Earthquake positioning method and device based on fusion of machine learning and dynamics calculation
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A technology of machine learning and positioning method, applied in the field of earthquake positioning, which can solve the problems of limited amount of historical data and inaccurate location of earthquake source.
Active Publication Date: 2021-09-03
PEKING UNIV
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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.
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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 invention relates to the technical field of earthquake positioning, in particular to an earthquake positioning method and device based on fusion of machine learning and dynamics calculation. The method comprises the following steps: acquiring multi-source data, and carrying out 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, generating label data for each sample according to a seismic source position and three-dimensional Gaussian distribution, and generating a training set and a test set by adopting a cross validation method; performing full convolutional neural network model training based on the training set, and performing precision evaluation on the trained model based on the test set to obtain a trained model; inputting real-time monitoring data into the trained model to preliminarily estimate the position of a seismic source; and correcting the preliminarily estimated position of the seismic source by adopting a dynamic calculation model. According to the method and the equipment, preliminary positioning of the seismic source can be rapidly realized, and then the accurate seismic source position can be further determined by using a dynamic calculation model.
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...
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