Statistical and Comparative Analysis of Multi-Channel Infrared Anomalies before Earthquakes in China and the Surrounding Area

Abstract

Abundant infrared remote sensing images and advanced information processing technologies are used to predict earthquakes. However, most studies only use single long-wave infrared data or its products, and the accuracy of prediction is not high enough. To solve this problem, this paper proposes a statistical method based on connected domain recognition to analyze multi-channel anomalies. We extract pre-seismic anomalies from multi-channel infrared remote sensing images using the relative power spectrum, then calculate positive predictive values, true positive rates and probability gains in different channels. The results show that the probability gain of the single-channel prediction method is extremely low. The positive predictive value of four-channel anomalies is 41.94%, which is higher than that of single-channel anomalies with the same distance threshold of 200 km. The probability gain of the multi-channel method is 2.38, while that of the single-channel method using the data of any channel is no more than 1.26. This study shows the advantages of the multi-channel method to predict earthquakes and indicates that it is feasible to use multi-channel infrared remote sensing images to improve the accuracy of earthquake prediction.