Aftershock Predict based on Convolution Neural Networks
Earthquake prediction is a difficult task. Constrained within a certain spatiotemporal range, earthquakes are only a probability event. In a large area, predicting earthquakes based on geographical events that have already occurred is reliable. Predicting the duration of aftershocks under the condition that a major earthquake has already occurred is the research content of this article. Extract 6 features from seismic phase data to predict the aftershock period. We constructed a convolutional neural network model, sorted out 855 data from 1351 data, and trained the network. The accuracy of training verification reaches 90%, and the accuracy of testing reaches 100%. After further refinement, this model can be used to predict the duration of aftershocks in earthquakes. Provide data guidance for earthquake rescue.
