Pre-seismic Anomalies Detection with Frequent Itemsets from Remote Sensing Satellite Data

doi:10.3969/j.issn.1006-2475.2016.08.004

Abstract

Abstract: Existing researches showed that seismic activity area remote sensing data was anomalous before an earthquake. In this paper, we proposed a method of data mining that could analyze the hyper-spectrum data from AIRS (atmospheric infrared detector) from January 2006 to December 2013. The main idea of this method was that we mined frequent itemsets of pre-seismic data and regarded them as pre-seismic anomalies, then we used the model to predict earthquakes. At the same time, we carried out reverse experiments using non-seismic data to verify this method. Main experimental results revealed that the AIRS data can be used to predict earthquakes. Among all experiments, the best prediction effect comes out when we set precursor time as 30 days and precursor region as square with center being epicenter and side length being 2°. The prediction accuracy is 81.8% and false positive is 5.6%.

Key words: data mining, earthquake prediction, infrared remote sensing, frequent itemsets

CLC Number:

TP391.9

LI Wen, XU Hui. Pre-seismic Anomalies Detection with Frequent Itemsets from Remote Sensing Satellite Data[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2016.08.004.

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