Fast and accurate P-wave arrival picking significantly affects the performance of earthquake early warning(EEW)systems.Automated P-wave picking algorithms used in EEW have encountered problems of falsely picking up no...Fast and accurate P-wave arrival picking significantly affects the performance of earthquake early warning(EEW)systems.Automated P-wave picking algorithms used in EEW have encountered problems of falsely picking up noise,missing P-waves and inaccurate P-wave arrival estimation.To address these issues,an automatic algorithm based on the convolution neural network(DPick)was developed,and trained with a moderate number of data sets of 17,717 accelerograms.Compared to the widely used approach of the short-term average/long-term average of signal characteristic function(STA/LTA),DPick is 1.6 times less likely to detect noise as a P-wave,and 76 times less likely to miss P-waves.In terms of estimating P-wave arrival time,when the detection task is completed within 1 s,DPick′s detection occurrence is 7.4 times that of STA/LTA in the 0.05 s error band,and 1.6 times when the error band is 0.10 s.This verified that the proposed method has the potential for wide applications in EEW.展开更多
Earthquake early warning(EEW)is discriminated from earthquake prediction by using initial seismic waves to predict the severity of ground motion and issue the warning information to potential affected area.The warning...Earthquake early warning(EEW)is discriminated from earthquake prediction by using initial seismic waves to predict the severity of ground motion and issue the warning information to potential affected area.The warning information is useful to mitigate the disaster and decrease the losses of life and economy.We reviewed the development history of EEW worldwide and summarized the methodologies using in different systems.Some new sensors came and are coming into EEW giving more developing potential to future implementation.The success of earthquake disaster mitigation relies on the cooperation of the whole society.展开更多
Purpose–Using the strong motion data ofK-net in Japan,the continuous magnitude prediction method based on support vector machine(SVM)was studied.Design/methodology/approach–In the range of 0.5–10.0 s after the P-wa...Purpose–Using the strong motion data ofK-net in Japan,the continuous magnitude prediction method based on support vector machine(SVM)was studied.Design/methodology/approach–In the range of 0.5–10.0 s after the P-wave arrival,the prediction time window was established at an interval of 0.5 s.12 P-wave characteristic parameters were selected as the model input parameters to construct the earthquake early warning(EEW)magnitude prediction model(SVM-HRM)for high-speed railway based on SVM.Findings–The magnitude prediction results of the SVM-HRM model were compared with the traditional magnitude prediction model and the high-speed railway EEW current norm.Results show that at the 3.0 s time window,themagnitude prediction error of the SVM-HRMmodel is obviously smaller than that of the traditionalτc method and Pd method.The overestimation of small earthquakes is obviously improved,and the construction of the model is not affected by epicenter distance,so it has generalization performance.For earthquake events with themagnitude range of 3–5,the single station realization rate of the SVM-HRMmodel reaches 95%at 0.5 s after the arrival of P-wave,which is better than the first alarm realization rate norm required by“The TestMethod of EEW andMonitoring Systemfor High-Speed Railway.”For earthquake eventswithmagnitudes ranging from3 to 5,5 to 7 and 7 to 8,the single station realization rate of the SVM-HRM model is at 0.5 s,1.5 s and 0.5 s after the P-wave arrival,respectively,which is better than the realization rate norm of multiple stations.Originality/value–At the latest,1.5 s after the P-wave arrival,the SVM-HRM model can issue the first earthquake alarm that meets the norm of magnitude prediction realization rate,which meets the accuracy and continuity requirements of high-speed railway EEW magnitude prediction.展开更多
It is critical to determine whether a site has potential damage in real-time after an earthquake occurs,which is a challenge in earthquake disaster reduction.Here,we propose a real-time Earthquake Potential Damage pre...It is critical to determine whether a site has potential damage in real-time after an earthquake occurs,which is a challenge in earthquake disaster reduction.Here,we propose a real-time Earthquake Potential Damage predictor(EPDor)based on predicting peak ground velocities(PGVs)of sites.The EPDor is composed of three parts:(1)predicting the magnitude of an earthquake and PGVs of triggered stations based on the machine learning prediction models;(2)predicting the PGVs at distant sites based on the empirical ground motion prediction equation;(3)generating the PGV map through predicting the PGV of each grid point based on an interpolation process of weighted average based on the predicted values in(1)and(2).We apply the EPDor to the 2022 M_(S) 6.9 Menyuan earthquake in Qinghai Province,China to predict its potential damage.Within the initial few seconds after the first station is triggered,the EPDor can determine directly whether there is potential damage for some sites to a certain degree.Hence,we infer that the EPDor has potential application for future earthquakes.Meanwhile,it also has potential in Chinese earthquake early warning system.展开更多
Seismic waves generated by an earthquake can produce dynamic perturbations in the Earth’s gravity field before the direct arrival of P-waves.Observations of these so-called prompt elasto-gravity signals by ground-bas...Seismic waves generated by an earthquake can produce dynamic perturbations in the Earth’s gravity field before the direct arrival of P-waves.Observations of these so-called prompt elasto-gravity signals by ground-based gravimeters and broadband seismometers have been reported for some large events,such as the 2011 M_(W)9.1 Tohoku earthquake.Recent studies have introduced prompt gravity strain signals(PGSSs)as a new type of observable seismic gravity perturbation that can be used to measure the spatial gradient of the perturbed gravity field.Theoretically,these types of signals can be recorded by indevelopment instruments termed gravity strainmeters,although no successful detection has been reported as yet.Herein,we propose an efficient approach for PGSSs based on a multilayered spherical Earth model.We compared the simulated waveforms with analytical solutions obtained from a homogeneous half-space model,which has been used in earlier studies.This comparison indicates that the effect of the Earth’s structural stratification is significant.With the help of the new simulation approach,we also demonstrated how the PGSSs depend on the magnitude of the seismic source.We further conducted synthetic tests estimating earthquake magnitude using gravity strain signals to demonstrate the potential application of this type of signal in earthquake early warning systems.These results provide essential information for future studies on the synthesis and application of earthquake-induced gravity strain signals.展开更多
基金National Natural Science Foundation of China under Grant Nos.51968016 and 5197083806the Guangxi Innovation Driven Development Project(Science and Technology Major Project,Grant No.Guike AA18118008).
文摘Fast and accurate P-wave arrival picking significantly affects the performance of earthquake early warning(EEW)systems.Automated P-wave picking algorithms used in EEW have encountered problems of falsely picking up noise,missing P-waves and inaccurate P-wave arrival estimation.To address these issues,an automatic algorithm based on the convolution neural network(DPick)was developed,and trained with a moderate number of data sets of 17,717 accelerograms.Compared to the widely used approach of the short-term average/long-term average of signal characteristic function(STA/LTA),DPick is 1.6 times less likely to detect noise as a P-wave,and 76 times less likely to miss P-waves.In terms of estimating P-wave arrival time,when the detection task is completed within 1 s,DPick′s detection occurrence is 7.4 times that of STA/LTA in the 0.05 s error band,and 1.6 times when the error band is 0.10 s.This verified that the proposed method has the potential for wide applications in EEW.
基金the National Natural Science Foundation of China(41704056)Seismological Science and Technology Spark Program(XH18056Y)
文摘Earthquake early warning(EEW)is discriminated from earthquake prediction by using initial seismic waves to predict the severity of ground motion and issue the warning information to potential affected area.The warning information is useful to mitigate the disaster and decrease the losses of life and economy.We reviewed the development history of EEW worldwide and summarized the methodologies using in different systems.Some new sensors came and are coming into EEW giving more developing potential to future implementation.The success of earthquake disaster mitigation relies on the cooperation of the whole society.
基金supported by the National Natural Science Foundation of China(U2039209,U1534202,51408564)Natural Science Foundation of Heilongjiang Province(LH2021E119)the National Key Research and Development Program of China(2018YFC1504003).
文摘Purpose–Using the strong motion data ofK-net in Japan,the continuous magnitude prediction method based on support vector machine(SVM)was studied.Design/methodology/approach–In the range of 0.5–10.0 s after the P-wave arrival,the prediction time window was established at an interval of 0.5 s.12 P-wave characteristic parameters were selected as the model input parameters to construct the earthquake early warning(EEW)magnitude prediction model(SVM-HRM)for high-speed railway based on SVM.Findings–The magnitude prediction results of the SVM-HRM model were compared with the traditional magnitude prediction model and the high-speed railway EEW current norm.Results show that at the 3.0 s time window,themagnitude prediction error of the SVM-HRMmodel is obviously smaller than that of the traditionalτc method and Pd method.The overestimation of small earthquakes is obviously improved,and the construction of the model is not affected by epicenter distance,so it has generalization performance.For earthquake events with themagnitude range of 3–5,the single station realization rate of the SVM-HRMmodel reaches 95%at 0.5 s after the arrival of P-wave,which is better than the first alarm realization rate norm required by“The TestMethod of EEW andMonitoring Systemfor High-Speed Railway.”For earthquake eventswithmagnitudes ranging from3 to 5,5 to 7 and 7 to 8,the single station realization rate of the SVM-HRM model is at 0.5 s,1.5 s and 0.5 s after the P-wave arrival,respectively,which is better than the realization rate norm of multiple stations.Originality/value–At the latest,1.5 s after the P-wave arrival,the SVM-HRM model can issue the first earthquake alarm that meets the norm of magnitude prediction realization rate,which meets the accuracy and continuity requirements of high-speed railway EEW magnitude prediction.
基金financially supported by the National Natural Science Foundation of China (U2039209, U1839208, and 51408564)the Natural Science Foundation of Heilongjiang Province (LH2021E119)+1 种基金Spark Program of Earthquake Science (XH23027YB)the National Key Research and Development Program of China (2018YFC1504003).
文摘It is critical to determine whether a site has potential damage in real-time after an earthquake occurs,which is a challenge in earthquake disaster reduction.Here,we propose a real-time Earthquake Potential Damage predictor(EPDor)based on predicting peak ground velocities(PGVs)of sites.The EPDor is composed of three parts:(1)predicting the magnitude of an earthquake and PGVs of triggered stations based on the machine learning prediction models;(2)predicting the PGVs at distant sites based on the empirical ground motion prediction equation;(3)generating the PGV map through predicting the PGV of each grid point based on an interpolation process of weighted average based on the predicted values in(1)and(2).We apply the EPDor to the 2022 M_(S) 6.9 Menyuan earthquake in Qinghai Province,China to predict its potential damage.Within the initial few seconds after the first station is triggered,the EPDor can determine directly whether there is potential damage for some sites to a certain degree.Hence,we infer that the EPDor has potential application for future earthquakes.Meanwhile,it also has potential in Chinese earthquake early warning system.
基金This work was supported by the National Natural Science Foundation of China(Nos.U1901602 and 42204060)Guangdong Provincial Key Laboratory of Geophysical High-Resolution Imaging Technology(No.2022B1212010002)+1 种基金Shenzhen Key Laboratory of Deep Offshore Oil and Gas Exploration Technology(No.ZDSYS20190902093007855)。
文摘Seismic waves generated by an earthquake can produce dynamic perturbations in the Earth’s gravity field before the direct arrival of P-waves.Observations of these so-called prompt elasto-gravity signals by ground-based gravimeters and broadband seismometers have been reported for some large events,such as the 2011 M_(W)9.1 Tohoku earthquake.Recent studies have introduced prompt gravity strain signals(PGSSs)as a new type of observable seismic gravity perturbation that can be used to measure the spatial gradient of the perturbed gravity field.Theoretically,these types of signals can be recorded by indevelopment instruments termed gravity strainmeters,although no successful detection has been reported as yet.Herein,we propose an efficient approach for PGSSs based on a multilayered spherical Earth model.We compared the simulated waveforms with analytical solutions obtained from a homogeneous half-space model,which has been used in earlier studies.This comparison indicates that the effect of the Earth’s structural stratification is significant.With the help of the new simulation approach,we also demonstrated how the PGSSs depend on the magnitude of the seismic source.We further conducted synthetic tests estimating earthquake magnitude using gravity strain signals to demonstrate the potential application of this type of signal in earthquake early warning systems.These results provide essential information for future studies on the synthesis and application of earthquake-induced gravity strain signals.
