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.
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Bi, Y., Wu, S., Xiong, P., Shen, X. (2009). A Comparative Analysis for Detecting Seismic Anomalies in Data Sequences of Outgoing Longwave Radiation. In: Karagiannis, D., Jin, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2009. Lecture Notes in Computer Science(), vol 5914. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10488-6_29
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DOI: https://doi.org/10.1007/978-3-642-10488-6_29
Publisher Name: Springer, Berlin, Heidelberg
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