Designing Optimal Strategies for Mineral Exploration

Geoffroy, J. G. De; Wignall, T. K.

doi:10.1007/978-1-4684-1230-7

Cited by 13 publications

(8 citation statements)

References 0 publications

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“…These views are consistent with the outcomes of the mathematical detection model described by de Geoffroy and Wignall (1985) and refined in their later work (Wignall and de Geoffroy, 1987) hereafter (WDG model). This model is illustrated in Fig.…”

Section: Conventional Detection Modelsupporting

confidence: 86%

“…The binomial Equation is expressed as endnote (1) Pr(x)~nC x p x q n{x (1) where in a 3D context, Pr(x) is the effective probability of hitting a gold target x times, n is the number of standard drill holes (endnote (2)), x is the number of hits, p (5B/A) is the basic probability of hitting a gold target, q (512p) is the basic probability of not hitting a gold target, n C x is the mathematical combination involving x hits from n tries, A is the volume of the geological population being explored expressed in cubic metres (endnote (3)) and B is the volume of the gold targets in the population also expressed in cubic metres. When x is zero, the first two terms on the RHS will equal 1.…”

Section: Binomial Frameworkmentioning

confidence: 99%

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Detection effectiveness of angled versus vertical drilling

Harrisson¹

2009

Mining Technology

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The paper examines the conventional belief that angled holes are more detection effective than vertical holes with dipping targets. The examination uses a binomial probability framework. Analysis in two and three dimensions shows that all holes can be evaluated for detection effectiveness in three dimensions but only standard vertical holes can be in two dimensions. A programme of standard vertical holes using its two-dimensional advantage had a higher detection capability than one of standard angled holes irrespective of the target's orientation. Although this outcome is at odds with the conventional model, the paper shows that the conventional model contains fundamental flaws. It identifies their source and explains why they occurred. These flaws together with evidence provided by the binomial model lead the paper to conclude that the conventional belief cannot be sustained. The financial implications of this conclusion for the industry are significant.

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Section: Conventional Detection Modelsupporting

confidence: 86%

Section: Binomial Frameworkmentioning

confidence: 99%

Detection effectiveness of angled versus vertical drilling

Harrisson¹

2009

Mining Technology

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“…The responses of underground materials in the surface respecting to the methods of exploration, are affected by complex geological patterns and, hence, interpretation of data obtained from such complicated domains is difficult (Geoffroy & Wignall, 1985). So complexity of geological setting could be simplified using multi-stage exploration process (Geoffroy and Wignall, 1985) such as sequential approach (Geoffroy & Wignall, 1985;Edwards & Bowen, 2013) for sustainable groundwater supplies in the terrain underlying by crystalline basement rocks.…”

Section: Conceptual Model Of Karstic Water Resourcesmentioning

confidence: 99%

“…So complexity of geological setting could be simplified using multi-stage exploration process (Geoffroy and Wignall, 1985) such as sequential approach (Geoffroy & Wignall, 1985;Edwards & Bowen, 2013) for sustainable groundwater supplies in the terrain underlying by crystalline basement rocks. For this, in the first step, conceptual model which characterize underground resource should be made (Edwards & Bowen, 2013).…”

Section: Conceptual Model Of Karstic Water Resourcesmentioning

confidence: 99%

Prospectivity Modeling of Karstic Groundwater Using a Sequential Exploration Approach in Tepal Area, Iran

Sharifi

Arab-Amiri

Kamkar‐Rouhani

et al. 2017

Archives of Mining Sciences

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The purpose of this study is water prospectivity modeling (WPM) for recognizing karstic water-bearing zones by using analyses of geo-exploration data in Kal-Qorno valley, located in Tepal area, north of Iran. For this, a sequential exploration method applied on geo-evidential data to delineate target areas for further exploration. In this regard, two major exploration phases including regional and local scales were performed. In the first phase, indicator geological features, structures and lithological units, were used to model groundwater prospectivity as a regional scale. In this phase, for karstic WPM, fuzzy lithological and structural evidence layers were generated and combined using fuzzy operators. After generating target areas using WPM, in the second phase geophysical surveys including gravimetry and geoelectrical resistivity were carried out on the recognized high potential zones as a local scale exploration. Finally the results of geophysical analyses in the second phase were used to select suitable drilling locations to access and extract karstic groundwater in the study area. wodonośnych pochodzenia krasowego zamodelowano warstwy struktur skalnych dowodzące występowa-nia wód w oparciu o podejście logiki rozmytej. Po wytyczeniu obszarów docelowych, w drugim etapie badań przeprowadzono szczegółowe analizy geofizyczne z wykorzystanie grawimetrii i badań oporności geo-elektrycznej w strefach potencjalnego występowania wód, w aspekcie badania w skali lokalnej. W końcowym etapie, wyniki analiz geofizycznych otrzymane w drugim etapie procedury wykorzystane zostały do wyznaczenia miejsc wykonania odwiertów do uzyskania wód gruntowych pochodzenia krasowego w badanym terenie.Słowa kluczowe: modelowanie występowania wód, badanie sekwencyjne, logika rozmyta, wody gruntowe pochodzenia krasowego

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“…In the first approach, the optimum hole spacing is computed considering the intersection probability of the drilling network and the target, which is varied as a function of the geometry of the target, the sampling pattern, and the relative orientation of the target to the grid (Houseknecht, 1982;Schejbal, 1998;Singer and Wickman, 1969;Shurygin, 1976). The optimum grid spacing was evaluated by maximizing the gross drilling return based on the probability of detecting the target, the value of the target, and the cost of drilling (Drew, 1979;Geoffroy and Wignall, 1985).…”

Section: Introductionmentioning

confidence: 99%