Earthquake positioning method and device based on fusion of machine learning and dynamics calculation
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.
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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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