A Comparative Analysis for Detecting Seismic Anomalies in Data Sequences of Outgoing Longwave Radiation

Yaxin Bi, Shengli Wu, Xiong Pan, Shen Xuhui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

In this paper we propose to use wavelet transformations as a data mining tool to detect seismic anomalies within data sequences of outgoing longwave radiation (OLR). The distinguishing feature of our method is that we calculate the wavelet maxima curves that propagate from coarser to finer scales in the defined grids over time and then identify strong singularities from the maxima lines distributing on the grids by only accounting for the characteristics of continuity in both time and space. The identified singularities are further examined by the Holder exponent to determine whether the identified singularities can be regarded as potential precursors prior to earthquakes. This method has been applied to analyze OLR data associated with an earthquake recently occurred in Wenchuan of China. Combining with the tectonic explanation of spatial and temporal continuity of the abnormal phenomena, the analyzing results reveal that the identified singularities could be viewed as the seismic anomalies prior to the Wuchan earthquake.
Original languageEnglish
Title of host publicationUnknown Host Publication
Pages285-296
Number of pages11
DOIs
Publication statusPublished - 17 Nov 2009
Event3rd International Conference on Knowledge Science, Engineering and Management - Vienna
Duration: 17 Nov 2009 → …

Conference

Conference3rd International Conference on Knowledge Science, Engineering and Management
Period17/11/09 → …

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