A Wavelet-Based Method for Detecting Seismic Anomalies in Remote Sensing Satellite Data

Pan Xiong, Yaxin Bi, Xuhui Shen

Research output: Chapter in Book/Report/Conference proceedingChapter

9 Citations (Scopus)

Abstract

In this paper we present a comparative analysis of two types of remote sensing satellite data by using the wavelet-based data mining techniques. The analyzed results reveal that the anomalous variations exist related to the earthquakes. The methods studied in this work include wavelet transformations and spatial/temporal continuity analysis of wavelet maxima. These methods have been used to analyze the singularities of seismic anomalies in remote sensing satellite data, which are associated with the two earthquakes of Wenchuan and Pure recently occurred in China.
Original languageEnglish
Title of host publicationMachine Learning and Data Mining in Pattern Recognition Lecture Notes in Computer Science
PublisherSpringer
Pages569-581
ISBN (Print)978-3-642-03069-7
Publication statusPublished - 2009

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    Xiong, P., Bi, Y., & Shen, X. (2009). A Wavelet-Based Method for Detecting Seismic Anomalies in Remote Sensing Satellite Data. In Machine Learning and Data Mining in Pattern Recognition Lecture Notes in Computer Science (pp. 569-581). Springer.