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Seismic wave amplitude prediction method based on deep learning

A prediction method and deep learning technology, applied in the field of deep learning, can solve problems such as seismic waves not being found, and achieve the effect of strong generalization ability

Inactive Publication Date: 2021-05-14
NORTHEASTERN UNIV
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Problems solved by technology

But for the estimation of the maximum amplitude in the seismic wave, no better method has been found before

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  • Seismic wave amplitude prediction method based on deep learning
  • Seismic wave amplitude prediction method based on deep learning

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Embodiment Construction

[0018] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0019] In this embodiment, the WINDOWS10 system is used as the development environment, combined with Anaconda+pytorch+NVIDIA1060Ti+Pycharm IDE, and the seismic wave amplitude prediction method based on deep learning of the present invention is used to predict the maximum amplitude of a certain seismic waveform.

[0020] A seismic wave amplitude prediction method based on deep learning, based on the ENZ three-component seismic waveform data recorded by the seismic station, select any one of the components, and obtain the first arrival time of the seismic wave P wave according to the STA / LTA algorithm and the AIC algorithm, and then from The first five seconds of seismic wa...

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Abstract

The invention provides a seismic wave amplitude prediction method based on deep learning and relates to the technical field of deep learning. According to the method, any component is selected based on ENZ three-component seismic waveform data recorded by a seismic station, a seismic wave P wave first arrival moment is obtained according to an STA / LTA algorithm and an AIC algorithm, and then seismic waveform data of the first five seconds are intercepted from the seismic wave P wave first arrival moment to serve as training data; after training data is preprocessed, feature extraction is carried out through a CNN network, finally, regression analysis is carried out on extracted features in an LSTM supervised learning mode, and then the maximum amplitude of subsequent seismic waveforms is predicted. The method has high generalization ability, the situation that errors become large due to waveform changes is avoided, and a large amount of time can be reserved for work such as early warning and rapid reporting.

Description

technical field [0001] The invention relates to the technical field of deep learning, in particular to a method for predicting seismic wave amplitude based on deep learning. Background technique [0002] Earthquake is a huge destructive and unpredictable natural disaster. Once it occurs, it will pose a huge threat to the safety of people's lives and property. Therefore, scientists have been working on earthquake early warning, earthquake property classification, and magnitude estimation. The amplitude of the seismic wave is one of the dynamic properties of the seismic wave. The study of the amplitude of the seismic wave is of great significance in earthquake research, especially the maximum amplitude of the seismic waveform is a very important parameter in various studies. The maximum amplitude of the seismic waveform is Contains a large amount of earthquake information, such as magnitude, intensity and other information closely related to human production and life. However...

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Application Information

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IPC IPC(8): G01V1/30G01V1/00G06N3/04G06N3/08
CPCG01V1/307G06N3/049G06N3/084G01V2210/63G06N3/045G01V1/01
Inventor 孟凡春任涛钟志达
Owner NORTHEASTERN UNIV
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