Deriving Optimal Exploration Target Zones on Mineral Prospectivity Maps

This paper describes a quantitative methodology for deriving optimal exploration target zones based on a probabilistic mineral prospectivity map. The methodology is demonstrated in the Rodalquilar mineral district in Spain. A subset of known occurrences of mineral deposits of the type sought was considered discovered and then used as training data, and a map of distances to faults/fractures and three band ratio images of hyperspectral data were used as layers of spatial evidence in weights-of-evidence (WofE) modeling of mineral prospectivity in the study area. A derived posterior probability map of mineral deposit occurrence showing non-violation of the conditional independence assumption and having the highest prediction rate was then put into an objective function in simulated annealing in order to derive a set of optimal exploration focal points. Each optimal exploration focal point represents a pixel or location within a circular neighborhood of pixels with high posterior probability of mineral deposit occurrence. Buffering of each optimal exploration focal point, based on proximity analysis, resulted in optimal exploration target zones. Many of these target zones coincided spatially with at least one occurrence of mineral deposit of the type sought in the subset of cross-validation (i.e., presumed undiscovered) mineral deposits of the type sought. The results of the study showed the usefulness of the proposed methodology for objective delineation of optimal exploration target zones based on a probabilistic mineral prospectivity map.

[1]  Arthur Getis,et al.  Point pattern analysis , 1985 .

[2]  G. Pieters,et al.  Optimizing spatial sampling for multivariate contamination in urban areas , 2000 .

[3]  D. Lozano,et al.  Mapa geologico de Espana , 1919 .

[4]  Johan Bouma,et al.  Soil Sampling Strategies for Precision Agriculture Research under Sahelian Conditions , 2000 .

[5]  F. Agterberg.  Combining indicator patterns in weights of evidence modeling for resource evaluation , 1992 .

[6]  C. Braak,et al.  Model-free estimation from spatial samples: A reappraisal of classical sampling theory , 1990 .

[7]  F. Agterberg,et al.  Regression models for estimating mineral resources from geological map data , 1980 .

[8]  Roussos Dimitrakopoulos,et al.  Quantitative Analysis of Mineral Resources for Strategic Planning: Implications for Australian Geological Surveys , 2001 .

[9]  D. Singer.  Basic concepts in three-part quantitative assessments of undiscovered mineral resources , 1993 .

[10]  D. P. Cox.  Estimation of undiscovered deposits in quantitative mineral resource assessments—examples from Venezuela and Puerto Rico , 1993 .

[11]  Alfred Stein,et al.  Field Sampling from a Segmented Image , 2008, ICCSA.

[12]  D. Singer.  Conditional estimates of the number of podiform chromite deposits , 1994 .

[13]  Rudolf Richter.  Atmospheric correction of DAIS hyperspectral image data , 1996 .

[14]  Q. Cheng,et al.  Conditional Independence Test for Weights-of-Evidence Modeling , 2002 .

[15]  D. Singer,et al.  Classification of mineral deposits into types using mineralogy with a probabilistic neural network , 1997 .

[16]  Mario Chica-Olmo,et al.  Artificial neural networks as a tool for mineral potential mapping with GIS , 2003 .

[17]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[18]  M. E. Johnson,et al.  Generalized simulated annealing for function optimization , 1986 .

[19]  David Taniar,et al.  Computational Science and Its Applications - ICCSA 2008, International Conference, Perugia, Italy, June 30 - July 3, 2008, Proceedings, Part I , 2008, ICCSA.

[20]  Donald A. Singer,et al.  Typing Mineral Deposits Using Their Grades and Tonnages in an Artificial Neural Network , 2003 .

[21]  Emmanuel John M. Carranza,et al.  Artificial Neural Networks for Mineral-Potential Mapping: A Case Study from Aravalli Province, Western India , 2003 .

[22]  R. McCammon,et al.  Statewide Estimates of Undiscovered Deposits of Gold, Silver, Copper, Lead, and Zinc , 2004 .

[23]  Anne B. Kahle,et al.  Use of imaging in the 0.46-2.36 [micrometers] spectral region for alteration mapping in the Cuprite mining district, Nevada , 1977 .

[24]  M. Podwysocki,et al.  Geology, geochronology, fluid inclusions, and isotope geochemistry of the Rodalquilar gold alunite deposit, Spain , 1995 .

[25]  A. Stein,et al.  Optimal field sampling for targeting minerals using hyperspectral data , 2005 .

[26]  F. Agterberg,et al.  Weights of evidence modelling: a new approach to mapping mineral potential , 1990 .

[27]  E. Carranza,et al.  Evidential belief functions for data-driven geologically constrained mapping of gold potential, Baguio district, Philippines , 2003 .

[28]  E. Carranza,et al.  Geologically Constrained Probabilistic Mapping of Gold Potential, Baguio District, Philippines , 2000 .

[29]  F. Agterberg,et al.  Statistical Pattern Integration for Mineral Exploration , 1990 .

[30]  Roger N. Clark,et al.  The US Geological Survey, digital spectral reflectance library: version 1: 0.2 to 3.0 microns , 1993 .

[31]  Stephen E. Fienberg,et al.  Discrete Multivariate Analysis: Theory and Practice , 1976 .

[32]  Wietske Bijker,et al.  Optimization of sampling schemes for vegetation mapping using fuzzy classification , 2005 .

[33]  Shi-Shang Jang,et al.  An Interactive Sampling Strategy Based on Information Analysis and Ordinary Kriging for Locating Hot Spot Regions , 2005 .

[34]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[35]  L. J. Savage,et al.  Probability and the weighing of evidence , 1951 .

[36]  Guocheng Pan,et al.  Canonical favorability model for data integration and mineral potential mapping , 1993 .

[37]  A. Crósta,et al.  Targeting key alteration minerals in epithermal deposits in Patagonia, Argentina, using ASTER imagery and principal component analysis , 2003 .

[38]  Graeme F. Bonham-Carter,et al.  Measuring the Performance of Mineral-Potential Maps , 2005 .