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Logistic Regression for Geologically Constrained Mapping of Gold Potential, Baguio District, Philippines

2001; Volume: 10; Issue: 3 Linguagem: Inglês

10.2113/0100165

1878-6715

Emmanuel John M. Carranza,

Statistical and numerical algorithms

Research Article| July 01, 2001 Logistic Regression for Geologically Constrained Mapping of Gold Potential, Baguio District, Philippines EMMANUEL JOHN M. CARRANZA; EMMANUEL JOHN M. CARRANZA Mines and Geosciences Bureau (MGB), Regional Office No. 5, Philippines *Present address: ITC, Hengelosestraat 99, 7514 AE Enschede, The Netherlands. Search for other works by this author on: GSW Google Scholar MARTIN HALE MARTIN HALE International Institute for Aerospace Survey and Earth Sciences (ITC) Enschede, The Netherlands, and Faculty of Applied Earth Sciences Delft University of Technology (TUD) Delft, The Netherlands Search for other works by this author on: GSW Google Scholar Exploration and Mining Geology (2001) 10 (3): 165–175. https://doi.org/10.2113/0100165 Article history received: 15 Oct 2001 accepted: 31 May 2002 first online: 02 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation EMMANUEL JOHN M. CARRANZA, MARTIN HALE; Logistic Regression for Geologically Constrained Mapping of Gold Potential, Baguio District, Philippines. Exploration and Mining Geology 2001;; 10 (3): 165–175. doi: https://doi.org/10.2113/0100165 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 An application of logistic regression to mapping of gold potential in the Baguio district of the Philippines is described. Categorical map data such as lithologic units and proximity classes of curvi-linear features, based on spatial association analyses, are quantified systematically and used as independent variables in logistic regression to predict the probability for presence or absence of gold mineralization. Regression experiments to compare between using all independent variables that are associated spatially with the response variable and using only statistically significant independent variables are performed. The results of the regression experiments are similar; however, the use of all independent variables produces slightly optimistic results but better prediction rates for the known gold deposits in the test district. At least 68% of the 'model' large-scale gold deposits and at least 76% of the 'validation' small-scale gold deposits were predicted correctly. The predicted geologically favorable zones are also similar to delineated geochemically anomalous zones. The technique presented using logistic regression as a data integration tool is effective for geologically constrained technique of mapping mineral potential. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.