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
Research and application of big data mining,at present,is a hot issue. This paper briefly introduces the basic ideas of big data research, analyses the necessity of big data application in earthquake precursor observa...Research and application of big data mining,at present,is a hot issue. This paper briefly introduces the basic ideas of big data research, analyses the necessity of big data application in earthquake precursor observation,and probes certain issues and solutions when applying this technology to work in the seismic-related domain. By doing so,we hope it can promote the innovative use of big data in earthquake precursor observation data analysis.展开更多
Digital data of precursors is noted for its high accuracy. Therefore, it is important to extract the high frequency information from the low ones in the digital data of precursors and to discriminate between the trend...Digital data of precursors is noted for its high accuracy. Therefore, it is important to extract the high frequency information from the low ones in the digital data of precursors and to discriminate between the trend anomalies and the short-term anomalies. This paper presents a method to separate the high frequency information from the low ones by using the wavelet transform to analyze the digital data of precursors, and illustrates with examples the train of thoughts of discriminating the short-term anomalies from trend anomalies by using the wavelet transform, thus provide a new effective approach for extracting the short-term and trend anomalies from the digital data of precursors.展开更多
Using the view point of nonlinear science and the method of selecting numerical features of pattern recognition for reference, the physical and numerical features of precursory ground tilt data are synthetically emplo...Using the view point of nonlinear science and the method of selecting numerical features of pattern recognition for reference, the physical and numerical features of precursory ground tilt data are synthetically employed. The dynamic changes of data series are described with the numerical features in multi dimensional space and their distributive relations instead of an unique factor. The relationship between the ground tilt data and earthquake is examined through recognition and classification.展开更多
The geomagnetic data recorded by Kashi and Jinghai observatories in China were analyzed with improved polarization method. We compared the result around 0.01 Hz which is thought to be useful to detect the ULF anomaly ...The geomagnetic data recorded by Kashi and Jinghai observatories in China were analyzed with improved polarization method. We compared the result around 0.01 Hz which is thought to be useful to detect the ULF anomaly with the result around 0.1 Hz which was inferred from the earthquake depth according to the skin effect, and found that 0.1 Hz is more proper to detect the ULF anomaly for both earthquakes studied in this paper.展开更多
The time-frequency analysis and anomaly detection of wavelet transformation make the method irresistibly advan- tageous in non-stable signal processing. In the paper, the two characteristics are analyzed and demonstr...The time-frequency analysis and anomaly detection of wavelet transformation make the method irresistibly advan- tageous in non-stable signal processing. In the paper, the two characteristics are analyzed and demonstrated with synthetic signal. By applying wavelet transformation to deformation data processing, we find that about 4 months before strong earthquakes, several deformation stations near the epicenter received at the same time the abnormal signal with the same frequency and the period from several days to more than ten days. The GPS observation sta- tions near the epicenter all received the abnormal signal whose period is from 3 months to half a year. These ab- normal signals are possibly earthquake precursors.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sponsored by the Earthquake Monitoring Special Project of "Precursor Observation Data Mining",Key Laboratory of Crustal Dynamics,Institute of Crustal Dynamics,China Earthquake Administration
文摘Research and application of big data mining,at present,is a hot issue. This paper briefly introduces the basic ideas of big data research, analyses the necessity of big data application in earthquake precursor observation,and probes certain issues and solutions when applying this technology to work in the seismic-related domain. By doing so,we hope it can promote the innovative use of big data in earthquake precursor observation data analysis.
文摘Digital data of precursors is noted for its high accuracy. Therefore, it is important to extract the high frequency information from the low ones in the digital data of precursors and to discriminate between the trend anomalies and the short-term anomalies. This paper presents a method to separate the high frequency information from the low ones by using the wavelet transform to analyze the digital data of precursors, and illustrates with examples the train of thoughts of discriminating the short-term anomalies from trend anomalies by using the wavelet transform, thus provide a new effective approach for extracting the short-term and trend anomalies from the digital data of precursors.
文摘Using the view point of nonlinear science and the method of selecting numerical features of pattern recognition for reference, the physical and numerical features of precursory ground tilt data are synthetically employed. The dynamic changes of data series are described with the numerical features in multi dimensional space and their distributive relations instead of an unique factor. The relationship between the ground tilt data and earthquake is examined through recognition and classification.
基金financially supported by the Special Project for Earthquake Research(200708033)
文摘The geomagnetic data recorded by Kashi and Jinghai observatories in China were analyzed with improved polarization method. We compared the result around 0.01 Hz which is thought to be useful to detect the ULF anomaly with the result around 0.1 Hz which was inferred from the earthquake depth according to the skin effect, and found that 0.1 Hz is more proper to detect the ULF anomaly for both earthquakes studied in this paper.
基金Joint Seismological Science Foundation of China (604021) and National Natural Science Foundation of China(40074024).
文摘The time-frequency analysis and anomaly detection of wavelet transformation make the method irresistibly advan- tageous in non-stable signal processing. In the paper, the two characteristics are analyzed and demonstrated with synthetic signal. By applying wavelet transformation to deformation data processing, we find that about 4 months before strong earthquakes, several deformation stations near the epicenter received at the same time the abnormal signal with the same frequency and the period from several days to more than ten days. The GPS observation sta- tions near the epicenter all received the abnormal signal whose period is from 3 months to half a year. These ab- normal signals are possibly earthquake precursors.
