Waveforms of seismic events,extracted from January 2019 to December 2021 were used to construct a test dataset to investigate the generalizability of PhaseNet in the Shandong region.The results show that errors in the...Waveforms of seismic events,extracted from January 2019 to December 2021 were used to construct a test dataset to investigate the generalizability of PhaseNet in the Shandong region.The results show that errors in the picking of seismic phases(P-and Swaves)had a broadly normal distribution,mainly concentrated in the ranges of−0.4–0.3 s and−0.4–0.8 s,respectively.These results were compared with those published in the original PhaseNet article and were found to be approximately 0.2–0.4 s larger.PhaseNet had a strong generalizability for P-and S-wave picking for epicentral distances of less than 120 km and 110 km,respectively.However,the phase recall rate decreased rapidly when these distances were exceeded.Furthermore,the generalizability of PhaseNet was essentially unaffected by magnitude.The M4.1 earthquake sequence in Changqing,Shandong province,China,that occurred on February 18,2020,was adopted as a case study.PhaseNet detected more than twice the number of earthquakes in the manually obtained catalog.This further verified that PhaseNet has strong generalizability in the Shandong region,and a high-precision earthquake catalog was constructed.According to these precise positioning results,two earthquake sequences occurred in the study area,and the southern cluster may have been triggered by the northern cluster.The focal mechanism solution,regional stress field,and the location results of the northern earthquake sequence indicated that the seismic force of the earthquake was consistent with the regional stress field.展开更多
PhaseNet and EQTransformer are two state-of-the-art earthquake detection methods that have been increasingly applied worldwide.To evaluate the generaliz-ation ability of the two models and provide insights for the dev...PhaseNet and EQTransformer are two state-of-the-art earthquake detection methods that have been increasingly applied worldwide.To evaluate the generaliz-ation ability of the two models and provide insights for the development of new models,this study took the sequences of the Yunnan Yangbi M6.4 earthquake and Qinghai Maduo M7.4 earthquake as examples to compare the earthquake detection effects of the two abovementioned models as well as their abilities to process dense seismic sequences.It has been demonstrated from the corresponding research that due to the differences in seismic waveforms found in different geographical regions,the picking performance is reduced when the two models are applied directly to the detection of the Yangbi and Maduo earthquakes.PhaseNet has a higher recall than EQTransformer,but the recall of both models is reduced by 13%-56%when compared with the results rep-orted in the original papers.The analysis results indicate that neural networks with deeper layers and complex structures may not necessarily enhance earthquake detection perfor-mance.In designing earthquake detection models,attention should be paid to not only the balance of depth,width,and architecture but also to the quality and quantity of the training datasets.In addition,noise datasets should be incorporated during training.According to the continuous waveforms detected 21 days before the Yangbi and Maduo earthquakes,the Yangbi earthquake exhibited foreshock,while the Maduo earthquake showed no foreshock activity,indicating that the two earthquakes’nucleation processes were different.展开更多
基金funded by the General Scientific Research Project of the Shandong Earthquake Agency(No.YB2202)the National Key Research and Development Program Project(No.2021YFC3000700)a Key Project under the Natural Science Foundation of Shandong Province(No.ZR2020KF003).
文摘Waveforms of seismic events,extracted from January 2019 to December 2021 were used to construct a test dataset to investigate the generalizability of PhaseNet in the Shandong region.The results show that errors in the picking of seismic phases(P-and Swaves)had a broadly normal distribution,mainly concentrated in the ranges of−0.4–0.3 s and−0.4–0.8 s,respectively.These results were compared with those published in the original PhaseNet article and were found to be approximately 0.2–0.4 s larger.PhaseNet had a strong generalizability for P-and S-wave picking for epicentral distances of less than 120 km and 110 km,respectively.However,the phase recall rate decreased rapidly when these distances were exceeded.Furthermore,the generalizability of PhaseNet was essentially unaffected by magnitude.The M4.1 earthquake sequence in Changqing,Shandong province,China,that occurred on February 18,2020,was adopted as a case study.PhaseNet detected more than twice the number of earthquakes in the manually obtained catalog.This further verified that PhaseNet has strong generalizability in the Shandong region,and a high-precision earthquake catalog was constructed.According to these precise positioning results,two earthquake sequences occurred in the study area,and the southern cluster may have been triggered by the northern cluster.The focal mechanism solution,regional stress field,and the location results of the northern earthquake sequence indicated that the seismic force of the earthquake was consistent with the regional stress field.
基金funded by the National Key R&D Program of China(No.2021YFC3000702)the National Natural Science Foundation of China(No.41774067)the Fundamental Research Funds for the Institute of Geophysics,China Earthquake Administration(Nos.DQ JB21Z05,DQJB20X07).
文摘PhaseNet and EQTransformer are two state-of-the-art earthquake detection methods that have been increasingly applied worldwide.To evaluate the generaliz-ation ability of the two models and provide insights for the development of new models,this study took the sequences of the Yunnan Yangbi M6.4 earthquake and Qinghai Maduo M7.4 earthquake as examples to compare the earthquake detection effects of the two abovementioned models as well as their abilities to process dense seismic sequences.It has been demonstrated from the corresponding research that due to the differences in seismic waveforms found in different geographical regions,the picking performance is reduced when the two models are applied directly to the detection of the Yangbi and Maduo earthquakes.PhaseNet has a higher recall than EQTransformer,but the recall of both models is reduced by 13%-56%when compared with the results rep-orted in the original papers.The analysis results indicate that neural networks with deeper layers and complex structures may not necessarily enhance earthquake detection perfor-mance.In designing earthquake detection models,attention should be paid to not only the balance of depth,width,and architecture but also to the quality and quantity of the training datasets.In addition,noise datasets should be incorporated during training.According to the continuous waveforms detected 21 days before the Yangbi and Maduo earthquakes,the Yangbi earthquake exhibited foreshock,while the Maduo earthquake showed no foreshock activity,indicating that the two earthquakes’nucleation processes were different.
