1887
Volume 49, Issue 6
  • ISSN: 0812-3985
  • E-ISSN: 1834-7533

Abstract

[

Multiple-point geostatistical simulation (MPS) was applied to develop 3D ore models matched to surrounding geological information accompanying aeromagnetic data using a training image (TI). Conventional 3D geological models generated from a limited number of boreholes and other geological information may be useful for evaluating the mineral resources around the boreholes, while also bearing uncertainty regarding the evaluation of the ore body over the entire area. Geostatistical analysis accompanying the geophysical interpretation is adopted to reduce the uncertainty of the 3D ore model. Among the geostatistical methods, MPS based on a TI made from available geological information is chosen to simulate the configuration and distribution of the ore body according to the geological structure. The present study proposes a method for reducing the uncertainty of the 3D ore model, applying MPS for mine evaluation to create probabilistic ore models and analysing the correlation between the models and geophysical data. This method was applied to a metal mine located in Korea. Single normal equation simulation (SNESIM) was chosen as the simulation algorithm, and aeromagnetic data were used to support the analysis of simulated models. With comparison/analysis of the probabilistic ore model and geophysical data, the 3D geological model utilising MPS represented the configuration and distribution of the ore body well according to the geological structure. The SNESIM cluster results indicated high reliability for the final interpretation of the 3D models.

,

Multiple-point geostatistical simulation (MPS) was applied to develop 3D ore models matched to surrounding geological information accompanying aeromagnetic data using training image. The present study proposes a method for reducing the uncertainty of the 3D ore model, applying MPS to create probabilistic ore models and analysing the correlation between the models and geophysical data.

]
© 2018 ASEG
Loading

Article metrics loading...

/content/journals/10.1071/EG17171
2018-11-01
2026-01-13

  • From This Site

    /content/journals/10.1071/EG17171
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Arpat G. B. Caers J. 2007 Conditional simulation with patterns:Mathematical Geology3917720310.1007/s11004‑006‑9075‑3
     https://doi.org/10.1007/s11004-006-9075-3 [Google Scholar]
  2. Bae, K. H., 2000, 3D modeling and visualization of the geological distribution for the rock tunnelling design: M.Sc. thesis, Seoul National University.
  3. Barrera, A. E., 2006, Improved geological modeling and dynamic data integration using the probability perturbation approach: M.Sc. thesis, University of Texas at Austin.
  4. Campedel, M., Moulines, E., Matre, H., and Datcu, M., 2005, Feature selection for satellite image indexing: ESA-EUSC: Image Information Mining, Frascati, Italy, 1–5.
  5. Choi B. K. Choi S. G. Seo J. E. Yoo I. K. Kang H. S. Koo M. H. 2010 Mineralogical and geochemical characteristics of the Wolgok-Seongok orebodies in the Gagok skarn deposit: their genetic implications:Economic and Environmental Geology43477490
    [Google Scholar]
  6. Dickson N. E. Comte J. C. Renard P. Straubhaar J. A. Mckinley J. M. Ofterdinger U. 2015 Integrating aerial geophysical data in multiple-point statistics simulations to assist groundwater flow models:Hydrogeology Journal2388390010.1007/s10040‑015‑1258‑x
     https://doi.org/10.1007/s10040-015-1258-x [Google Scholar]
  7. Dovera, L., Caers, J., and Borgomano, J., 2006, MPG simulation of facies thicknesses interpreted through sequence stratigraphy – application on a carbonate outcrop: 10th European Conference on the Mathematics of Oil Recovery, EAGE.
  8. Dunkle, K. M., Anderson, M. P., and Hart, D. J., 2011, Multiple-point geostatistics for creation of 3D hydrostratigraphic models, Outagamie County, WI: Three-Dimensional Geological Mapping Workshop, Extended Abstracts, 23–27.
  9. Emerson D. W. 1986 Physical properties of skarns:Exploration Geophysics1720121210.1071/EG986201
     https://doi.org/10.1071/EG986201 [Google Scholar]
  10. Fogg G. E. Noyes C. D. Carle S. F. 1998 Geologically based model of heterogeneous hydraulic conductivity in an alluvial setting:Hydrogeology Journal613114310.1007/s100400050139
     https://doi.org/10.1007/s100400050139 [Google Scholar]
  11. Guardiano, F. B., and Srivastava, R. M., 1993, Multivariate geostatistics: beyond bivariate moments, in A. O. Soares, ed., Geostatistics Tróia ‘92: Kluwer Academic Publications, 133–144.
  12. Heo S. Y. Oh J. H. Yang K. H. Hwang J. Y. Park S. G. 2012 The relationship between the mineral characteristics and spectral induced polarization for the core rock samples from the Gagok skarn deposit:Economic and Environmental Geology4535136310.9719/EEG.2012.45.4.351
     https://doi.org/10.9719/EEG.2012.45.4.351 [Google Scholar]
  13. Honarkhah M. Caers J. 2010 Stochastic simulation of patterns using distance-based pattern modeling:Mathematical Geosciences4248751710.1007/s11004‑010‑9276‑7
     https://doi.org/10.1007/s11004-010-9276-7 [Google Scholar]
  14. Journel, A. G., 2007, Roadblocks to the evaluation of ore reserves – the simulation overpass and putting more geology into numerical models of deposits, in R. Dimitrakopoulos, ed., Orebody modeling and strategic mine planning: The Australasian Institute of Mining and Metallurgy, 14, 29–32.
  15. Journel, A. B., and Alabert, F. G., 1989, Focusing on spatial connectivity of extreme-valued attributes: stochastic indicator models of reservoir heterogeneities: SPE Annual Technical Conference, Expanded Abstracts, 18324.
  16. Journel A. Zhang T. 2006 The necessity of a multiple-point prior model:Mathematical Geology3859161010.1007/s11004‑006‑9031‑2
     https://doi.org/10.1007/s11004-006-9031-2 [Google Scholar]
  17. Koltermann C. E. Gorelick S. M. 1996 Heterogeneity in sedimentary deposits: a review of structure-imitating, process-imitation, and descriptive approaches:Water Resources Research322617265810.1029/96WR00025
     https://doi.org/10.1029/96WR00025 [Google Scholar]
  18. Korea Institute of Geoscience and Mineral Resources (KIGAM), 2014, Regional Geophysical Anomaly Mapping.
  19. Lee, K. B., 2014, Channelized reservoir characterization using ensemble smoother with a distance-based method: Ph.D. thesis, Seoul National University.
  20. Lee H. S. Rim H. R. Jung H. J. Jung H. K. Yang J., M 2010 Detecting steel pile using bore-hole 3-components fluxgate magnetometer:Journal of the Korean Earth Science Society3167368010.5467/JKESS.2010.31.7.673
     https://doi.org/10.5467/JKESS.2010.31.7.673 [Google Scholar]
  21. Mariethoz G. Renard P. Straubhaar J. 2010 The direct sampling method to perform multiple‐point geostatistical simulations:Water Resources Research46W11536 10.1029/2008WR007621
     https://doi.org/10.1029/2008WR007621 [Google Scholar]
  22. Oh S. H. 2005 RMR evaluation by integration of geophysical and borehole data using non-linear Indicator transform and 3D kriging:Journal of the Korean Earth Science Society26429435
    [Google Scholar]
  23. Okabe H. Blunt M. J. 2005 Pore space reconstruction using multiple-point statistics:Journal of Petroleum Science Engineering4612113710.1016/j.petrol.2004.08.002
     https://doi.org/10.1016/j.petrol.2004.08.002 [Google Scholar]
  24. Osterholt, V., and Dimitrakopoulos, R., 2007, Simulation of orebody geology with multiple-point geostatistics: application at Yandi channel iron ore deposit, WA and implications for resource uncertainty, in R. Dimitrakopoulos, ed., Orebody modeling and strategic mine planning: The Australasian Institute of Mining and Metallurgy, 14, 51–60.
  25. Park G. S. Cho S. J. Oh H. J. Lee C. W. 2014 Mineral potential mapping of Gagok Mine using 3D geological modeling:Journal of the Korean Earth Science Society3541242110.5467/JKESS.2014.35.6.412
     https://doi.org/10.5467/JKESS.2014.35.6.412 [Google Scholar]
  26. Remy, N., Boucher, A., and Wu, J., 2009, Applied geostatistics with SGeMS: a user’s guide: Cambridge University Press.
  27. Rezaee H. Asghari O. Koneshloo M. Ortiz J. M. 2014 Multiple-point geostatistical simulation of dykes: application at Sungun porphyry copper system, Iran:Stochastic Environmental Research and Risk Assessment281913192710.1007/s00477‑014‑0857‑8
     https://doi.org/10.1007/s00477-014-0857-8 [Google Scholar]
  28. Sammut, C., and Webb, G. I., 2011, Encyclopedia of machine learning: Springer.
  29. Scheidt, C., 2011, Distance-Based-Model-Selection, Stanford University. Available at https://github.com/scheidtc
  30. Scheidt C. Caers J. 2009 Representing spatial uncertainty using distances and kernels:Mathematical Geosciences4139741910.1007/s11004‑008‑9186‑0
     https://doi.org/10.1007/s11004-008-9186-0 [Google Scholar]
  31. Shin S. Park S. Kim H. R. 2013 Physical properties of rocks at the Gagok Skarn Deposit:Geophysics and Geophysical Exploration1618018910.7582/GGE.2013.16.3.180
     https://doi.org/10.7582/GGE.2013.16.3.180 [Google Scholar]
  32. Son Y. J. Kim J. D. 2012 Mine haulage system design for reopening of Yangyang iron mine using 3D modeling:Tunnel and Underground Space2241242810.7474/TUS.2012.22.6.412
     https://doi.org/10.7474/TUS.2012.22.6.412 [Google Scholar]
  33. Strebelle, S. B., 2000, Sequential simulation drawing structures from training images: Ph.D. thesis, Stanford University.
  34. Strebelle S. 2002 Conditional simulation of complex geological structures using multiple-point statistics:Mathematical Geology3412110.1023/A:1014009426274
     https://doi.org/10.1023/A:1014009426274 [Google Scholar]
  35. Strebelle, S. B., and Journel, A. G., 2001, Reservoir modeling using multiple-point statistics: SPE Annual Technical Conference and Exhibition, Expanded Abstracts, 71324.
  36. Yun S. Einaudi M. T. 1982 Zinc-lead skarns of the Yeonhwa-Ulchin district, South Korea:Economic Geology and the Bulletin of the Society of Economic Geologists771013103210.2113/gsecongeo.77.4.1013
     https://doi.org/10.2113/gsecongeo.77.4.1013 [Google Scholar]
  37. Zhang T. Switzer P. Journel A. 2006 Filter-based classification of training image patterns for spatial simulation:Mathematical Geology38638010.1007/s11004‑005‑9004‑x
     https://doi.org/10.1007/s11004-005-9004-x [Google Scholar]
/content/journals/10.1071/EG17171
Loading
Multiple-point geostatistical simulation for mine evaluation with aeromagnetic data
Exploration Geophysics 49, 952 (2018); https://doi.org/10.1071/EG17171
/content/journals/10.1071/EG17171
/content/journals/10.1071/EG17171
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): cluster analysis; geophysical data; mine evaluation; multiple-point geostatistics; training image

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error