1887
Volume 31, Issue 1-2
  • ISSN: 0812-3985
  • E-ISSN: 1834-7533

Abstract

Economic benefits can flow from geophysical logging at all stages of the mining cycle. The most commonly cited benefit is the substitution of delineation diamond drilling with cheaper percussion or reverse circulation drilling in cases where geophysical logs can substitute for core. This approach can deliver an attractive direct saving in drilling costs if drill metreage is unchanged, or a potentially greater indirect benefit from better resource delineation if more holes are drilled within the original drilling budget. Operational advantages of logging include data objectivity, speed of interpretation, and reduced core handling and analysis costs.

Substitution of diamond drilling with percussion drilling is not always feasible. However, the amount of core drilling which can be foregone in favour of more economical drilling plus logging expands enormously if grade can be reliably inferred from petrophysical logs. For some ore systems a close correlation can exist between a petrophysical parameter and grade. More commonly, individual petrophysical grade estimates are less reliable than assays, but the overall correlation with grade may still be adequate for discrimination between grade ranges, for example, ore versus waste, or low-grade versus high-grade. In order to exploit the potential for petrophysical grade estimation in these cases, efficient means must be found to infer grade ranges from borehole logs.

An automated interpretation tool, LogTrans, has been developed for a geophysical log analysis. LogTrans performs rapid analysis of multi-parameter logs and expedites presentation of interpreted results in a form meaningful to mining engineers and geologists. The LogTrans algorithm exploits the contrasts in petrophysical signatures between different classes of rock, distinguished by lithology, grade, mechanical properties, or a combination of characteristics. Interpretation entails two stages; the first, is a statistical characterisation, where geophysical logs and core-based geoscientific data from a set of control holes are combined to form an attribute lookup table. The second stage statistical discrimination, examines the measured physical properties at a particular depth and compares them to the lookup table for a rock class assignment.

In this paper we present two examples of petrophysical grade estimation, one from the Newlands Coal Mine and one from the Mount Isa underground copper operations.

© 2000 ASEG
Loading

Article metrics loading...

/content/journals/10.1071/EG00236
2000-03-01
2026-01-13

  • From This Site

    /content/journals/10.1071/EG00236
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Asten, M. W., 1983, Borehole log analysis using an interactive computer: Expl. Geophys., 14, 3-10.
  2. Baldwin, J. L., Bateman, R. M., and Wheatley, C. L., 1990, Application of a neural network to the problem of mineral identification from well logs: The Log Analyst, Sept.-Oct., 279-293.
  3. BPB Instruments, 1981, Coal interpretation manual, East Leake, England.
  4. Borsaru, M., Ceravolo, C., Charbucinski, J., Eisler, P., and Youl, S., 1986, Ash determination of black coal in exploration boreholes by the neutron-gamma method, in Killeen, P.G., Ed., Borehole geophysics for mining and geotechnical applications: Geological Survey of Canada, 261-268.
  5. Campbell, G., 1994, Geophysical contributions to mine-development planning - a risk reduction approach: 15th CMMI Congress, South African IMM, 3, 283-325.
  6. Cinq-Mars, A., Mwenifumbo, C. J., Killeen, P. G., and Chouteau, M., 1992, Multiparameter geophysical logging at the Yava lead deposit - a statistical approach: Geophys. Prosp., 40, 829-848.
  7. Conaway, J. G. and Killeen, P. G., 1978, Quantitative uranium determinations from gamma-ray logs by application of digital time series analysis: Geophysics, 43, 1204-1221.
  8. Coudert, L., Frappa, M. and Arias, R., 1994, A statistical method for litho-facies identification: J. Appl. Geophys., 32, 257-267.
  9. Edwards, K. W. and Banks, K. M., 1978, A theoretical approach to the evaluation of in-situ coal: CIM Bulletin, April, 124-131.
  10. Emilsson, J., 1993, Geophysical multi-parameter logging techniques applied to ore exploration in the Skellefte Field: M.Sc. thesis, Lulea Univ. of Technology.
  11. Fallon, G. N. and Fullagar, P. K., 1997, Optimising the drilling budget with geophysical logging: Proceedings of 3rd International Mine Geology Conference, Launceston, Aus. IMM Publ. 6/97, 167-174.
  12. Fallon, G. N., Fullagar, P. K. and Sheard, S. N., 1997, Application of geophysics in metalliferous mines: Austral. J. Earth Sci., 44, 391-409.
  13. Fullagar, P. K. and Fallon, G. N., 1997, Geophysics in metalliferous mines for orebody delineation and rock mass characterisation, in A.G. Gubins, Ed., Proceedings of Exploration ‘97: Fourth Decennial International Conference on Mineral Exploration, 573-584.
  14. Fullagar, P. K., Fallon, G. N., Hatherly, P. J. and Emerson, D. W., 1996a, Implementation of geophysics at metalliferous mines - AMIRA Project P436 Final Report: Centre for Mining Technology and Equipment, Brisbane, Report MM1-96/11.
  15. Fullagar, P. K., Fallon, G. N. and Zhou, B., 1996b, In-mine geophysical trials at Mt. Isa Mine: Centre for Mining Technology and Equipment, Brisbane, Report MM1-96/7.
  16. Fullagar, P. K., Fallon, G. N. and Zhou, B., 1998, Automated interpretation of geophysical borehole logs for orebody delineation and grade estimation: in Proceedings of 1998 Australian Mining Technology Conference, Fremantle, 292-307.
  17. Hattula, A., 1992, Borehole logging system at Outokumpu Enonkoski Ni mine, in From, T., Hattula, A., Malmstrom, L., Marinder, N.-E., Maki, T., and Silvennoinen, H., Eds, Analys i Borrhål, Samnordiskt Projekt, Gruv Teknik 2000, MITU Sweden Report 92:14 T.
  18. Kassenaar, J. D. C., 1991, An application of principal components analysis to borehole geophysical data: in Proceedings of 4th International MGLS/KEGS Symposium on Borehole Geophysics for Minerals, Geotechnical and Groundwater Applications, Toronto, 211-218.
  19. Killeen, P. G., 1997, Borehole geophysics - exploring the third dimension, in Gubins, A.G., Ed., Proceedings of Exploration ‘97: Fourth Decennial International Conference on Mineral Exploration, 31-42.
  20. Lavers, B. A. and Smits, L. J. M.,1976, Recent developments in coal petrophysics: SPWLA seventeenth annual logging symposium, Denver, Paper S, 1 - 19.
  21. McCracken, K. G., 1993, The application of geophysics to mine planning and operations: Final report, AMIRA Project P403.
  22. McCreary, R. G. and Wanstedt, S., 1995, Applications of multivariate statistics and pattern recognition to geophysical logging at Noranda: 6th International Symposium on Borehole Geophysics for Minerals, Geotechnical and Groundwater Applications, Santa Fe, New Mexico, 22-25.
  23. Nilsson, P., 1995, Pattern recognition techniques applied to borehole geophysical data in site investigations: MSc thesis, Lulea Univ. of Technology.
  24. Urbancic, T. I. and Bailey, R. C., 1988, Statistical techniques applied to borehole geophysical data in gold exploration: Geophys. Prosp., 36, 752-771.
  25. Venn, M., 1995, The electrical rock properties of the 1100 copper ore system, Mount Isa, Australia: M.Sc thesis, Monash Univ.
  26. Virkkunen, R. and Hattula, A., 1992, Borehole logging at LKAB Malmberget Fe Mine, in From, T., Hattula, A., Malmstrom, L., Marinder, N.-E., Maki, T., and Silvennoinen, H., Eds., Analys i Borrhål: Samnordiskt Projekt, Gruv. Teknik 2000, MITU Sweden Report 92:14 T.
  27. Wanstedt S., 1992, Geophysical logging applied to ore characterisation in the Zinkgruvan mine, Sweden: Expl. Geophys., 23, 401-406.
  28. Yang, Y. P. and Emerson, D. W., 1997, Electromagnetic conductivities of rock cores: Theory and analog results: Geophysics, 62,1779-1793.
  29. Zhang, Y., Salisch, H. A. and McPherson, J. G., 1999, Application of neural networks to identify lithofacies from well logs: Expl. Geophys., 30, 45-49.
/content/journals/10.1071/EG00236
Loading
  • Article Type: Research Article
Keyword(s): Coal; copper; geophysical logging; grade estimation; Mount Isa; multivariate statistics; Newlands

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