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Knowledge Science, Engineering and Management

Volume 5914 of the series Lecture Notes in Computer Science pp 285-296

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

  • Yaxin BiAffiliated withSchool of Computing and Mathematics, University of Ulster
  • , Shengli WuAffiliated withSchool of Computing and Mathematics, University of Ulster
  • , Pan XiongAffiliated withInstitute of Earthquake Science, China Earthquake Administration
  • , Xuhui ShenAffiliated withInstitute of Earthquake Science, China Earthquake Administration

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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.