An application of high-dimensional statistics to predictive modeling of grade variability

Hinz, J; Grigoryev, I; Novikov, A

An application of high-dimensional statistics to predictive modeling of grade variability

Hinz, J Grigoryev, I Novikov, A

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Geosciences (Switzerland), 2020, 10, (4)
Issue Date:: 2020-04-01

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Field	Value	Language
dc.contributor.author	Hinz, J
dc.contributor.author	Grigoryev, I
dc.contributor.author	Novikov, A https://orcid.org/0000-0001-8529-173X
dc.date.accessioned	2020-10-15T05:39:32Z
dc.date.available	2020-10-15T05:39:32Z
dc.date.issued	2020-04-01
dc.identifier.citation	Geosciences (Switzerland), 2020, 10, (4)
dc.identifier.issn	2076-3263
dc.identifier.issn	2076-3263
dc.identifier.uri	http://hdl.handle.net/10453/143265
dc.description.abstract	© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The economic viability of a mining project depends on its efficient exploration, which requires a prediction of worthwhile ore in a mine deposit. In this work, we apply the so-called LASSO methodology to estimate mineral concentration within unexplored areas. Our methodology outperforms traditional techniques not only in terms of logical consistency, but potentially also in costs reduction. Our approach is illustrated by a full source code listing and a detailed discussion of the advantages and limitations of our approach.
dc.language	en
dc.publisher	MDPI AG
dc.relation.ispartof	Geosciences (Switzerland)
dc.relation.isbasedon	10.3390/geosciences10040116
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0403 Geology, 0907 Environmental Engineering, 0909 Geomatic Engineering
dc.title	An application of high-dimensional statistics to predictive modeling of grade variability
dc.type	Journal Article
utslib.citation.volume	10
utslib.for	0403 Geology
utslib.for	0907 Environmental Engineering
utslib.for	0909 Geomatic Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - QFRC - Quantitative Finance Research Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2020-10-15T05:39:23Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	10
utslib.citation.issue	4

Abstract:

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The economic viability of a mining project depends on its efficient exploration, which requires a prediction of worthwhile ore in a mine deposit. In this work, we apply the so-called LASSO methodology to estimate mineral concentration within unexplored areas. Our methodology outperforms traditional techniques not only in terms of logical consistency, but potentially also in costs reduction. Our approach is illustrated by a full source code listing and a detailed discussion of the advantages and limitations of our approach.

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http://hdl.handle.net/10453/143265