The task of modeling the distribution of a large number of earthquake events with frequent tremors detected prior to a main shock presents us unique challenges to model a robust classifier tool for rapid responses are needed in order to address victims. We have designed using a relational database for running a geophysical modeling application after connecting database record of all clusters of foreshock events from (1998-2010) for a complete catalog of seismicity analysis for the Himalayan basin. by Nath et al,2010. This paper develops a reduced rough set analysis method and implements this novel structure and reasoning process for foreshock cluster forecasting. In this study, we developed a reusable information technology infrastructure, called Efficient Machine Readable for Emergency Text Selection(EMRETS). The association and importance of precursory information in reference to earthquake rupture analysis is found out through attribute reduction based on rough set analysis. Secondly, find the importance of attributes through information entropy is a novel approach for high dimensional complex polynomial problems pre-dominant in geo-physical research and prospecting. Thirdly, we discuss the reducible indiscernible matrix and decision rule generation for a particular set of geographical co-ordinates leading to the spatial discovery of future earthquake having prior foreshock. This paper proposes a framework for extracting, classifying, analyzing, and presenting semi-structured catalog data sources through feature representation and selection.

Non-rigid image registration in extracting deformation map for two satellite images of the same region before and after earthquake occurrence based on measure of intensity dissimilarity C(Ir, T(If)) can play a significant role in post hazard analysis. In this paper, we have proposed a novel image transformation and regional segmentation of the same visualized region by assigning displacement label to change in intensity using Advanced Synthetic Aperture Radar (ASAR) satellite images. We used graph cut based non rigid registraion with a data term and a smoothness term for assigning markovianity between neighboring pixels. Displacement labels has been directly assigned from this data term for small intensity difference. Secondly, our data term imposes stricter penalty for intensity mismatches and hence yields higher registration accuracy.
Based on the satellite image analysis through image segmentation, it is found that the area of .997 km2 for the Honshu region was a maximum damage zone localized in the coastal belt of NE Japan fore-arc region. A further objective has been to correlate fractal analysis of seismic clustering behavior with image segmentation suggesting that increase in the fractal dimension coefficient is associated with the deviation of the pixel values that gives a metric of the devastation of the de-clustered region.