A Pre-Seismic Anomaly Detection Approach Based on Earthquake Cross Partial Multi-View Data Fusion

Abstract

It is a challenge to detect pre-seismic anomalies by using only one dataset due to the complexity of earthquakes. Therefore, it is a promising direction to use multiparameteric data. The earthquake cross partial multi-view data fusion approach (EQ-CPM) is proposed in this paper. By using this method, electromagnetic data and seismicity indicators are fused. This approach tolerates the absence of data and complements the missing part in fusion. First, the effectiveness of seismicity indicators and electromagnetic data was validated through two earthquake case studies. Then, four machine learning algorithms were applied to detect pre-seismic anomalies by using the fused data and two original datasets. The results show that the fused data provided better performance than the single-modal data. In the Matthews correlation coefficient index, the results of our method showed an 8% improvement compared with the latest study.

A pre-seismic anomaly detection approach based on graph attention isomorphism network

“…Electromagnetic data collected by seismic stations often have missing values, the reason mainly includes instrument failure, communication issues, data storage problems, and environmental factors. To solve this issue, we propose an MVL-based approach in [31], which aims to address the absence of data by incorporating a fusion technique to complement the missing information. Furthermore, by using SMOTE method [32], the MVL-based approach helps to overcome the class imbalance problem.…”

“…After feature fusion, the distance between positive and negative samples will be larger. In this paper, we use the MVL-based approach in [31] to complete the feature fusion task.…”