GEOSS-Based Thermal Parameters Analysis for Earthquake Anomaly Recognition

Although earthquakes are difficult to be predicted because of the complexity of the Earth system and the uncertainty of seismogenic processes, GEOSS provides multiple sources of observation data and brings a possibility to extract a thermal anomaly that would have a significant effect on earthquake prediction. Referring to the analysis on the lithosphere-coversphere-atmosphere (LCA) coupling due to stress enhancement in seismogenic zone, six thermal parameters, including surface latent heat flux (SLHF), thermal infrared radiation (TIR), outgoing longwave radiation (OLR), diurnal temperature range (DTR), atmospheric temperature, and skin temperature, are selected for GEOSS-based earthquake anomaly recognition (EAR). The inherent relations between the six thermal parameters are preliminarily introduced in consideration of possible LCA coupling. With overquantity, quasi-synchronism, and geo-consistency being defined as three rules for data mining, a deviation-time-space-thermal (DTS-T) EAR method as well as its procedures are developed in this paper. With 2008 M7.3 Yutian earthquake, China, 2008 M8.0 Wenchuan earthquake, China, and 2010 M7.1 Christchurch earthquake, New Zealand, being examples of tectonic earthquakes, the technical procedures of DTS-T method are demonstrated, which show that the obtained compositive thermal anomaly has a significant effect on earthquake prediction.

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