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Seismic positioning method and equipment based on fusion of machine learning and dynamic computing

A technology of machine learning and positioning methods, applied in the field of earthquake positioning, can solve problems such as limited historical data and inaccurate source locations

Active Publication Date: 2022-04-12
PEKING UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to the inaccurate location of the epicenter in the historical data and the limited amount of historical data, the positioning results obtained by the trained positioning model also have limitations.

Method used

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  • Seismic positioning method and equipment based on fusion of machine learning and dynamic computing
  • Seismic positioning method and equipment based on fusion of machine learning and dynamic computing
  • Seismic positioning method and equipment based on fusion of machine learning and dynamic computing

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Experimental program
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Effect test

Embodiment 1

[0057] This embodiment implements an earthquake location method based on the fusion of machine learning and dynamic calculation, such as figure 1 shown, including the following steps:

[0058] S1. Acquire multi-source data, and perform regularization processing on the multi-source data;

[0059] S2. Perform short-time Fourier transform on the regularized data to obtain a time-frequency image as a sample, generate label data for each sample according to the three-dimensional Gaussian distribution according to the source position, and use the cross-validation method to generate a training set and a test set;

[0060] S3. Perform full convolutional neural network model training based on the training set, and evaluate the accuracy of the trained model based on the test set to obtain a trained model;

[0061] S4. Input the real-time monitoring data into the trained model to preliminarily estimate the source position;

[0062] S5. Correct the preliminary estimated hypocenter posit...

Embodiment 2

[0086] This embodiment implements an earthquake location method based on machine learning and dynamic calculation fusion, including the following steps:

[0087] Obtain multi-source data, and perform regularization processing on the multi-source data;

[0088] Perform short-time Fourier transform on the regularized data to obtain time-frequency images as samples, generate label data for each sample according to the three-dimensional Gaussian distribution according to the source position, and use cross-validation method to generate training set and test set;

[0089] The full convolutional neural network model is trained based on the training set, and the accuracy of the trained model is evaluated based on the test set to obtain the trained model;

[0090] Input the real-time monitoring data into the trained model to preliminarily estimate the source location;

[0091] The initial estimation of the hypocentral position is corrected by the dynamic calculation model.

[0092] T...

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Abstract

The present application relates to the technical field of earthquake positioning, and more specifically, the present application relates to an earthquake positioning method and equipment based on fusion of machine learning and dynamic calculation. The method includes: acquiring multi-source data, and performing regularization processing on the multi-source data; performing short-time Fourier transform on the regularized data to obtain a time-frequency image as a sample, and for each sample according to the source position Generate label data according to the three-dimensional Gaussian distribution, and use the cross-validation method to generate training sets and test sets; conduct full convolutional neural network model training based on the training set, and evaluate the accuracy of the trained model based on the test set to obtain the trained model; real-time The monitoring data is input into the trained model to preliminarily estimate the hypocenter location; the preliminarily estimated hypocenter location is corrected by using the dynamic calculation model. The method and device can quickly realize the preliminary location of the seismic source, and then use the dynamic calculation model to further determine the precise location of the seismic source.

Description

technical field [0001] The present application relates to the technical field of earthquake positioning, and more specifically, the present application relates to an earthquake positioning method and equipment based on fusion of machine learning and dynamic calculation. Background technique [0002] my country is a country with frequent earthquakes, and earthquakes are extremely destructive natural disasters, which pose a great threat to people's lives and property safety. Earthquakes caused by geological structures transmit energy outward through seismic waves, causing deformation of the ground surface and destruction of landmark building structures, and causing a series of secondary disasters. When an earthquake occurs, quickly and accurately determining the location of the earthquake source will help improve the timeliness and reliability of earthquake early warning. [0003] Due to the complexity of the earthquake scene, although great achievements have been made in ear...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01V1/30G06N3/04G06N3/08
CPCG01V1/307G06N3/04G06N3/08G01V2210/65
Inventor 陈永强吴志鹏周惟於
Owner PEKING UNIV
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