Earthquake anomaly intelligent identification method based on Zhang Heng No.1 satellite multiple parameters

The Zhangheng-1 satellite-based method integrates CNN and Bi-LSTM with transfer learning to enhance earthquake anomaly detection, addressing large-area data coverage issues and improving prediction accuracy and timeliness.

CN119199957APending Publication Date: 2024-12-27INST OF EARTHQUAKE SCI CHINA EARTHQUAKE ADMINISTATION
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Patent Information

Application Number
CN202411406151.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-10-10
Publication Date
2024-12-27

AI Technical Summary

Technical Problem

Existing earthquake monitoring methods face challenges in accurately and efficiently identifying ionospheric anomalies using ground-based equipment, which are insufficient for large-area, dynamic, and continuous data coverage.

Method used

An intelligent earthquake anomaly recognition method utilizing multi-parameter data from the Zhangheng-1 satellite, combining convolutional neural networks (CNN) and bidirectional long short-term memory networks (Bi-LSTM) with transfer learning, to process and analyze ionospheric data for precise earthquake precursor identification.

Benefits of technology

The method significantly enhances the accuracy and timeliness of earthquake anomaly detection, enabling early warnings and improving the reliability of earthquake prediction systems, with broad applicability across different regions and magnitudes.

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Abstract

The invention discloses a seismic anomaly intelligent identification method based on Zhang Heng No.1 satellite multiple parameters. The method comprises the following steps: step 1, obtaining and preprocessing multi-parameter satellite data; step 2, extracting pre-earthquake ionosphere disturbance characteristics based on a deep learning model and analyzing the pre-earthquake ionosphere disturbance characteristics; 3, earthquake abnormal information is extracted and intelligently recognized; and step 4, optimizing the model and evaluating earthquake prediction performance. According to the method, ionosphere and other parameter data provided by Zhang Zheng No.1 satellites are acquired, a convolutional neural network and a bidirectional long-short-term memory network (Bi-LSTM) are combined, and pre-earthquake ionosphere disturbance anomaly is identified by using a deep learning technology. Compared with the prior art, the method has the advantages that by integrating the multi-parameter remote sensing data and the deep learning model, the recognition precision of the earthquake abnormal signal before the earthquake is effectively improved; and in combination with a transfer learning technology, the recognition effect under the condition of less training data is further improved.
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