Development of a Decision Support System based on remote sensing and GIS techniques for gold-rich area identification in SE Spain

Abstract

Remote sensing techniques and spatial data analysis through Geographic Information Systems (GIS) have been jointly applied in a mineral exploration context to identify gold-rich potential areas in SE Spain. Results con rm the usefulness of this integrated methodological approach as an e V ective tool to assess mineral potential in the studied region. Satellite and airborne image analysis have o V ered valuable thematic information referring both to lithology and altered zone mapping from photointerpretation and digital classi cation. For this goal, SPOT panchromatic and Landsat TM images were merged in order to obtain high resolution image documents for photointerpretation purposes, and the Feature Principal Component Selection technique was applied to highlight hydrothermal alteration zones characterized by hydroxyl-bearing minerals. Remote sensing results were integrated, in conjunction with existing maps and data from mineral exploration surveys, into the GIS as vector or raster layers. The GIS implementation stage consisted of the creation of a relational database, including a complete set of georeferenced data, and the elaboration of a speci c user interface for spatial analysis using GIS facilities in order to establish a Decision Support System (DSS). The system was used to simulate di V erent mineral exploration scenarios, calculating and mapping a Mineral Potential Index that was interpreted in a practical sense in terms of surface reduction and economic costs.

Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping

“…Each of the continuous-value fuzzy evidence maps created in this paper can also be used in multi-index overlay modeling (e.g., Bonham-Carter, 1994;Harris et al, 2001;Chico-Olmo et al, 2002;De Araújo and Macedo, 2002;Billa et al, 2004). In all of these methods, continuous evidential scores of distances in each proximity value of evidential features can be derived directly using a suitable logistic function even without using the locations of known mineral deposit occurrences as in traditional data-driven MPM approaches and even without discretization of evidential values into some arbitrary classes and assigning weights to classes based on an analyst's expert opinion as in traditional knowledgedriven MPM approaches.…”

Data-Driven Index Overlay and Boolean Logic Mineral Prospectivity Modeling in Greenfields Exploration

“…Multi-class index overlay is traditionally applied to knowledge-driven MPM (Bonham-Carter 1994;Chico-Olmo et al 2002;Billa et al 2004;Carranza 2008b) whereby evidential maps are categorized into some arbitrary classes, and are then integrated considering weights subjectively assigned to each evidential map. Each of the jth classes of the ith evidential map is assigned a score S ij according to their relative importance.…”

Geometric average of spatial evidence data layers: A GIS-based multi-criteria decision-making approach to mineral prospectivity mapping

“…Techniques for GIS-based MCDM (GIS-MCDM) are widely used to solve certain problems in the fields of geoscience (e.g., Chico-Olmo et al, 2002;De Araújo and Macedo, 2002;Pazand et al, 2011;Lisitsin et al, 2013;Feizizadeh et al, 2014). Because natural resource management is plagued with uncertainties of various kinds (e.g., Nepomuceno Filho et al, 1999;Runge et al, 2011;Carranza, 2014;Ford et al, 2015;Khan and Deutsch, 2015), GIS-MCDM techniques are primarily concerned with combining information from several criteria into a single evaluation model to modulate uncertainty Feizizadeh et al, 2014).…”