Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
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)
Silversides, K., Melkumyan, A.: Integration of downhole data sources with different resolution for improved boundary detection. In: 12th SEGJ International Symposium, Tokyo (2015)
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)
Silversides, K., Melkumyan, A., Hatherly, P., Wyman, D.: Boundary classification for automated geological modelling. In: 35th APCOM Symposium, pp. 133–120. AusIMM, Australia (2011)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Berlin (2006)
Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. Springer Science+Business Media, LLC, Heidelberg (2006)
Acknowledgements
This work has been supported by the Australian Centre for Field Robotics and the Rio Tinto Centre for Mine Automation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Silversides, K.L., Melkumyan, A. (2016). Gaussian Processes Based Fusion of Multiple Data Sources for Automatic Identification of Geological Boundaries in Mining. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-46681-1_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46680-4
Online ISBN: 978-3-319-46681-1
eBook Packages: Computer ScienceComputer Science (R0)