Automatic rock recognition from drilling performance data. Zhou, H., Hatherly, P., Monteiro, S. T, Ramos, F., Oppolzer, F., Nettleton, E., & Scheding, S. In IEEE International Conference on Robotics and Automation (ICRA), pages 3407–3412, Saint Paul, MN, May, 2012. IEEE.
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Paper doi abstract bibtex
Paper doi abstract bibtex Automated rock recognition is a key step for building a fully autonomous mine. When characterizing rock types from drill performance data, the main challenge is that there is not an obvious one-to-one correspondence between the two. In this paper, a hybrid rock recognition approach is proposed which combines Gaussian Process (GP) regression with clustering. Drill performance data is also known as Measurement While Drilling (MWD) data and a rock hardness measure - Adjusted Penetration Rate (APR) is extracted using the raw data in discrete drill holes. GP regression is then applied to create a more dense APR distribution, followed by clustering which produces discrete class labels. No initial labelling is needed. Comparisons are made with alternative measures of rock hardness from MWD data as well as state-of-the-art GP classification. Experimental results from an actual mine site show the effectiveness of our proposed approach.
@InProceedings{zhou2012automatic,
Title = {Automatic rock recognition from drilling performance data},
Author = {Zhou, Hang and Hatherly, Peter and Monteiro, Sildomar T and Ramos, Fabio and Oppolzer, Florian and Nettleton, Eric and Scheding, Steve},
Booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
Year = {2012},
Address = {Saint Paul, MN},
Month = {May},
Organization = {IEEE},
Pages = {3407--3412},
Abstract = {Automated rock recognition is a key step for building a fully autonomous mine. When characterizing rock types from drill performance data, the main challenge is that there is not an obvious one-to-one correspondence between the two. In this paper, a hybrid rock recognition approach is proposed which combines Gaussian Process (GP) regression with clustering. Drill performance data is also known as Measurement While Drilling (MWD) data and a rock hardness measure - Adjusted Penetration Rate (APR) is extracted using the raw data in discrete drill holes. GP regression is then applied to create a more dense APR distribution, followed by clustering which produces discrete class labels. No initial labelling is needed. Comparisons are made with alternative measures of rock hardness from MWD data as well as state-of-the-art GP classification. Experimental results from an actual mine site show the effectiveness of our proposed approach.},
Doi = {10.1109/ICRA.2012.6224745},
Gsid = {Wp0gIr-vW9MC},
Url = {https://github.com/sildomar/sildomar.github.io/raw/master/files/Zhou_ICRA_2012.pdf}
}Downloads: 0
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