Extended Weights-of-Evidence Modelling for Predictive Mapping of Base Metal Deposit Potential in Aravalli Province, Western India

Abstract

Research Article| October 01, 2001 Extended Weights-of-Evidence Modelling for Predictive Mapping of Base Metal Deposit Potential in Aravalli Province, Western India ALOK PORWAL; ALOK PORWAL International Institute for Geo-information Science and Earth Observation (ITC) Enschede, The Netherlands Department of Mines and Geology, Government of Rajasthan Udaipur, India Search for other works by this author on: GSW Google Scholar E.J.M. CARRANZA; E.J.M. CARRANZA International Institute for Geo-information Science and Earth Observation (ITC) Enschede, The Netherlands Search for other works by this author on: GSW Google Scholar M. HALE M. HALE International Institute for Geo-information Science and Earth Observation (ITC) Enschede, The Netherlands Delft University of Technology, Delft, The Netherlands Search for other works by this author on: GSW Google Scholar Exploration and Mining Geology (2001) 10 (4): 273–287. https://doi.org/10.2113/0100273 Article history received: 11 Jul 2002 accepted: 11 Dec 2002 first online: 02 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share MailTo Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation ALOK PORWAL, E.J.M. CARRANZA, M. HALE; Extended Weights-of-Evidence Modelling for Predictive Mapping of Base Metal Deposit Potential in Aravalli Province, Western India. Exploration and Mining Geology 2001;; 10 (4): 273–287. doi: https://doi.org/10.2113/0100273 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyExploration and Mining Geology Search Advanced Search Abstract Approaches to mineral potential mapping based on weights of evidence generally use binary maps, whereas, real-world geospatial data are mostly multi-class in nature. The consequent reclassification of multi-class maps into binary maps is a simplification that might result in a loss of information. This paper describes results of using multi-class evidential maps in an extended weights-of-evidence model vis-à-vis results of using binary evidential maps in a simple-weights-of-evidence model. The study area in the south-central part of Aravalli province (western India) hosts a number of SEDEX-type base metal deposits in Proterozoic supracrustal rocks. Recognition criteria for base metal deposits were represented as both multi-class and binary evidential maps. The known mineral deposits were divided into two subsets, viz., the training and the validation subsets. The training subset was used to calculate, for the evidential maps, the weights, contrasts, and posterior probabilities and their variances. The distributions of expected frequencies of base metal deposits estimated from the posterior probabilities and the observed frequencies were compared using standard goodness-of-fit tests to verify conditional independence of the input evidential maps. The posterior probabilities from both the models were mapped and interpreted to classify the study area into zones favorable, permissive, and non-permissive for base metal deposit occurrence. As compared to the simple weights-of-evidence model, the extended weights-of-evidence model results in more robust and finely differentiated posterior probabilities in favorable and permissive zones and has a better prediction rate. The results also reveal that the statistical properties of the weights of evidence, the contrasts, and the posterior probabilities are not significantly degenerated by using multi-class evidential maps in weights-of-evidence modelling. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.