Mathematical Geosciences

, Volume 42, Issue 4, pp 433–446 | Cite as

Applicability of Statistical Learning Algorithms for Spatial Variability of Rock Depth

  • Pijush Samui
  • T. G. Sitharam
Case Study


Two algorithms are outlined, each of which has interesting features for modeling of spatial variability of rock depth. In this paper, reduced level of rock at Bangalore, India, is arrived from the 652 boreholes data in the area covering 220 sq⋅km. Support vector machine (SVM) and relevance vector machine (RVM) have been utilized to predict the reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth. The support vector machine (SVM) that is firmly based on the theory of statistical learning theory uses regression technique by introducing ε-insensitive loss function has been adopted. RVM is a probabilistic model similar to the widespread SVM, but where the training takes place in a Bayesian framework. Prediction results show the ability of learning machine to build accurate models for spatial variability of rock depth with strong predictive capabilities. The paper also highlights the capability of RVM over the SVM model.


Support vector machine Relevance vector machine Rock depth Spatial variability 


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Copyright information

© International Association for Mathematical Geosciences 2010

Authors and Affiliations

  1. 1.Center for Disaster Mitigation and ManagementVITVelloreIndia
  2. 2.Department of Civil EngineeringIndian Institute of ScienceBangaloreIndia

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