Mapping of prospectivity and estimation of number of undiscovered prospects for lode gold, southwestern Ashanti Belt, Ghana

Carranza, Emmanuel John M.; Owusu, E.A.; Hale, Martin

doi:10.1007/s00126-009-0250-6

Cited by 95 publications

(32 citation statements)

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“…These faults, totally overlain by Mesozoic strata, are considered to be formed before Indosinian period and reactivated in the Mesozoic [45], although the detailed geometrical and kinematical characteristics of these faults are still not clear. In previous studies, faults have been proven to act as favorable pathways for transporting ore-related magma and ore-forming fluids from deep sources to shallow trap zones, resulting in a strong association of these faults with hydrothermal mineral deposits [10,16,19]. In the study area, the petrological data and geophysical profiles evidence that a magma chamber was developed in the Mesozoic at about −10 km from the surface [42].…”

Section: Interpretation Of Structural Controls On Copper Mineralizationmentioning

confidence: 75%

Structural Controls on Copper Mineralization in the Tongling Ore District, Eastern China: Evidence from Spatial Analysis

Sun

et al. 2018

Minerals

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Structures exert significant controls on hydrothermal mineralization, although such controls commonly have cryptic expression in geological datasets dominated by 2D maps. Analysis of spatial patterns of mineral deposits and quantification of their correlation with detailed structural features are beneficial to understand the plausible structural controls on mineralization. In this paper, a series of GIS-based spatial methods, including fractal, Fry, distance distribution and weights-of-evidence analyses, were employed to reveal structural controls on copper mineralization in the Tongling ore district, eastern China. The results indicate that Yanshanian intrusions exert the most significant control on copper mineralization, followed by EW-trending faults, intersections of basement faults and folds. The scale-variable distribution patterns of copper occurrences are attributed to the different structural controls operating in the basement and sedimentary cover. In the basement, EW-trending faults serve as pathways for channeling Yanshanian magma from a deep magma chamber to structurally controlled trap zones in the caprocks, imposing an important regional control on the spatial distribution of Cretaceous magmatic-hydrothermal system genetically related to copper mineralization. In the sedimentary cover, bedding-parallel shear zones, formed during the progressive folding and shearing in Indosinian and overprinted by tensional deformation in Yanshanian, act as favorable sites for hosting, focusing and depositing the ore-bearing fluids, playing a vital role in the localization of stratabound deposits at fine scale.

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Section: Interpretation Of Structural Controls On Copper Mineralizationmentioning

confidence: 75%

Structural Controls on Copper Mineralization in the Tongling Ore District, Eastern China: Evidence from Spatial Analysis

Sun

et al. 2018

Minerals

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“…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).…”

Section: Consistency With Previous Resultsmentioning

confidence: 99%

“…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.…”

Section: N()~  -Dbmentioning

confidence: 99%

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

Blenkinsop

2014

Pure Appl. Geophys.

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Abstract:Mass dimensions of natural resources, established from power law scaling relationships between numbers of resources and distance from an origin, have important implications for ore-forming processes, resource estimation and exploration. The relation between the total quantity of resource and distance, measured by the mass-radius scaling exponent, may be even more useful. Lode gold deposits, geothermal wells and volcanoes, and conventional and unconventional gas wells are examined in this study. The scaling exponents generally increase from the lode gold through geothermal wells to gas data sets, reflecting decreasing degrees of clustering. Mass dimensions are similar to the mass-radius scaling exponents, and could be used as substitutes in the common case that data are not available for the latter. All of these resources are formed by fluid fluxes in the crust, and therefore percolation theory is an appropriate unifying framework to understand their significance. The mass dimensions indicate that none of the percolation networks that formed the deposits reached the percolation threshold. 2 AbstractMass dimensions of natural resources, established from power law scaling relationships between numbers of resources and distance from an origin, have important implications for oreforming processes, resource estimation and exploration. The relation between the total quantity of resource and distance, measured by the mass-radius scaling exponent, may be even more useful. Lode gold deposits, geothermal wells and volcanoes, and conventional and unconventional gas wells are examined in this study. The scaling exponents generally increase from the lode gold through geothermal wells to gas data sets, reflecting decreasing degrees of clustering. Mass dimensions are similar to the mass-radius scaling exponents, and could be used as substitutes in the common case that data are not available for the latter. All of these resources are formed by fluid fluxes in the crust, and therefore percolation theory is an appropriate unifying framework to understand their significance. The mass dimensions indicate that none of the percolation networks that formed the deposits reached the percolation threshold.

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“…Turcotte 1986;Lucido et al 1991;Agterberg et al 1993;Cheng et al 1994;Sim et al 1999;Goncalves et al 2001;Shen & Zhao 2002;Li et al 2003;Lima et al 2003;Ortega et al 2006;Ali et al 2007;Carranza 2009;Zuo et al 2009;Afzal et al 2010;Wang et al 2011;Zuo 2011). Fractal analysis is able to indicate and correspondingly justify the differences within mineralization, alteration, lithology and zonation of ore deposits especially in hydrothermal occurrences such as porphyry Cu deposits (Goncalves et al 2001;Cheng 2007;Carranza 2008;Carranza et al 2009;Cheng & Agterberg 2009;Afzal et al 2011Afzal et al , 2012. However, proper knowledge of the geological and geochemical aspects of a deposit is important in order to identify characteristics of geochemical populations on the basis of fractal analysis (Cheng 1999;Sim et al 1999;Goncalves et al 2001;Li et al 2003;Carranza 2009;Carranza & Sadeghi 2010).…”

Section: Correlation Between Geology and Concentration-volume Fractalmentioning

confidence: 99%

Correlation between geology and concentration-volume fractal models: significance for Cu and Mo mineralized zones separation in the Kahang porphyry deposit (Central Iran)

Yasrebi¹,

Afzal²,

Wetherelt³

et al. 2013

Geologica Carpathica

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This study identifies the major mineralized zones including supergene enrichment and hypogene enrichment in the Kahang Cu-Mo porphyry deposit which is located in Central Iran based on subsurface data and utilization of the concentration-volume (C-V) fractal model. Additionally, a correlation between results achieved from a C-V fractal model and geological models consisting of zonation, mineralography and alteration have been conducted in order to have an accurate recognition and modification of the main mineralized zones. Log-log plots indicate five geochemical populations for Cu and Mo in the deposit which means that mineralization commences with 0.075 % and 13 ppm for Cu and Mo (as the first thresholds) respectively. The main mineralization began for Cu 0.42 % and Mo 100 ppm and also enriched mineralization containing Cu 1.8 % and Mo 645 ppm which is located in the central part of the deposit. According to the C-V model, the main Cu-Mo mineralized zones occur in the hypogene zone, especially in the central, NW and NE parts of the Kahang deposit. The supergene enrichment zone derived via the C-V model is smaller than that in the geological model and is located in the central and eastern parts of the deposit. Results analysed by the C-V fractal model certify that the interpreted zones based on the fractal model are accurate. To certify this, a logratio matrix has been employed to validate the C-V fractal model for the Cu and Mo main mineralized zones.

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Mapping of prospectivity and estimation of number of undiscovered prospects for lode gold, southwestern Ashanti Belt, Ghana

Cited by 95 publications

References 96 publications

Structural Controls on Copper Mineralization in the Tongling Ore District, Eastern China: Evidence from Spatial Analysis

Structural Controls on Copper Mineralization in the Tongling Ore District, Eastern China: Evidence from Spatial Analysis

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

Correlation between geology and concentration-volume fractal models: significance for Cu and Mo mineralized zones separation in the Kahang porphyry deposit (Central Iran)

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