ENG  Vol.3 No.9 , September 2011
Application of Geostatistical Models for Estimating Spatial Variability of Rock Depth
Abstract: Rock depth information of a site is a significant factor for geotechnical engineering and earthquake ground response analysis. In this paper, reduced level of rock at Bangalore is arrived from the 652 boreholes in the area covering 220 km2. Geostatistical modeling based on kriging (simple and ordinary) techniques has been applied for estimating reduced level of hard rock in Bangalore. The models are used to compute variance of estimated reduced level of the rock. A new type of cross-validation analysis proves the robustness of the developed models. The comparison between the simple and ordinary kriging model demonstrates that the ordinary kriging model is superior to simple kriging model in predicting reduced level of rock in the subsurface of Bangalore.
Cite this paper: nullP. Samui and T. Sitharam, "Application of Geostatistical Models for Estimating Spatial Variability of Rock Depth," Engineering, Vol. 3 No. 9, 2011, pp. 886-894. doi: 10.4236/eng.2011.39108.

[1]   G. Matheron, “Principles of Geostatistics,” Society of Economic Geologists, Vol. 58, No. 8, 1963, pp. 1246- 1266. doi:10.2113/gsecongeo.58.8.1246

[2]   E. H. Isaaks and R. M. Srivastava, “An Introduction to Applied Geostatics,” Oxford University Press, New York, 1989.

[3]   J. C. Davis, “Statistics and Data Analysis in Geology,” 3rd Edition, Wiley, New York, 2002.

[4]   J. P. Delhomme, “Spatial Variability and Uncertainty in Groundwater Flow Parameters: A Geostatistical Approach,” Water Resources Research, Vol. 15, No. 2, 1979, pp. 269-280. doi:10.1029/WR015i002p00269

[5]   F. Gambolati and G. Volpi, “Groundwater Contour Mapping in Venic by Stochastic Interpolators 1. Theroy”. Water Resources Research, Vol. 15, No. 2, 1979, pp. 281-290. doi:10.1029/WR015i002p00281

[6]   M. Soulie, “Geostatistical Applications in Goetechnics,” Geostatistics for Naturai Resources Characterization: Part 2, NATO ASI Series,” Reidel Publishing Company, Dordrecht, 1983, pp. 703-730.

[7]   P. H. S. W. Kulatilake and A. Ghosh, “An Investigation into Accuracy of Sparial Variation Estimation Using Static Cone Penetrometer Data,” Proceeding of the First Internatioanl Symposium on Penetration Testing, Orlando, 1988, pp. 815-821.

[8]   P. H. S. W. Kulatilake, “Probabilistic Potentiometric Surface Mapping,” Journal of Geotechnical & Geoenvironmental Engineering, Vol. 115, No. 11, 1989, pp. 1569-1587. doi:10.1061/(ASCE)0733-9410(1989)115:11(1569)

[9]   M. Soulie, P. Montes and V. Sivestri, “Modeling Spatial Variability of Soil Parameters,” Canadian Geotechnical Journal, Vol. 27, No. 5, 1990, pp. 617-630. doi:10.1139/t90-076

[10]   P. Chiasson, J. Lafleur, M. Soulie and K. T. Law, “Characterizing Spatial Variability of Clay by Geostatistics,” Canadian Geotechnical Journal, Vol. 32, No. 1, 1995, pp. 1-10. doi:10.1139/t95-001

[11]   M. W. O’Neill and L. M. Yoon, “Spatial Variability of CPT Parameters at University of Houston NGES,” Probabilistic Site Characterization at the National Geotechnical Experimental Sites, Geotechnical Special Publication, Vol. 121, 2004, pp.1-12.

[12]   K. M. Dawson and L. G. Baise, “Three-Dimensional Liquefaction Potential Analysis Using Geostatistical Interpolation,” Soil Dynamics and Earthquake Engineering, Vol. 52, No. 5, 2005, pp. 369-381. doi:10.1016/j.soildyn.2005.02.008

[13]   B. P. Radhakrishna and R. Vaidyanadhan. “Geology of Karnataka,” Geological Society of India, Bangalore, 1997.

[14]   G. Matheron,“Théorie des Variables Régionalisées in Traité d’Informatique Géologique,” Masson, Paris, 1972, pp. 306-378.

[15]   A. Guillaume, “Introduction a la Géologie Quantitative,” Masson, Paris, 1977.

[16]   P. K. Kitanidis, “Introduction to Geostatistics: Applications in Hydrogeology,” University Press, Cambridge, 1997, pp. 86-95.

[17]   C. V. Deutsh, “Correcting for Negative Weights in Ordinary Kriging,” Computers & Geosciences, Vol. 22, No. 7, 1996, pp. 765-773. doi:10.1016/0098-3004(96)00005-2