A seismic prediction method based on the BiLSTM-ConvGRU model
Earthquake prediction is a highly challenging task in Earth science,but due to the nonlinear and complex spatio-temporal characteristics of earthquake data,traditional prediction methods are difficult to effectively handle.A method combining bidirectional long short-term memory network(BiLSTM)and convolutional gated recurrent unit(ConvGRU)has been proposed for seismic data analysis in central and northern California.This method enhances the modeling capability of the model by capturing spatiotemporal correlations in the data.The experimental results show that the BiLSTM ConvGRU model exhibits significant advan-tages in evaluation metrics such as MSE and PSNR,and has broad application prospects.
seismic predictionBiLSTMConvGRUcentral and northern California
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