First, we used traditional methods to detect P-wave first motions and locate AE hypocenters in three dimensions. In addition, we selected events located with sufficient accuracy(normally corresponding AE events of relatively larger energy, showing clear P-wave first motion and a higher signal-to-noise ratio in most channels) as template events. Then, the template events were used to scan and match other poorly located events in triggered event records or weak events in continuous records. Through crosscorrelation of the multi-channel waveforms between the template and the event to be detected, the weak signal was detected and located using a grid-searching algorithm(with the grid centered at the template hypocenter). In order to examine the performance of the approach, we calibrated the proposed method using experimental data of different rocks and different types of experiments. The results show that the proposed method can significantly improve the detection capability and location accuracy, and can be applied to various laboratory and in situ experiments, which use multi-channel AE monitoring with waveforms recorded in either triggering or continuous mode.展开更多
Earthquake detection and location are essential in earthquake studies,which generally consists of two main classes:waveform-based and pick-based methods.To evaluate the ability of two different methods,a graphicsproce...Earthquake detection and location are essential in earthquake studies,which generally consists of two main classes:waveform-based and pick-based methods.To evaluate the ability of two different methods,a graphicsprocessing-unit-based Match&Locate(GPU-M&L)method and a rapid earthquake association and location(REAL)method are applied to continuous seismic data recorded by 24 digital seismic stations from Jiangsu Seismic Network during 2013 for comparison.GPU-M&L is one of waveform-based methods by waveform cross-correlations while REAL is one of pick-based method to associate arrivals of different seismic phases and locate events through counting the number of P and S picks and travel time residuals.Twenty-six templates are selected from the Jiangsu Seismic Network local catalog by using the GPU-M&L.The number of newly detected and located events is about 2.8 times more than those listed in the local catalog.We both utilize a deep-neural-network-based arrival-time picking method called PhaseNet and a shortterm/long-term average(STA/LTA)trigger algorithm for seismic phase detection and picking by applying the REAL.We then refine seismic locations using a least-squares location method(VELEST)and a high-precision relative location method(hypoDD).By applying STA/LTA and PhaseNet,1006 and 1893 events are associated and located,respectively.The newly detected events are mainly clustered and show steeply dipping fault planes.By analyzing the performance of these methods based on long-term continuous seismic data,the detected catalogs by the GPU-M&L and REAL show that the magnitudes of completeness are 1.4 and 0.8,respectively,which are smaller than 2.6 given by the local catalog.Although REAL provides improvement compared with GPU-M&L,REAL is highly dependent on phase detection and picking which is strongly affected by signal-noise ratio(SNR).Stations at southeast of the study region with low SNR may lead to few detections in the same area.展开更多
基金funding support from Grant-in-Aid for Scientific Research(Grant No.19H00722)by Japan Society for the Promotion of Science(JSPS)。
文摘In this paper, a template matching and location method, which has been rapidly adopted in microseismic research in recent years, is applied to laboratory acoustic emission(AE) monitoring. First, we used traditional methods to detect P-wave first motions and locate AE hypocenters in three dimensions. In addition, we selected events located with sufficient accuracy(normally corresponding AE events of relatively larger energy, showing clear P-wave first motion and a higher signal-to-noise ratio in most channels) as template events. Then, the template events were used to scan and match other poorly located events in triggered event records or weak events in continuous records. Through crosscorrelation of the multi-channel waveforms between the template and the event to be detected, the weak signal was detected and located using a grid-searching algorithm(with the grid centered at the template hypocenter). In order to examine the performance of the approach, we calibrated the proposed method using experimental data of different rocks and different types of experiments. The results show that the proposed method can significantly improve the detection capability and location accuracy, and can be applied to various laboratory and in situ experiments, which use multi-channel AE monitoring with waveforms recorded in either triggering or continuous mode.
基金This research is co-supported by National Key R&D Program of China(No.2017YFC1500402)National Natural Science Foundation of China(Nos.41874063 and U1939203)Shanghai Sheshan National Geophysical Observatory(No.2020K02)。
文摘Earthquake detection and location are essential in earthquake studies,which generally consists of two main classes:waveform-based and pick-based methods.To evaluate the ability of two different methods,a graphicsprocessing-unit-based Match&Locate(GPU-M&L)method and a rapid earthquake association and location(REAL)method are applied to continuous seismic data recorded by 24 digital seismic stations from Jiangsu Seismic Network during 2013 for comparison.GPU-M&L is one of waveform-based methods by waveform cross-correlations while REAL is one of pick-based method to associate arrivals of different seismic phases and locate events through counting the number of P and S picks and travel time residuals.Twenty-six templates are selected from the Jiangsu Seismic Network local catalog by using the GPU-M&L.The number of newly detected and located events is about 2.8 times more than those listed in the local catalog.We both utilize a deep-neural-network-based arrival-time picking method called PhaseNet and a shortterm/long-term average(STA/LTA)trigger algorithm for seismic phase detection and picking by applying the REAL.We then refine seismic locations using a least-squares location method(VELEST)and a high-precision relative location method(hypoDD).By applying STA/LTA and PhaseNet,1006 and 1893 events are associated and located,respectively.The newly detected events are mainly clustered and show steeply dipping fault planes.By analyzing the performance of these methods based on long-term continuous seismic data,the detected catalogs by the GPU-M&L and REAL show that the magnitudes of completeness are 1.4 and 0.8,respectively,which are smaller than 2.6 given by the local catalog.Although REAL provides improvement compared with GPU-M&L,REAL is highly dependent on phase detection and picking which is strongly affected by signal-noise ratio(SNR).Stations at southeast of the study region with low SNR may lead to few detections in the same area.
