A Complex Systems Model Approach to Quantified Mineral Resource Appraisal

For federal and state land management agencies, mineral resource appraisal has evolved from value-based to outcome-based procedures wherein the consequences of resource development are compared with those of other management options. Complex systems modeling is proposed as a general framework in which to build models that can evaluate outcomes. Three frequently used methods of mineral resource appraisal (subjective probabilistic estimates, weights of evidence modeling, and fuzzy logic modeling) are discussed to obtain insight into methods of incorporating complexity into mineral resource appraisal models. Fuzzy logic and weights of evidence are most easily utilized in complex systems models. A fundamental product of new appraisals is the production of reusable, accessible databases and methodologies so that appraisals can easily be repeated with new or refined data. The data are representations of complex systems and must be so regarded if all of their information content is to be utilized.The proposed generalized model framework is applicable to mineral assessment and other geoscience problems. We begin with a (fuzzy) cognitive map using (+1,0,−1) values for the links and evaluate the map for various scenarios to obtain a ranking of the importance of various links. Fieldwork and modeling studies identify important links and help identify unanticipated links. Next, the links are given membership functions in accordance with the data. Finally, processes are associated with the links; ideally, the controlling physical and chemical events and equations are found for each link. After calibration and testing, this complex systems model is used for predictions under various scenarios.

[1]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[2]  H. Gershengorn,et al.  A tale of two futures: HIV and antiretroviral therapy in San Francisco. , 2000, Science.

[3]  E. A. Bray Mineral resource potential and geology of Coronado National Forest, southeastern Arizona and southwestern New Mexico , 1996 .

[4]  DeVerle P. Harris,et al.  Mineral Resources Appraisal. , 1985 .

[5]  D. Groves,et al.  Using fuzzy logic in a Geographic Information System environment to enhance conceptually based prospectivity analysis of Mississippi Valley‐type mineralisation , 2000 .

[6]  Q. Cheng,et al.  Weights of evidence modeling and weighted logistic regression for mineral potential mapping , 1993 .

[7]  Q. Cheng,et al.  Fuzzy Weights of Evidence Method and Its Application in Mineral Potential Mapping , 1999 .

[8]  C. A. Carlson Spatial distribution of ore deposits , 1991 .

[9]  John C. Davis,et al.  Computers in geology---25 years of progress , 1993 .

[10]  Bernard P. Zeigler,et al.  Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems , 2000 .

[11]  D. Singer Basic concepts in three-part quantitative assessments of undiscovered mineral resources , 1993 .

[12]  Mark E. Gettings,et al.  Quantifying favorableness for occurrence of a mineral deposit type using fuzzy logic; an example from Arizona , 1993 .

[13]  Gary William Flake,et al.  The Computational Beauty of Nature: Computer Explorations of Fractals, Chaos, Complex Systems and Adaptation , 1998 .

[14]  William K. Nuttle Ecosystem managers can learn from past successes , 2000 .

[15]  Dennis P. Cox,et al.  Mineral deposit models , 1986 .

[16]  D. Singer,et al.  Classification of mineral deposits into types using mineralogy with a probabilistic neural network , 1997 .

[17]  D. Harris Mineral resources appraisal : mineral endowment, resources, and potential supply : concepts, methods and cases , 1984 .

[18]  G. Hall,et al.  EXTECH I: a multidisciplinary approach to massive sulphide research in the Rusty Lake-Snow Lake greenstone belts, Manitoba , 1996 .

[19]  Dietrich Dörner,et al.  The Logic Of Failure: Recognizing And Avoiding Error In Complex Situations , 1997 .

[20]  F. Agterberg,et al.  Statistical applications in the earth sciences , 1990 .

[21]  Abraham Kandel,et al.  Fuzzy techniques in pattern recognition , 1982 .

[22]  G. Bonham-Carter,et al.  VHMS favourability mapping with GIS-based integration models, Chisel Lake-Anderson Lake area , 1996 .

[23]  Bart Kosko,et al.  Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence , 1991 .

[24]  M. Gettings,et al.  Comments on the "three-step" method for quantification of undiscovered mineral resources , 1993 .

[25]  C. Knox-Robinson Vectorial fuzzy logic: A novel technique for enhanced mineral prospectivity mapping, with reference to the orogenic gold mineralisation potential of the Kalgoorlie Terrane, Western Australia , 2000 .

[26]  F. Golley A History of the Ecosystem Concept in Ecology: More Than the Sum o f the Parts , 1993 .

[27]  F. Fisher Tertiary mineralization and hydrothermal alteration in the Stinkingwater mining region, Park County, Wyoming , 1972 .