Application of Geostatistical Simulation to Assessment of the Effects of Uncertainty of Spatial Data in Mineral Potential Prediction

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2006 Vol.43, Issue 3 Preview Page Next Page

Application of Geostatistical Simulation to Assessment of the Effects of Uncertainty of Spatial Data in Mineral Potential Prediction 광상부존가능성 예측에 대한 공간자료의 불확실성의 영향 추정을 위한 지구통계학적 시뮬레이션의 적용: 박노욱, 오석훈
No-Wook Park and Seokhoon Oh

30 June 2006. pp. 213-223

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Abstract

Traditional mineral potential maps have been generated by integrating spatial data sets derived from sparsely sampled data and uncertainty is inherent in them. Thus, it is important to assess the effect of uncertainty of the spatial data on mineral potential mapping. This paper presents a geostatistical simulation approach to assessing the effects of uncertainty of input spatial data on data integration for mineral potential mapping. Geostatistical simulation, which is based on the concept of random function, can stochastically generate multiple and alternative realizations of unknown truth and a set of realizations generated from geostatistical simulation can be used as input into a spatial data integration model for mineral potential mapping. The resulting probabilistic distributions of integration outputs and prediction capabilities can be used to assess the uncertainty regarding mineral potential prediction. This approach is illustrated by a case study for mineral potential mapping with geochemical data sets. The case study results indicate that uncertainty of input spatial data both affected the spatial distribution of integration results and resulted in the differences of prediction capabilities about future discovery. This probabilistic information on spatial uncertainty can be used as decision-support information on uncertainty or risk assessment for unexplored future discovery.

Keywords

Uncertainty

Geostatistical simulation

Kriging

Mineral potential

공간적으로 산재된 샘플링 자료를 내삽하여 얻어진 단일 자료들을 통합하여 작성된 광상부존가능성도에는 필연적으로 불확실성이 수반되게 되므로 불확실한 공간 자료의 영향을 추정하는 것이 중요하다. 이 연구에서는 이러한 공간 자료의 불확실성이 광상부존가능성도 작성을 위한 통합 모델에 미치는 영향을 정량적으로 분석하기 위해 지구통계학적 시뮬레이션을 적용하였다. 확률 함수의 개념에 기반을 둔 지구통계학적 시뮬레이션을 통해 얻어지는 다양한 실현을 공간 통합 모델에 입력자료로 사용할 경우 통합 결과 분포와 예측 능력에 대한 불확실성 분포를 얻을 수 있다. 지화학 자료를 이용한 광상부존가능성도 작성 사례연구를 통해, 입력자료의 불확실성은 공간 통합 결과의 공간적 분포에 영향을 미치고 이에 따라 아직 발견되지 않은 광상에 대한 예측 능력에 대해서도 차이가 나타나는 것을 확인하였다. 이러한 확률적 분포는 작성된 광상부존가능성도를 미래의 개발과 연관지어 해석하기 위한 의사결정 보조자료로 활용할 수 있을 것으로 기대된다.

키워드

불확실성

지구통계학적 시뮬레이션

크리깅

광상부존가능성

References

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박노욱, 지광훈, Chung, C.F., 권병두, 2005, “산사태 취약성 분석을 위한 GIS 기반 확률론적 추정 모델과 모수적 모델의 적용”, 자원환경지질, 제38권 1호, pp. 45-55.
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Carranza, E.J.M., 2004, “Weight of evidence modeling of mineral potential: a case study using small number of prospects, Abra, Philippines”, Natural Resources Research, Vol. 13, No. 3, pp. 173-187.
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De Bruin, S., 2000, “Predicting the areal extent of landcover types using classified imagery and geostatistics”, Remote Sensing of Environment, Vol. 74, No. 3, pp. 387-396.
Deutsch, C.V. and Journel, A.G., 1998, GSLIB: Geostatistical software library and user's guide, Oxford University Press, New York, p. 369.
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Kyriakidis, P.C. and Dungan, J.L., 2001, “A geostatistical approach for mapping thematic classification accuracy and evaluating the impact of inaccurate spatial data on ecological model predictions”, Environmental and Ecological Statistics, Vol. 8, No. 4, pp. 311-330.
McLachlan, G.J., 1992, Discriminant analysis and statistical pattern recognition, John Wiley & Sons, Inc., New Jersey, pp. 526.
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Information

Publisher :The Korean Society of Mineral and Energy Resources Engineers
Publisher(Ko) :한국자원공학회
Journal Title :Journal of the Korean Society for Geosystem Engineering
Journal Title(Ko) :한국지구시스템공학회지
Volume : 43
No :3
Pages :213-223

[1] 고상모 외, 2002, 국내광물자원 자료전산화 및 광상재평가 종합시스템 개발연구 III(1:250,000 서울, 강릉지질도폭), 한국지질자원연구원, p. 84.

[2] 박노욱, 지광훈, Chung, C.F., 권병두, 2005, “산사태 취약성 분석을 위한 GIS 기반 확률론적 추정 모델과 모수적 모델의 적용”, 자원환경지질, 제38권 1호, pp. 45-55.

[3] 이진수, 서효준, 황인호, 1998, 지화학조사연구(1:250,000 강릉도폭 광역 지화학도), 한국자원연구소, p. 147.

[4] 최종근, 2002, 공간정보 모델링: 크리깅과 최적화 기법, 구미서관, p. 291.

[5] An, P., Moon, W.M., and Rencz, A., 1991, “Application of fuzzy set theory to integrated mineral exploration”, Canadian Journal of Exploration Geophysics, Vol. 27, No. 1, pp. 1-11.

[6] Bonham-Carter, G.F., Agterberg, F.P., and Wright, D.F., 1988, “Integration of geological datasets for gold exploration in Nova Scotia”, Photogrammetric Engineering and Remote Sensing, Vol. 54, No. 11, pp. 1585-1592.

[7] Carranza, E.J.M., 2004, “Weight of evidence modeling of mineral potential: a case study using small number of prospects, Abra, Philippines”, Natural Resources Research, Vol. 13, No. 3, pp. 173-187.

[8] Chung, C.F. and Keating, P.B., 2002, “Mineral potential evaluation based on airborne geophysical data”, Exploration Geophysics, Vol. 33, No. 1, pp. 28-34.

[9] De Bruin, S., 2000, “Predicting the areal extent of landcover types using classified imagery and geostatistics”, Remote Sensing of Environment, Vol. 74, No. 3, pp. 387-396.

[10] Deutsch, C.V. and Journel, A.G., 1998, GSLIB: Geostatistical software library and user's guide, Oxford University Press, New York, p. 369.

[11] Goovaerts, P., 1997, Geostatistics for natural resources evaluation, Oxford University Press, New York, p. 483.

[12] Goovaerts, P., 1999, “Impact of simulation algorithm, magnitude of ergodic fluctuations and number of realizations on the spaces of uncertainty of flow properties”, Stochastic Environmental Research and Risk Assessment, Vol. 13, No. 3, pp. 161-182.

[13] Kyriakidis, P.C. and Dungan, J.L., 2001, “A geostatistical approach for mapping thematic classification accuracy and evaluating the impact of inaccurate spatial data on ecological model predictions”, Environmental and Ecological Statistics, Vol. 8, No. 4, pp. 311-330.

[14] McLachlan, G.J., 1992, Discriminant analysis and statistical pattern recognition, John Wiley & Sons, Inc., New Jersey, pp. 526.

[15] Moon, W.M., 1990, “Integration of geophysical and geological dta using evidential belief function”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 28, No. 4, pp. 711-720.

[16] Oh, S., 2000, Geostatistical approach to Bayesian inversion of geophysical data, Ph.D. Thesis, Seoul National University. Pardo-Iguzquiza, E. 1999, “VARFIT: A fortran-77 program for fitting variogram models by weighted least squares”, Computers & Geosciences, Vol. 25, No. 3, pp. 251-261.

[17] Park, N.-W., 2004, Multi-source spatial data fusion with geostatistical uncertainty assessment: applications to landslide susceptibility analysis and land-cover classification, Ph. D. Thesis, Seoul National University.

[18] Pearl, J., 1997, Probabilistic reasoning in intelligent systems: networks of plausible inference, Morgan Kaufmann Publishers, San Mateo, Calif., p. 552.

[19] SpatialModels, 2004, User's guide of spatial prediction modeling system, SpatialModels Inc., p. 108.

[20] Wang, G., Gertner, G.Z., Fang, S., and Anderson, A.B., 2003, “Mapping multiple variables for predicting soil loss by joint sequential co-simulation with TM images and slope map”, Photogrammetric Engineering and Remote Sensing, Vol. 69, No. 8, pp. 889-898.

Journal of the Korean Society of Mineral and Energy Resources Engineers ISSN:2288-0291(Print) 2288-2790(Online) 한국자원공학회지

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