A quantitative estimation of the value of geoscience information in mineral exploration: Optimal search sequences.

Persistent Link:
http://hdl.handle.net/10150/186863
Title:
A quantitative estimation of the value of geoscience information in mineral exploration: Optimal search sequences.
Author:
Stanley, Michael Clare.
Issue Date:
1994
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
This research provides quantitative estimates of value of geoscience information in the exploration for porphyry copper deposits of Arizona. As part of the study, an expert system named DISCOVERY is designed to integrate geological models, statistical decision theory, and mineral economics within a Monte Carlo simulation framework. The system requires, for each exploration survey, a probability that a simulated deposit will be detected. These detection probabilities are estimated using expert opinion from a panel of experienced geoscientists. This dissertation pioneers the application of influence allocation processes in geoscience, to alleviate criticisms associated with expert opinion. The work has five major focuses: (1) an adaptation of Grayson's (1960) exploration decision theory into a modified Bayesian framework; (2) the use of electronic brainstorming to define principal recognition features that define exploration deposit models; (3) the use of influence allocation voting tools to estimate detection probability by survey type and sampling intensity; (4) a comprehensive engineering cost model to derive the net present value of deposits simulated within the system; and (5) a comprehensive drilling model to describe optimal sampling intensity in regional exploration. The system operates using two models: (1) an estimate of the value of information based upon 'static' estimates; and (2) a 'dynamic' simulation model that replicates the activities of the exploration industry. The static model provides value estimates on a survey by survey basis, consistent with prevailing economic conditions. The dynamic model loosens the economic constraints in order to simulate exploration practices and determine the optimal sequence of search surveys. Collectively these two models provide estimates of the value of information derived from exploration surveys and determine the optimum search strategy for porphyry copper deposits in Arizona. The static model produces estimates of net gain displaying a high level of consistency for each survey technology and sampling intensity across many thousands of iterations. The dynamic model does not produce satisfying results, requiring additional modifications to the Bayesian structure in order to better simulate exploration.
Type:
text; Dissertation-Reproduction (electronic)
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Mining and Geological Engineering; Graduate College
Degree Grantor:
University of Arizona
Committee Chair:
Harris, DeVerle P.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleA quantitative estimation of the value of geoscience information in mineral exploration: Optimal search sequences.en_US
dc.creatorStanley, Michael Clare.en_US
dc.contributor.authorStanley, Michael Clare.en_US
dc.date.issued1994en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractThis research provides quantitative estimates of value of geoscience information in the exploration for porphyry copper deposits of Arizona. As part of the study, an expert system named DISCOVERY is designed to integrate geological models, statistical decision theory, and mineral economics within a Monte Carlo simulation framework. The system requires, for each exploration survey, a probability that a simulated deposit will be detected. These detection probabilities are estimated using expert opinion from a panel of experienced geoscientists. This dissertation pioneers the application of influence allocation processes in geoscience, to alleviate criticisms associated with expert opinion. The work has five major focuses: (1) an adaptation of Grayson's (1960) exploration decision theory into a modified Bayesian framework; (2) the use of electronic brainstorming to define principal recognition features that define exploration deposit models; (3) the use of influence allocation voting tools to estimate detection probability by survey type and sampling intensity; (4) a comprehensive engineering cost model to derive the net present value of deposits simulated within the system; and (5) a comprehensive drilling model to describe optimal sampling intensity in regional exploration. The system operates using two models: (1) an estimate of the value of information based upon 'static' estimates; and (2) a 'dynamic' simulation model that replicates the activities of the exploration industry. The static model provides value estimates on a survey by survey basis, consistent with prevailing economic conditions. The dynamic model loosens the economic constraints in order to simulate exploration practices and determine the optimal sequence of search surveys. Collectively these two models provide estimates of the value of information derived from exploration surveys and determine the optimum search strategy for porphyry copper deposits in Arizona. The static model produces estimates of net gain displaying a high level of consistency for each survey technology and sampling intensity across many thousands of iterations. The dynamic model does not produce satisfying results, requiring additional modifications to the Bayesian structure in order to better simulate exploration.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineMining and Geological Engineeringen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.chairHarris, DeVerle P.en_US
dc.contributor.committeememberRieber, Michaelen_US
dc.contributor.committeememberPeters, William C.en_US
dc.contributor.committeememberGlass, Charles E.en_US
dc.contributor.committeememberTitley, Spencer R.en_US
dc.identifier.proquest9506994en_US
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