Gaussian Processes Based Fusion of Multiple Data Sources for Automatic Identification of Geological Boundaries in Mining

  • Katherine L. SilversidesEmail author
  • Arman Melkumyan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9950)


Mining stratified ore deposits such as Banded Iron Formation (BIF) hosted iron ore deposits requires detailed knowledge of the location of orebody boundaries. In one Marra Mamba style deposit, the alluvial to bedded boundary only creates distinctive signatures when both the magnetic susceptibility logs and the downhole chemical assays are considered. Identifying where the ore to BIF boundary occurs with the NS3-NS4 stratigraphic boundary requires both natural gamma logs and chemical assays. These data sources have different downhole resolutions. This paper proposes a Gaussian Processes based method of probabilistically processing geophysical logs and chemical assays together. This method improves the classification of the alluvial to bedded boundary and allows the identification of concurring stratigraphic and mineralization boundaries. The results will help to automatically produce more accurate and objective geological models, significantly reducing the need for manual effort.


Gaussian processes Signal processing Banded Iron Formation Geophysical logging Geochemical assay 



This work has been supported by the Australian Centre for Field Robotics and the Rio Tinto Centre for Mine Automation.


  1. 1.
    Thorne, S.W., Hagemann, S., Webb, A., Clout, J.: Banded iron formation-related iron ore deposits of the Hamersley Province, Western Australia. In: Hagemann, S., Rosiere, C., Gutzmer, J., Beukes N.J. (eds.) Banded Iron Formation-Related High Grade Iron Ore, Rev. Econ. Geol. 15, 197–221 (2008)Google Scholar
  2. 2.
    Borsaru, M., Zhoua, B., Aizawa, T., Karashima, H., Hashimoto, T.: Automated lithology prediction from PGNAA and other geophysical logs. Appl. Radiat. Isotopes 64, 272–282 (2006)CrossRefGoogle Scholar
  3. 3.
    Silversides, K., Melkumyan, A.: Integration of downhole data sources with different resolution for improved boundary detection. In: 12th SEGJ International Symposium, Tokyo (2015)Google Scholar
  4. 4.
    Silversides, K., Melkumyan, A., Wyman, D.: Fusing gaussian processes and dynamic time warping for improved natural gamma signal classification. Math. Geosci. 48, 187–210 (2016)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Silversides, K., Melkumyan, A., Hatherly, P., Wyman, D.: Boundary classification for automated geological modelling. In: 35th APCOM Symposium, pp. 133–120. AusIMM, Australia (2011)Google Scholar
  6. 6.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Berlin (2006)zbMATHGoogle Scholar
  7. 7.
    Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. Springer Science+Business Media, LLC, Heidelberg (2006)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Australian Centre for Field RoboticsUniversity of SydneySydneyAustralia

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