Seismic magnitude rapid estimation method based on deep learning feature fusion

A technology of feature fusion and deep learning, applied in the field of earthquake early warning, can solve the problems of lack of integration of deep learning technology and traditional methods, and achieve the effect of improving accuracy

Active Publication Date: 2020-08-14
ZHEJIANG UNIV
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Problems solved by technology

However, the current research on the deep learning application of the seismic magnitude rapid estimation task is not very common, and the traditional

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  • Seismic magnitude rapid estimation method based on deep learning feature fusion
  • Seismic magnitude rapid estimation method based on deep learning feature fusion
  • Seismic magnitude rapid estimation method based on deep learning feature fusion

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Embodiment Construction

[0028] In order to describe the present invention more specifically, the technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0029] Such as figure 1 As shown, the present invention is based on the deep learning feature fusion method for rapid estimation of earthquake magnitude, and the specific steps are as follows:

[0030] (1) Collect continuous waveform records of ground vibration recorded by seismic monitoring stations for rapid estimation of earthquake magnitude. Specifically: from the continuous waveform data recorded by the seismic monitoring station, the P-wave arrival time of a seismic event is taken as the starting point of the interception, and the 3s after the P-wave arrival time of the seismic event is taken as the interception end point, thereby intercepting A seismic event data sample with a length of 3s. The feature dimension of the sample in this embodiment i...

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Abstract

The invention discloses a seismic magnitude rapid estimation method based on deep learning feature fusion. The method comprises the following steps: (1) collecting a large number of seismic monitoringstation ground vibration waveform records and corresponding seismic catalogues; (2) calculating traditional parameter characteristics and statistical characteristics of a data sample; (3) training adepth model, and extracting image features and time sequence features in the data sample; and (4) fusing the features extracted from the data sample to form vector representation, training an XGBoostmodel to obtain a seismic magnitude rapid estimation model, and further utilizing the model to obtain a corresponding seismic magnitude according to seismic waveform record estimation. Research results of traditional seismology are utilized, and are combined with deep learning, a seismic magnitude rapid estimation model is learned by utilizing a large amount of seismic data, and the accuracy of seismic magnitude rapid estimation is improved.

Description

technical field [0001] The invention belongs to the technical field of earthquake early warning, and in particular relates to a method for rapidly estimating earthquake magnitude based on deep learning feature fusion. Background technique [0002] Over the years, geologists have used the continuous waveform data recorded by seismic monitoring stations to conduct a lot of work and research in the fields of earthquake early warning, earthquake quick report, and earthquake mechanism. Among them, tasks such as seismic event detection, automatic seismic phase picking, and rapid magnitude estimation are the focus and hotspots of related research. [0003] The task of rapid earthquake magnitude estimation is very important in earthquake early warning. If an accurate estimate of the magnitude of an earthquake can be given a few seconds before an earthquake occurs, it will be of great help to earthquake early warning and disaster relief work. , for more time. [0004] The research ...

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Application Information

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IPC IPC(8): G01V1/28G01V1/30G06K9/62G06N3/04G06N3/08
CPCG01V1/28G01V1/307G01V1/282G06N3/084G06N3/049G06N3/045G06F18/253
Inventor 潘纲徐逸志赵莎刘杰董霖方毅李石坚
Owner ZHEJIANG UNIV
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