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BIF-hosted deposit unit differentiation using multivariate Gaussian processes on measure while drilling data

BIF-hosted deposit unit differentiation using multivariate Gaussian processes on measure while... Measure while drilling (MWD) data collected from production holes can provide information on the location of stratigraphic units in banded iron formation-hosted iron ore deposits. Stratigraphic modelling in these deposits is typically based on data from exploration holes, and adding more densely spaced production data can potentially increase model detail at the bench scale. Previous MWD classification methods struggle to differentiate between neighbouring ore units. In this paper, multivariate Gaussian Processes (GPs) were applied to locate the contact between two iron ore units in the Dales Gorge Member in the Brockman Iron Ore Formation. Production MWD points were then labelled based on the GP output. By altering parameters of the labelling process, 24.4–49.4% of the test data were labelled, with accuracies from 81.4 to 86.8%. Classifications from the same hole were compared to ensure MWD label consistency. The results demonstrate that the proposed method can improve geological unit classification from MWD data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Earth Science Taylor & Francis

BIF-hosted deposit unit differentiation using multivariate Gaussian processes on measure while drilling data

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BIF-hosted deposit unit differentiation using multivariate Gaussian processes on measure while drilling data

Abstract

Measure while drilling (MWD) data collected from production holes can provide information on the location of stratigraphic units in banded iron formation-hosted iron ore deposits. Stratigraphic modelling in these deposits is typically based on data from exploration holes, and adding more densely spaced production data can potentially increase model detail at the bench scale. Previous MWD classification methods struggle to differentiate between neighbouring ore units. In this paper,...
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Publisher
Taylor & Francis
Copyright
© 2020 Institute of Materials, Minerals and Mining and The AusIMM
ISSN
2572-6838
eISSN
2572-6846
DOI
10.1080/25726838.2020.1829253
Publisher site
See Article on Publisher Site

Abstract

Measure while drilling (MWD) data collected from production holes can provide information on the location of stratigraphic units in banded iron formation-hosted iron ore deposits. Stratigraphic modelling in these deposits is typically based on data from exploration holes, and adding more densely spaced production data can potentially increase model detail at the bench scale. Previous MWD classification methods struggle to differentiate between neighbouring ore units. In this paper, multivariate Gaussian Processes (GPs) were applied to locate the contact between two iron ore units in the Dales Gorge Member in the Brockman Iron Ore Formation. Production MWD points were then labelled based on the GP output. By altering parameters of the labelling process, 24.4–49.4% of the test data were labelled, with accuracies from 81.4 to 86.8%. Classifications from the same hole were compared to ensure MWD label consistency. The results demonstrate that the proposed method can improve geological unit classification from MWD data.

Journal

Applied Earth Science – Taylor & Francis

Published: Oct 1, 2020

Keywords: Hamersley region; machine learning; modelling; iron ore; stratigraphy

References

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