From Predictive Mapping of Mineral Prospectivity to Quantitative Estimation of Number of Undiscovered Prospects

Abstract

This paper proposes that the spatial pattern of known prospects of the deposit-type sought is the key to link predictive mapping of mineral prospectivity (PMMP) and quantitative mineral resource assessment (QMRA). This proposition is demonstrated by PMMP for hydrothermal Au-Cu deposits (HACD) and by estimating the number of undiscovered prospects for HACD in Catanduanes Island (Philippines). The results of analyses of the spatial pattern of known prospects of HACD and their spatial associations with geological features are consistent with existing knowledge of geological controls on hydrothermal Au-Cu mineralization in the island and elsewhere, and are used to define spatial recognition criteria of regional-scale prospectivity for HACD. Integration of layers of evidence representing the spatial recognition criteria of prospectivity via application of data-driven evidential belief functions results in a map of prospective areas occupying 20% of the island with fitting-and prediction-rates of 76% and 70%, respectively. The predictive map of prospective areas and a proxy measure for degrees of exploration based on the spatial pattern of known prospects of HACD were used in one-level prediction of undiscovered mineral endowment, which yielded estimates of 79 to 112 undiscovered prospects of HACD. Application of radial-density fractal analysis of the spatial pattern of known prospects of HACD results in an estimate of 113 undiscovered prospects of HACD. Thus, the results of the study support the proposition that PMMP can be a part of QMRA if the spatial pattern of discovered prospects of the deposit-type sought is considered in both PMMP and QMRA.

Regional gold potential mapping in Kelantan (Malaysia) using probabilistic based models and GIS

“…These methods may be more appropriate for lesser to unexplored landscapes with few known deposits of the type sought [16][17][18]. The knowledge-driven approaches are undertaken with fuzzy logic [17,18] and Dempster-Shafer evidence belief functions [19][20][21][22][23]. Data-driven techniques offer a more objective approach to the modeling process.…”

Scaling Laws for the Distribution of Gold, Geothermal, and Gas Resources

“…Mass dimensions of various types of gold deposit have been presented by Blenkinsop (1994Blenkinsop ( , 1995, Carranza (2009;2010) and Carranza et al (2009). In all these studies, different fractal dimensions are given at low and high r values, ranging from 0.54 for the low r values, to 1.52 (high r).…”

“…Box counting and mass dimensions have been determined for gold deposits (e.g. Carlson 1991;Blenkinsop 1994, Carranza 2009Carranza et al 2009;Carranza 2010;Carranza and Saghedi 2010) and for petroleum deposits (Barton and Scholz 1995), but massradius scaling exponents are hardly reported in the literature. The aim of this paper is to investigate mass dimensions and the suitability of the mass-radius scaling exponent to describe the distribution of some natural resources.…”

Control of style-of-faulting on spatial pattern of earthquake-triggered landslides

“…Likewise, Fry analysis has only been used recently by to study landslide controls, but it has been employed extensively to deduce trends of structures controlling mineralization (e.g. Vearncombe and Vearncombe 1999;Carranza 2009Carranza , 2011Carranza et al 2009;Carranza and Sadeghi 2010) and geothermal fields (Carranza et al 2008b) as well as to infer post-mineralization deformation (Carranza and Sadeghi 2014). As shown by , distance distribution analysis and Fry analysis are complementary and supplementary methods that can be used in conjunction with one another to investigate certain controls on landsliding.…”