Earthquake precursor data have been used as an important basis for earthquake prediction.In this study,a recurrent neural network(RNN)architecture with long short-term memory(LSTM)units is utilized to develop a predic...Earthquake precursor data have been used as an important basis for earthquake prediction.In this study,a recurrent neural network(RNN)architecture with long short-term memory(LSTM)units is utilized to develop a predictive model for normal data.Furthermore,the prediction errors from the predictive models are used to indicate normal or abnormal behavior.An additional advantage of using the LSTM networks is that the earthquake precursor data can be directly fed into the network without any elaborate preprocessing as required by other approaches.Furthermore,no prior information on abnormal data is needed by these networks as they are trained only using normal data.Experiments using three groups of real data were conducted to compare the anomaly detection results of the proposed method with those of manual recognition.The comparison results indicated that the proposed LSTM network achieves promising results and is viable for detecting anomalies in earthquake precursor data.展开更多
A way to detect the seismic precursor in granular medium is described and a model of propagation for precursive stress-strain signals is proposed.A strain sensor buried in a sandpit is used to measure a seismic precur...A way to detect the seismic precursor in granular medium is described and a model of propagation for precursive stress-strain signals is proposed.A strain sensor buried in a sandpit is used to measure a seismic precursor signal.The signal has been investigated and confirmed to originate from a specific earthquake.A comparison of simulated and experimental signals indicates that the signal results from the strain in the earth's strata.Based on the behavioral characteristics of granular materials,an analysis of why this method can be so sensitive to the seismic strain signal is undertaken and a model for the propagation of this stress-strain signal is proposed.The Earth's lithosphere is formed of tectonic plates,faults and fault gouges at their boundaries.In the case of the quasi-static mechanics of seismic precursory stress-strain propagation,the crustal lithosphere should be treated as a large-scale granular system.During a seismogenic event,accumulated force generates the stick-slip motion of adjacent tectonic plates and incrementally pushes blocks farther apart through stick-slip shift.The shear force released through this plate displacement causes soil compression deformation.The discrete properties of the sand in the sandpit lead to the sensitive response of the sensor to the deformation signal which enables it to detect the seismic precursor.From the analysis of the mechanism of the stress-strain propagation in the lithosphere,an explanation is found for the lack of signal detection by sensors installed in rocks.The principles and method presented in this paper provide a new technique for investigating seismic precursors to shallow-source earthquakes.展开更多
基金supported by the Science for Earthquake Resilience of China(No.XH18027)Research and Development of Comprehensive Geophysical Field Observing Instrument in China's Mainland(No.Y201703)Research Fund Project of Shandong Earthquake Agency(Nos.JJ1505Y and JJ1602)
文摘Earthquake precursor data have been used as an important basis for earthquake prediction.In this study,a recurrent neural network(RNN)architecture with long short-term memory(LSTM)units is utilized to develop a predictive model for normal data.Furthermore,the prediction errors from the predictive models are used to indicate normal or abnormal behavior.An additional advantage of using the LSTM networks is that the earthquake precursor data can be directly fed into the network without any elaborate preprocessing as required by other approaches.Furthermore,no prior information on abnormal data is needed by these networks as they are trained only using normal data.Experiments using three groups of real data were conducted to compare the anomaly detection results of the proposed method with those of manual recognition.The comparison results indicated that the proposed LSTM network achieves promising results and is viable for detecting anomalies in earthquake precursor data.
基金supported by the Knowledge Innovation Project of the Chinese Academy of Sciences(KJCX2-SW-W15,KKCX1-YW-03)the National Natural Science Foundation of China(10374111)
文摘A way to detect the seismic precursor in granular medium is described and a model of propagation for precursive stress-strain signals is proposed.A strain sensor buried in a sandpit is used to measure a seismic precursor signal.The signal has been investigated and confirmed to originate from a specific earthquake.A comparison of simulated and experimental signals indicates that the signal results from the strain in the earth's strata.Based on the behavioral characteristics of granular materials,an analysis of why this method can be so sensitive to the seismic strain signal is undertaken and a model for the propagation of this stress-strain signal is proposed.The Earth's lithosphere is formed of tectonic plates,faults and fault gouges at their boundaries.In the case of the quasi-static mechanics of seismic precursory stress-strain propagation,the crustal lithosphere should be treated as a large-scale granular system.During a seismogenic event,accumulated force generates the stick-slip motion of adjacent tectonic plates and incrementally pushes blocks farther apart through stick-slip shift.The shear force released through this plate displacement causes soil compression deformation.The discrete properties of the sand in the sandpit lead to the sensitive response of the sensor to the deformation signal which enables it to detect the seismic precursor.From the analysis of the mechanism of the stress-strain propagation in the lithosphere,an explanation is found for the lack of signal detection by sensors installed in rocks.The principles and method presented in this paper provide a new technique for investigating seismic precursors to shallow-source earthquakes.
