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