Geology-exploration endowment models for simultaneous estimation of discoverable mineral resources and endowment

Pan, Guocheng; Harris, DeVerle P.

doi:10.1007/bf02065804

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…There is a need for more work in this area. Currently, some of the most interesting work is being performed by Liu and others (1991), Harris andPan (1987, 1990), and Pan and Harris (1991).…”

Section: Other Methods Of Estimating the Number Of Undiscovered Depositsmentioning

confidence: 99%

Comments on the "three-step" method for quantification of undiscovered mineral resources

Bultman¹,

Force²,

Gettings³

et al. 1993

Open-File Report

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This report is a position paper intended to promote discussion and debate about USGS mineral-resource estimation techniques, and was presented on August 6,1992, to the Panel for Evaluation of USGS Mineral-Resource Assessment Methodology. Although there is substantial unresolved disagreement between the authors and two of the three technical reviewers of this report, all reviewers approved the manuscript for publication and it is being released in open-file format to facilitate rapid and thorough debate about this area of research. The work by the panel comprises another in a continuing series of scientific reviews and evaluations of USGS resource assessment methodologies that periodically are contracted for, or convened by, the USGS. The most recent previous example is an evaluation by the National Research Council of the Department of the Interior's 1989 assessment procedures for undiscovered oil and gas resources.

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Section: Other Methods Of Estimating the Number Of Undiscovered Depositsmentioning

confidence: 99%

Comments on the "three-step" method for quantification of undiscovered mineral resources

Bultman¹,

Force²,

Gettings³

et al. 1993

Open-File Report

View full text Add to dashboard Cite

show abstract

“…The discovery process for some deposit types, e.g., those for which structural, geochemical, alteration, or geophysical signatures are correlated to deposit size or those for which discovery is primarily by drilling and for which size is strongly related to areal extent, is size biased, meaning that large, high-grade deposits tend to be discovered in early stages of the exploration of regions (Chung et al 1992;Pan and Harris 1991). For such deposit types, the prognostication of exploration outcomes or the estimation of additional resources in undiscovered deposits should take into account the implication of this bias to the tonnages and grades of the undiscovered deposits.…”

Section: Scarceness Rareness and Exceptionalnessmentioning

confidence: 99%

“…Two different approaches to improvement of estimation by mathematical models estimated on control areas are: (1) use only control areas that are exhaustively explored and (2) extend the mathematical model to include exploration variables (such as those defined in Pan and Harris (1991). Both of these solutions present difficulties however: (1) except for very small regions, there are few regions large enough to make good control areas that are exhaustively explored and (2) information on exploration activities generally is not available for regions large enough to make good control areas.…”

Section: Prediction With Dynamic Control Samplesmentioning

confidence: 99%

General Framework of Quantitative Target Selections

Pan¹

2018

Handbook of Mathematical Geosciences

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Mineral target selection has been an important research subject for geoscientists around the world in the past three decades. Significant progress has been made in development of mathematical techniques and estimation methodologies for mineral mapping and resource assessment. Integration of multiple data sets, either by experts or statistical methods, has become a common practice in estimation of mineral potentials. However, real effect of these methodologies is at best very limited in terms of uses for government macro policy making, resource management, and mineral exploration in commercial sectors. Several major problems in data integration remain to be solved in order to achieve significant improvement in the effect of resource estimation. Geoscience map patterns are used for decision-making for mineral target selections. The optimal data integration methods proposed so far can be effectively applied by using GIS technologies. The output of these methods is a prognostic map that indicates where hidden ore bodies may occur. Issues related to randomness of mineral endowment, intrinsic statistical relations, exceptionalness of ore, intrinsic geological units, and economic translation and truncation, are addressed in this chapter. Moreover, a number of specific important technical issues in information synthesis are also identified, including information enhancement, spatial continuity, data integration and target delineation. Finally, a new concept of dynamic control areas is proposed for future development of quantification of mineral resources. G. Pan ( ✉ )China Hanking Holdings,

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“…Fourth, generate a synthesized favorability or probability measure for the occurrence of critical genetic factor(s) based on the probabilities estimated in the third step. Finally, delineate potential exploration targets from the synthesized thvorability or probability measure through optimum discretization (see Pan and Harris, 1990). These targets have been referred to as intrinsic geological units with respect to the chosen critical genetic factor(s) (Pan and Harris, in press).…”

Section: Target Delineationmentioning

confidence: 99%

Fundamental issues in quantitative estimation of mineral resources

Pan¹,

Harris

Heiner³

1992

Nat Resour Res

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Several issues considered to be fundamental in quantitative estimation of mineral resources and selection of mineral targets are addressed. Integration of multiple data sets, either by experts or by statistical methods, has become a common practice in estimation of mineral potential. Several major problems in data integration must be solved to significantly improve mineral resource estimation. Issues related to randomness of mineral endowment, basic statistical tools, exceptionalness of ore, and economic truncation and translation are discussed in the first part of the article. A number of important technical problems in data integration are also identified; they include data compilation, information enhancement, information synthesis, and target selection.

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Geology-exploration endowment models for simultaneous estimation of discoverable mineral resources and endowment

Cited by 9 publications

References 17 publications

Comments on the "three-step" method for quantification of undiscovered mineral resources

Comments on the "three-step" method for quantification of undiscovered mineral resources

General Framework of Quantitative Target Selections

Fundamental issues in quantitative estimation of mineral resources

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