100
ACKNOWLEDGMENTS
This work has been supported by the Australian Centre for Field Robotics and the Rio Tinto
Centre for Mine Automation.
REFERENCES
Bishop, C.M. 2006 Pattern Recognition and Machine Learning Berlin: Springer.
Dalstra, H.J. & Rosiere, C.A. 2008 Structural controls on high-grade iron ores hosted by banded iron
formation: a global perspective. In Hagemann, S., Rosiere, C., Gutzmer, J. & Beukes, N.J. (eds.)
Banded Iron Formation-Related High-Grade Iron Ore. Littleton, CO: Society of Econimic Geologists,
INC.
De-Vitry, C., Vann, J. & Arvidson, H. 2010 Multivariate iron ore deposit resource estimation – a
practitioner’s guide to selecting methods. Transactions of the Institution of Mining and Metallurgy
Section B, 119: 154–165.
Jones, H., Walraven, F. & Knott, G.G. 1973 Natural gamma logging as an aid to iron ore exploration
in the Pilbara region of Western Australia. Australasian Institute of Mining and Metallurgy Annual
Conference. Perth, Australia.
Rasmussen, C.E. & Williams, C.K.I. 2006 Gaussian Processes for Machine Learning, Massachusetts:
MIT press.
Silversides, K.L. & Melkumyan, A. 2018 Characterising Measure While Drilling data responses to
changes in rock hardness. IN Hron, K., Bábek, O., Fišerová, E. & Van Den Boogaart, R. (eds.) The
19th Annual Conference of the International Association for Mathematical Geosciences (IAMG2018).
Olomouc, Czech Republic.
Teale, R. 1965 The concept of specific energy in rock drilling. International Journal of Rock Mechanics
and Mining Sciences, 2: 57–73.
Thorne, W., Hagemann, S., Webb, A. & Clout, J. 2008 Banded iron formation-related iron ore deposits of the Hammersley Province, Western Australia. IN Hagemann, S., Rosiere, C., Gutzmer, J. &
Beukes, N. (eds.) Reviews in Economic Geology: Banded Iron Formation-Related High-Grade Iron Ore.
Westminster, CO, USA: Society of Economic Geologists.
Zhou, H., Hatherly, P., Ramos, F. & Nettleton, E. 2011 An Adaptive Data Driven Model for Characterizing Rock Properties from Drilling Data. 2011 IEEE International Conference on Robotics and
Automation (ICRA). Shanghai, China.
ACKNOWLEDGMENTS
This work has been supported by the Australian Centre for Field Robotics and the Rio Tinto
Centre for Mine Automation.
REFERENCES
Bishop, C.M. 2006 Pattern Recognition and Machine Learning Berlin: Springer.
Dalstra, H.J. & Rosiere, C.A. 2008 Structural controls on high-grade iron ores hosted by banded iron
formation: a global perspective. In Hagemann, S., Rosiere, C., Gutzmer, J. & Beukes, N.J. (eds.)
Banded Iron Formation-Related High-Grade Iron Ore. Littleton, CO: Society of Econimic Geologists,
INC.
De-Vitry, C., Vann, J. & Arvidson, H. 2010 Multivariate iron ore deposit resource estimation – a
practitioner’s guide to selecting methods. Transactions of the Institution of Mining and Metallurgy
Section B, 119: 154–165.
Jones, H., Walraven, F. & Knott, G.G. 1973 Natural gamma logging as an aid to iron ore exploration
in the Pilbara region of Western Australia. Australasian Institute of Mining and Metallurgy Annual
Conference. Perth, Australia.
Rasmussen, C.E. & Williams, C.K.I. 2006 Gaussian Processes for Machine Learning, Massachusetts:
MIT press.
Silversides, K.L. & Melkumyan, A. 2018 Characterising Measure While Drilling data responses to
changes in rock hardness. IN Hron, K., Bábek, O., Fišerová, E. & Van Den Boogaart, R. (eds.) The
19th Annual Conference of the International Association for Mathematical Geosciences (IAMG2018).
Olomouc, Czech Republic.
Teale, R. 1965 The concept of specific energy in rock drilling. International Journal of Rock Mechanics
and Mining Sciences, 2: 57–73.
Thorne, W., Hagemann, S., Webb, A. & Clout, J. 2008 Banded iron formation-related iron ore deposits of the Hammersley Province, Western Australia. IN Hagemann, S., Rosiere, C., Gutzmer, J. &
Beukes, N. (eds.) Reviews in Economic Geology: Banded Iron Formation-Related High-Grade Iron Ore.
Westminster, CO, USA: Society of Economic Geologists.
Zhou, H., Hatherly, P., Ramos, F. & Nettleton, E. 2011 An Adaptive Data Driven Model for Characterizing Rock Properties from Drilling Data. 2011 IEEE International Conference on Robotics and
Automation (ICRA). Shanghai, China.