Mathematical Geosciences

, 41:421

Deriving Optimal Exploration Target Zones on Mineral Prospectivity Maps

  • Pravesh Debba
  • Emmanuel J. M. Carranza
  • Alfred Stein
  • Freek D. van der Meer
Article

Abstract

This paper describes a quantitative methodology for deriving optimal exploration target zones based on a probabilistic mineral prospectivity map. The methodology is demonstrated in the Rodalquilar mineral district in Spain. A subset of known occurrences of mineral deposits of the type sought was considered discovered and then used as training data, and a map of distances to faults/fractures and three band ratio images of hyperspectral data were used as layers of spatial evidence in weights-of-evidence (WofE) modeling of mineral prospectivity in the study area. A derived posterior probability map of mineral deposit occurrence showing non-violation of the conditional independence assumption and having the highest prediction rate was then put into an objective function in simulated annealing in order to derive a set of optimal exploration focal points. Each optimal exploration focal point represents a pixel or location within a circular neighborhood of pixels with high posterior probability of mineral deposit occurrence. Buffering of each optimal exploration focal point, based on proximity analysis, resulted in optimal exploration target zones. Many of these target zones coincided spatially with at least one occurrence of mineral deposit of the type sought in the subset of cross-validation (i.e., presumed undiscovered) mineral deposits of the type sought. The results of the study showed the usefulness of the proposed methodology for objective delineation of optimal exploration target zones based on a probabilistic mineral prospectivity map.

Keywords

Simulated annealing Epithermal deposits Weights-of-evidence Hyperspectral remote sensing Hydrothermal alteration 

References

  1. Abrams M, Ashley R, Rowan L, Goetz AFH, Kahle A (1977) Use of imaging in the .46–2.36 μm spectral region for alteration mapping in the Cuprite mining district. Nevada: USGS OFR-77-585 Google Scholar
  2. Agterberg FP (1992) Combining indicator patterns in weights of evidence modeling for resource evaluation. Nonrenew Resour 1(1):39–50 CrossRefGoogle Scholar
  3. Agterberg FP, Bonham-Carter GF (1999) Logistic regression and weights of evidence modeling in mineral exploration. In: Proceedings of 28th international symposium on computer applications in the mineral industries, pp 483–590 Google Scholar
  4. Agterberg FP, Bonham-Carter GF (2005) Measuring the performance of mineral-potential maps. Nat Resour Res 14(1):1–17 CrossRefGoogle Scholar
  5. Agterberg FP, Cheng W (2002) Conditional independence test of weights-of-evidence modeling. Nat Resour Res 11(4):249–255 CrossRefGoogle Scholar
  6. Agterberg FP, Bonham-Carter GF, Wright DF (1990) Statistical pattern integration for mineral exploration. In: Gaal G, Merriam DF (eds) Computer applications in resource exploration and assessment for minerals and petroleum. Pergamon, Elmsford, pp 1–21 Google Scholar
  7. Arribas A (Jr), Cunningham CG, Rytuba JJ, Rye RO, Kelley WC, Podwysocki MH, McKee EH, Tosdal RM (1995) Geology, geochronology, fluid inclusions, and isotope geochemistry of the Rodalquilar gold alunite deposit, Spain. Econ Geol 90:795–822 CrossRefGoogle Scholar
  8. Bishop MM, Fienberg SE, Holland PW (1975) Discrete multivariate analysis: theory and practice. MIT Press, Cambridge, 587 p Google Scholar
  9. Bohachevsky IO, Johnson ME, Stein ML (1986) Generalized simulated annealing for function optimization. Technometrics 28(3):209–217 CrossRefGoogle Scholar
  10. Bonham-Carter GF, Agterberg FP, Wright DF (1988) Integration of geological datasets for gold exploration in Nova Scotia. Photogramm Eng Remote Sens 54:1585–1592 Google Scholar
  11. Bonham-Carter GF, Agterberg FP, Wright DF (1989) Weights of evidence modelling: a new approach to mapping mineral potential. In: Agterberg FP, Bonham-Carter GF (eds) Statistical applications in the Earth sciences: geological survey. Canada paper 89-9, pp 171–183 Google Scholar
  12. Boots BN, Getis A (1988) Point pattern analysis. Sage university scientific geography series, vol 8. Sage, Thousand Oaks, 93 p Google Scholar
  13. Carranza EJM, Hale M (2000) Geologically constrained probabilistic mapping of gold potential, Baguio district, Philippines. Nat Resour Res 9(3):237–253 CrossRefGoogle Scholar
  14. Carranza EJM, Hale M (2003) Evidential belief functions for data-driven geologically constrained mapping of gold potential, Baguio district, Philippines. Ore Geol Rev 22(1):117–132 CrossRefGoogle Scholar
  15. Chung CF, Agterberg FP (1980) Regression models for estimating mineral resources from geological map data. Math Geol 12(5):473–488 CrossRefGoogle Scholar
  16. Clark RN, Swayze GA, Gallagher AJ, King TVV, Calvin WM (1993) The US geological survey, digital spectral library: Version 1: 0.2 to 3.0 microns. US Geological Survey Open File Report, pp 93–592 Google Scholar
  17. Cox DP (1993) Estimation of undiscovered deposits in quantitative mineral resource assessments examples from Venezuela and Puerto Rico. Nonrenew Res 2(2):82–91 CrossRefGoogle Scholar
  18. Crósta AP, de Souza-Filho CR, Azevedo F, Brodie C (2003) Targeting key alteration minerals in epithermal deposits in Patagonia, Argentina, using ASTER imagery and principal component analysis. Int J Remote Sens 24(21):4233–4240 CrossRefGoogle Scholar
  19. Cudahy T, Okada K, Brauhart C (2000) Targeting VMS-style Zn mineralisation at Panorama, Australia, using airborne hyperspectral VNIR-SWIR HyMap data. In: ERIM proceedings of the 14th international conference on applied geologic remote sensing, Las Vegas, pp 395–402 Google Scholar
  20. de Gruijter JJ, ter Braak CJF (1990) Model-free estimation from spatial samples: a reappraised of classical sampling theory. Math Geol 22(4):407–415 CrossRefGoogle Scholar
  21. Debba P, van Ruitenbeek F, van der Meer F, Carranza EJM, Stein A (2005) Optimal field sampling for targeting minerals using hyperspectral data. Remote Sens Environ 99(4):373–386 CrossRefGoogle Scholar
  22. Debba P, Stein A, van der Meer F, Carranza EJM, Lucieer A (2008) Field sampling from a segmented image. In: Gervasi O, Murgante B, Laganá A, Taniar D, Mun Y, Gavrilova ML (eds) Computational science and its applications ICCSA 2008. Series LNCS, vol 5072. Springer, Heidelberg, pp 756–768. ISBN 978-3-540-69838-8 CrossRefGoogle Scholar
  23. Goetz AFH, Srivastava V (1985) Mineralogical mapping Cuprite mining district, Nevada. In: Vane G, Goetz A (eds) Proc Airborne imaging spectrometer data analysis workshop. Jet propulsion laboratory publication 85-41. Jet Propulsion Laboratory, Pasadena, pp 22–31 Google Scholar
  24. Good IJ (1950) Probability and the weighing of evidence. Griffin, London, 119 p Google Scholar
  25. IGME (1981) Mapa Geologico de España (Carboneras, 1.046/24–43; El Pozo de los Frailes, 1.060/24–44), e. 1:50,000. Instituto Geologico y Minero de España (IGME), Servicio de Publicaciones, Ministerio de Industria y Energia, Madrid Google Scholar
  26. Kirkpatrick S, Gelatt CD (Jr), Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680 CrossRefGoogle Scholar
  27. Kruse FA (2002) Comparison of AVIRIS and Hyperion for hyperspectral mineral mapping. In: SPIE aerospace conference, 9–16 March 2002, Big Sky, Montana. Published on CD-ROM, IEEE Catalog Number 02TH8593C, Paper 6.0102, pp 1–12 Google Scholar
  28. Lillesand TM, Kiefer RW, Chipman JW (1994) Remote sensing and image interpretation. Wiley, New York, 750 p Google Scholar
  29. McCammon RB, Root DH, Schruben PG (2004) Statewide estimates of undiscovered deposits of gold, silver, copper, lead, and zinc. Nat Resour Res 13(3):201–207 CrossRefGoogle Scholar
  30. Pan G (1993) Canonical favorability model for data integration and mineral potential mapping. Comput Geosci 19(8):1077–1100 CrossRefGoogle Scholar
  31. Porwal A, Carranza EJM, Hale M (2003) Artificial neural networks for mineral-potential mapping: a case study from Aravallia province, Western India. Nat Resour Res 12(3):155–171 CrossRefGoogle Scholar
  32. Richter R (1996) Atmospheric correction of DAIS hyperspectral image data. In: SPIE proceedings, vol 2756. Int Soc Opt Eng, Bellingham, pp 390–399 CrossRefGoogle Scholar
  33. Rigol-Sanchez JP, Chica-Olmo M, Abarca-Hernandez F (2003) Artificial neural networks as a tool for mineral potential mapping with GIS. Int J Remote Sens 24(5):1151–1156 CrossRefGoogle Scholar
  34. Scott M, Dimitrakopoulous R (2001) Quantitative analysis of mineral resources for strategic planning: implications for Australian geological surveys. Nat Resour Res 10(3):159–177 CrossRefGoogle Scholar
  35. Singer DA (1993) Basic concepts in three-part quantitative assessments of undiscovered mineral resources. Nonrenew Resour 2(2):69–81 CrossRefGoogle Scholar
  36. Singer DA (1994) Conditional estimates of the number of podiform chromite deposits. Nonrenew Resour 3(3):200–204 CrossRefGoogle Scholar
  37. Singer DA, Kouda R (1997) Classification of mineral deposits into types using mineralogy with a probabilistic neural network. Nonrenew Res 6(1):27–32 CrossRefGoogle Scholar
  38. Singer DA, Kouda R (2003) Typing mineral deposits using their grades and tonnages in an artificial neural network. Nat Resour Res 12(3):201–208 CrossRefGoogle Scholar
  39. Shyan-Shu S, Ji-Zheng C, Shi-Shang J (2005) An interactive sampling strategy based on information analysis and ordinary kriging for locating hot spot regions. Math Geol 37(1):29–48 CrossRefGoogle Scholar
  40. Tapia R, Stein A, Bijker W (2005) Optimization of sampling schemes for vegetation mapping using fuzzy classification. Remote Sens Environ 99(4):425–433 CrossRefGoogle Scholar
  41. Thiart C, Bonham-Carter GF, Agterberg FP, Cheng Q, Panahi A (2004) An application of the new omnibus test for conditional independence in weights-of-evidence modeling. In: Harris J, Wright D (eds) Special volume on GIS applications in the Earth sciences. Geological Association of Canada, Calgary (chapter in book in press) Google Scholar
  42. van Groenigen JW, Gandah M, Bouma J (2000a) Soil sampling strategies for precision agriculture research under Sahelian conditions. Soil Sci Soc Am J 64:1674–1680 Google Scholar
  43. van Groenigen JW, Pieters G, Stein A (2000b) Optimizing spatial sampling for multivariate contamination in urban areas. Environmetrics 11:227–244 CrossRefGoogle Scholar

Copyright information

© International Association for Mathematical Geology 2008

Authors and Affiliations

  • Pravesh Debba
    • 1
  • Emmanuel J. M. Carranza
    • 2
  • Alfred Stein
    • 2
  • Freek D. van der Meer
    • 2
  1. 1.CSIR, Logistics and Quantitative MethodsCSIR Built EnvironmentPretoriaSouth Africa
  2. 2.International Institute for Geo-Information Science and Earth Observation (ITC)EnschedeThe Netherlands

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