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Spatial Statistical Technique in relating earthquake epicentres with structural features

Moreover, the standard deviation ellipse for the data of epicenters of earthquakes is drawn with the calculated major and minor axes that show the directions of maximum and minimum spread of locations respectively (Chandramani, 2001). It has been observed that the orientation of the major axis of the standard deviation ellipse is more or less parallel with the trend of the major thrusts and faults occurring in that area (Fig. 1). This is a clear indication of the major role the structural features, mainly the thrusts, play in the occurrences of earthquakes in the region. Further, the Rose diagrams plotted for both the faults and the thrusts show that the major thrust (NNW-SSE) and the fault planes (NW-SE) have the trend that is more or less parallel to the major axis of the standard deviation ellipse.


Fig. 1: The state of Himachal Pradesh and its surrounding areas showing the positions of earthquake epicenters, structural features like faults and thrusts, Rose diagrams for the thrusts and the faults and calculated central tendencies like mean and median centers (almost overlapping at this scale) and standard distance ellipse.

Conclusion
The proliferation of GIS has prompted researchers in many fields to reconsider their way of conducting research or solving practical problems. With the facility of statistical analysis within GIS, analysts can now process larger volumes of data within a shorter period of time and with greater precision. For detecting spatial pattern in point distribution, three techniques are commonly used. The first one is the Quadrat analysis, which determines if a point distribution is similar to a random pattern. The second one is the nearest neighbor analysis, which compares the average distance between nearest neighbor in a point distribution to that of a theoretical pattern. The third one is the spatial autocorrelation coefficient, which measures how similar and dissimilar an attribute of neighboring point is. In this study, the spatial dataset containing information of earthquake epicenters provides a very good example of point distribution. The statistical analysis of this dataset reveals a clustered pattern in which adjacent points show similar characteristics.

The structural features mainly thrusts and faults occurring in the area are believed to be instrumental in the occurrence of earthquakes. This is further strengthened by the orientation of the major axis of the standard deviation ellipse, which is parallel to the orientation of the major thrusts present in that area. 

Reference

• Chandramani, (2001), Point Pattern Analysis with GIS, M.Tech. Seminar Report, Department of Earth Sciences, Indian Institute of Technology, Roorkee. (Unpublished).
  
• Lee, J., and D. W. S. Wong, (2001), Staistical Analysis with ArcView GIS, John Wiley & Sons, New York.
  
• Saraf, A. K., P. Mishra, S. Mitra, B. Sarma and D. K. Mukhopadhyay, (2002) Remote Sensing and GIS Technologies for improvements in Geological Structures Interpretation and mapping. International Journal of Remote Sensing, (in press).
  
• Srikantia, S.V. and O. N. Bhargava, (1998) Geology of Himachal Pradesh, Geological Society of India Publications, Bangalore, India.