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<front>
<journal-meta>
<journal-id journal-id-type="publisher">global-journal-of-science-frontier-research-h-environment-environmental-geology</journal-id>
<journal-title-group>
<journal-title>Global Journal of Science Frontier Research - H: Environment &amp; Environmental geology</journal-title>
</journal-title-group>
<issn publication-format="print">0975-5896</issn>
<issn publication-format="electronic">2249-4626</issn>
<publisher><publisher-name>Global Journals Publishing Group Incorporated</publisher-name></publisher>
<self-uri xlink:href="https://globaljournals.org/journal-seo-export/jats/58342.xml" />
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<article-meta>
<article-id pub-id-type="publisher-id">58342</article-id>
<title-group>
<article-title>Aftershock Predict based on Convolution Neural Networks</article-title>
<subtitle>Predicting Aftershock Duration via Neural Networks</subtitle>
</title-group>
<contrib-group>
<contrib contrib-type="author"><name><surname>Hua</surname><given-names>Jiyong</given-names></name></contrib>
<contrib contrib-type="author"><name><surname>Li</surname><given-names>Zhi Jun</given-names></name></contrib>
<contrib contrib-type="author"><name><surname>Jin</surname><given-names>Gege</given-names></name></contrib>
<contrib contrib-type="author"><name><surname>Yin</surname><given-names>Hongmei</given-names></name></contrib>
</contrib-group>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2023-12-13">
<day>13</day>
<month>12</month>
<year>2023</year>
</pub-date>
<volume>23</volume>
<issue>H6</issue>
<fpage>45</fpage>
<lpage>51</lpage>
<abstract><p>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.</p></abstract>
<kwd-group kwd-group-type="author-generated">
<kwd>convolution neural network; aftershock predict; earthquake predict</kwd>
</kwd-group>
<self-uri content-type="pdf" xlink:href="https://globaljournals.org/GJSFR_Volume23/4-Aftershock-Predict-based.pdf" />
<self-uri content-type="html" xlink:href="https://globaljournals.org/scholarly-articles/aftershock-predict-based-on-convolution-neural-networks/" />
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<title>Full Text</title>
<p>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.</p>
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</article>