An integrated data-directed numerical method for estimating the undiscovered mineral endowment in a region

McCammon, Richard B.; Finch, Warren Irvin; Kork, John O.; Bridges, N.J.

doi:10.1007/bf02286436

Cited by 9 publications

(7 citation statements)

References 5 publications

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“…Accordingly, a grade‐tonnage model for the type of mineral deposits that is most similar to those of the known HACD in the island is unsuitable for assessment of undiscovered HACD in the island. However, application of one‐level prediction for estimating undiscovered metal endowment (McCammon & Kork, 1992; McCammon et al ., 1994) is demonstrated here because, unlike the afore‐mentioned methods, it is based on a measure of favorability of mineral deposit occurrence.…”

Section: Quantitative Estimation Of Number Of Undiscovered Prospectsmentioning

confidence: 99%

“…One‐level prediction (denoted as OLP) determines only unknown endowment in unexplored prospective areas (McCammon & Kork, 1992; McCammon et al ., 1994). It involves dividing an area into a grid of equal‐area cells and assumes that the datasets used are sufficient to represent per cell (i) favorability of mineral deposit occurrence, (ii) degree of exploration, and (iii) known endowment.…”

Section: Quantitative Estimation Of Number Of Undiscovered Prospectsmentioning

confidence: 99%

“…The assumption and application of a calibration constant C to every unit cell in a study area introduces Type I and Type II errors (McCammon & Kork, 1992; McCammon et al ., 1994). Type I error pertains to unit cells, whereas Type II error pertains to unit cells.…”

Section: Quantitative Estimation Of Number Of Undiscovered Prospectsmentioning

confidence: 99%

“…Like C , estimates of Type I and Type II errors associated with C are derived from control unit cells. Formulas for estimation of Type I and Type II errors can be found in McCammon and Kork (1992), McCammon et al . (1994), Carranza et al .…”

Section: Quantitative Estimation Of Number Of Undiscovered Prospectsmentioning

confidence: 99%

“…Unlike in Scott and Dimitrakopoulos (2001) and Fallon et al . (2010), one‐level prediction (McCammon & Kork, 1992; McCammon et al ., 1994) and fractal analysis (cf. Raines, 2008; Zuo et al ., 2009b) were employed here for estimation of the number of undiscovered prospects.…”

Section: Introductionmentioning

confidence: 99%

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

Carranza

2010

Resource Geology

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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.

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Section: Quantitative Estimation Of Number Of Undiscovered Prospectsmentioning

confidence: 99%

Section: Quantitative Estimation Of Number Of Undiscovered Prospectsmentioning

confidence: 99%

Section: Quantitative Estimation Of Number Of Undiscovered Prospectsmentioning

confidence: 99%

Section: Quantitative Estimation Of Number Of Undiscovered Prospectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Carranza

2010

Resource Geology

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Bayesian and multivariate methods applied to favorability quantification in Recôncavo Basin and Ribeira Belt, Brazil

1998

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

2009

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An integrated data-directed numerical method for estimating the undiscovered mineral endowment in a region

Cited by 9 publications

References 5 publications

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

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

Bayesian and multivariate methods applied to favorability quantification in Recôncavo Basin and Ribeira Belt, Brazil

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

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