Statistical Analysis of the Relationship between AETA Electromagnetic Anomalies and Local Earthquakes

Author:

Guo Qinmeng,Yong Shanshan,Wang Xin’an

Abstract

To verify the relationship between AETA (Acoustic and Electromagnetics to Artificial Intelligence (AI)) electromagnetic anomalies and local earthquakes, we have performed statistical studies on the electromagnetic data observed at AETA station. To ensure the accuracy of statistical results, 20 AETA stations with few data missing and abundant local earthquake events were selected as research objects. A modified PCA method was used to obtain the sequence representing the signal anomaly. Statistical results of superposed epoch analysis have indicated that 80% of AETA stations have significant relationship between electromagnetic anomalies and local earthquakes. These anomalies are more likely to appear before the earthquakes rather than after them. Further, we used Molchan’s error diagram to evaluate the electromagnetic signal anomalies at stations with significant relationships. All area skill scores are greater than 0. The above results have indicated that AETA electromagnetic anomalies contain precursory information and have the potential to improve local earthquake forecasting.

Funder

Shenzhen science &technology

Publisher

MDPI AG

Subject

General Physics and Astronomy

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Clone Selection Algorithm Optimized Support Vector Machine for AETA Geoacoustic Anomaly Detection;Electronics;2023-11-30

2. Multi-Station Collaborative Analysis of Earthquake Precursors Considering Data Missing;2023 6th International Conference on Information Systems and Computer Networks (ISCON);2023-03-03

3. Weekly earthquake prediction in a region of China based on an intensive precursor network AETA;Frontiers in Earth Science;2022-09-28

4. Multi-Station Collaborative Analysis of Impending Seismic Precursor Based on Graph Neural Networks;2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE);2022-04

5. An Earthquake Forecast Model Based on Multi-Station PCA Algorithm;Applied Sciences;2022-03-24

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