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Academic Journal of Computing & Information Science, 2023, 6(12); doi: 10.25236/AJCIS.2023.061210.

Aftershock predict based on convolution neural networks


Zhijun Li1, Gege Jin2, Hongmei Yin3, Chen Chen4, Jiyong Hua1

Corresponding Author:
Jiyong Hua

1Yangzhou University, Yangzhou, China

2Yangzhou Technician Branch, Jiangsu United Vocational and Technical College,  Yangzhou, China

3Yangzhou Customs, Yangzhou, China

4Jiangsu College of Tourism, Yangzhou, China


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.


Convolution Neural Network; Aftershock Predict; Earthquake Predict

Cite This Paper

Zhijun Li, Gege Jin, Hongmei Yin, Chen Chen, Jiyong Hua. Aftershock predict based on convolution neural networks. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 12: 90-96. https://doi.org/10.25236/AJCIS.2023.061210.


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