Skip to main content
SpringerLink
Log in

Quantitative analysis of geological ore-controlling factors and stereoscopic quantitative prediction of concealed ore bodies

  • Published:
Journal of Central South University of Technology Aims and scope Submit manuscript

Abstract

To address the issues for assessing and prospecting the replaceable resource of crisis mines, a geological ore-controlling field model and a mineralization distribution field model were proposed from the viewpoint of field analysis. By dint of solving the field models through transferring the continuous models into the discrete ones, the relationship between the geological ore-controlling effect field and the mineralization distribution field was analyzed, and the quantitative and located parameters were extracted for describing the geological factors controlling mineralization enrichment. The method was applied to the 3-dimensional localization and quantitative prediction for concealed ore bodies in the depths and margins of the Dachang mine in Guangxi, China, and the 3-dimensional distribution models of mineralization indexes and ore-controlling factors such as magmatic rocks, strata, faults, lithology and folds were built. With the methods of statistical analysis and the non-linear programming, the quantitative index set of the geological ore-controlling factors was obtained. In addition, the stereoscopic located and quantitative prediction models were set up by exploring the relationship between the mineralization indexes and the geological ore-controlling factors. So far, some concealed ore bodies with the resource volume of a medium-sized mineral deposit are found in the deep parts of the Dachang Mine by means of the deep prospecting drills following the prediction results, from which the effectiveness of the predication models and results is proved.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. LIU Liang-ming, PENG Sheng-lin. Key strategies for predictive exploration in mature environment: Model innovation, exploration technology optimization and information integration[J]. J Cent South Univ Technol, 2005, 12(2): 186–191.

    Article  Google Scholar 

  2. PENG Sheng-lin, YANG Mu, LIU Liang-ming. Approaches to location prognosis of concealed ore deposits (bodies) of productive mines[J]. J Cent South Univ Technol, 2002, 9(2): 112–117.

    Article  Google Scholar 

  3. BONHAM-CARTER G F, AGTERBERG F P, WRIGHT D F. Weights of evidence modelling: A new approach to mapping mineral potential[J]. Geological Survey of Canada, 1990, 89(9): 171–183.

    Google Scholar 

  4. AGTERBERG F P. Combining indicator patterns in weights of evidence modeling for resource evaluation[J]. Nonrenewable Resources, 1992, 1(1): 39–50.

    Article  Google Scholar 

  5. SINGER D A. Basic concepts in three-part quantitative assessments of undiscovered mineral resources[J]. Nonrenewable Resources, 1993, 2(2): 69–81.

    Article  Google Scholar 

  6. ZHAO Peng-da. Theories, principles, and methods for statistical prediction of mineral deposits[J]. Journal of Mathematical Geology, 1992, 24(6): 589–595.

    Article  MathSciNet  Google Scholar 

  7. WANG Shi-chen, CHEN Yong-liang, XIA Li-xian. Theory and method of mineral prediction using integrated information[M]. Beijing: Science Press, 2000: 65–180. (in Chinese)

    Google Scholar 

  8. ZHAO Peng-da. Three-component quantitative resource prediction and assessments: Theory and practice of digital mineral prospecting[J]. Earth Science — Journal of China University of Geosciences, 2002, 27(5): 482–489. (in Chinese)

    Google Scholar 

  9. XIAO Ke-yan, ZHU Yu-shen, SONG Guo-yao. GIS quantitative assessments of mineral resources[J]. Geology in China, 2000, 27(7): 29–32. (in Chinese)

    Google Scholar 

  10. HARRIS J R, WILKONSON L, GUNSKY E C. Effective use and interpretation of lithogeochemical data in regional mineral exploration programs: Application of Geographic Information Systems (GIS) technology[J]. Ore Geology Reviews, 2000, 16(3/4): 107–143.

    Article  Google Scholar 

  11. ASADI H H, HALE M. A predictive GIS model for mapping potential gold & base metal mineralization in Takab area, Iran[J]. Computers & Geosciences, 2001, 27(8): 901–912.

    Article  Google Scholar 

  12. CHEN Y. MRPM: Three visual basic programs for mineral resource potential mapping[J]. Computers & Geosciences, 2004, 30(7): 969–983.

    Article  Google Scholar 

  13. HARRRIS J R, SANBORN-BARRIE M, PANAGAPKO D A, SKULSKI T, PARKER J R. Gold prospectivity maps of the Red Lake greenstone belt: Application of GIS technology[J]. Canadian Journal of Earth Sciences, 2006, 43(7): 865–893.

    Article  Google Scholar 

  14. WANG Zhi-jing, CHENG Qiu-ming. GIS-based (W+-W−) weight of evidence model and its application to gold resources assessment in Abitibi, Canada[J]. Journal of China University of Geosciences, 2006, 17(1): 71–78.

    Article  MathSciNet  Google Scholar 

  15. LI Q, CHENG Q. VisualAnomaly: A GIS-based multifractal method for geochemical & geophysical anomaly separation in Walsh domain[J]. Computers & Geosciences, 2006, 32(5): 663–672.

    Article  Google Scholar 

  16. YE Tian-zu, XIAO Ke-yan, YAN Guang-shen. Methodology of deposit modeling and mineral resource potential assessment using integrated geological information[J]. Earth Science Frontiers, 2007, 14(5): 11–19. (in Chinese)

    Google Scholar 

  17. ZHOU W, CHEN G, LI H, LUO H, HUANG S L. GIS application in mineral resource analysis-A case study of offshore marine placer gold at Nome, Alaska[J]. Computers & Geosciences, 2007, 33(6): 773–788.

    Article  Google Scholar 

  18. FORD A, BLENKINSOP T G. Combining fractal analysis of mineral deposit clustering with weights of evidence to evaluate patterns of mineralization: Application to copper deposits of the Mount Isa Inlier, NW Queensland, Australia[J]. Ore Geology Reviews, 2008, 33(3/4): 435–450.

    Article  Google Scholar 

  19. CASSARD D, BILLA M, LAMBERT A, PICOT J C, HUSSON Y, LASSERRE J L, DELOR C. Gold predictivity mapping in French Guiana using an expert-guided data-driven approach based on a regional-scale GIS[J]. Ore Geology Reviews, 2008, 34(3): 471–500.

    Article  Google Scholar 

  20. CARRANZA E J M, RUITENBEEK F J A, HECKER C, MEIJDE M, MEER F D. Knowledge-guided data-driven evidential belief modeling of mineral prospectivity in Cabo de Gata, SE Spain[J]. International Journal of Applied Earth Observation and Geoinformation, 2008, 10(3): 374–387.

    Article  Google Scholar 

  21. WANG Gong-wen, CHEN Jian-ping. Mineral resource prediction and assessment of copper multi-mineral deposit based on GIS technology in the north of Sanjiang Region, China[J]. Earth Science Frontiers, 2008, 15(4): 27–32. (in Chinese)

    Article  MathSciNet  Google Scholar 

  22. MAO Xian-cheng, DAI Ta-gen, ZOU Yan-hong, WU Xiang-bing. Research and system development of geological mineral database of the Dachang orefield[J]. Geology and Prospecting, 2003, 39(5): 72–76. (in Chinese)

    Google Scholar 

  23. ZOU Yan-hong, MAO Xian-cheng. Establishment and application of geological survey database[J]. J Cent South Univ: Science and Technology, 2004, 35(3): 463–467. (in Chinese)

    Google Scholar 

  24. CEN Kuang, YU Chong-wen. Mass and energy transport in ore forming processes of sulfide deposits in Tongling district[J]. Geochimica, 2001, 30(6): 533–539. (in Chinese)

    Google Scholar 

  25. ZHAO C B, PENG S L, LIU L M, HOBBS B E, ORD A. Effective loading algorithm associated with explicit dynamic relaxation method for simulating static problems[J]. J Cent South Univ Technol, 2009, 16(1):125–130.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Geoscience and Environmental Engineering, Central South University, Changsha, 410083, China

    Xian-cheng Mao  (毛先成), Yan-hong Zou  (邹艳红), Xiao-qin Lu  (卢晓琴), Xiang-bin Wu  (吴湘滨) & Ta-gen Dai  (戴塔根)

  2. Key Laboratory of Metallogenic Prediction of Non-ferrous Metals of Ministry of Education, Central South University, Changsha, 410083, China

    Xian-cheng Mao  (毛先成), Yan-hong Zou  (邹艳红), Xiang-bin Wu  (吴湘滨) & Ta-gen Dai  (戴塔根)

Authors
  1. Xian-cheng Mao  (毛先成)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yan-hong Zou  (邹艳红)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiao-qin Lu  (卢晓琴)
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiang-bin Wu  (吴湘滨)
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ta-gen Dai  (戴塔根)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xian-cheng Mao  (毛先成).

Additional information

Foundation item: Project(2007CB416608) supported by the National Basic Research Program of China; Project(2006BAB01B07) supported by the National Science and Technology Pillar Program during the 11th Five-Year Plan Period

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mao, Xc., Zou, Yh., Lu, Xq. et al. Quantitative analysis of geological ore-controlling factors and stereoscopic quantitative prediction of concealed ore bodies. J. Cent. South Univ. Technol. 16, 987–993 (2009). https://doi.org/10.1007/s11771-009-0164-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11771-009-0164-6

Key words

Search

Navigation