- Title
- An application of high-dimensional statistics to predictive modeling of grade variability
- Creator
- Hinz, Juri; Grigoryev, Igor; Novikov, Alexander
- Date
- 2020
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/172080
- Identifier
- vital:14423
- Identifier
-
https://doi.org/10.3390/geosciences10040116
- Identifier
- ISBN:2076-3263 (ISSN)
- Abstract
- 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. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- Publisher
- MDPI AG
- Relation
- Geosciences (Switzerland) Vol. 10, no. 4 (2020), p.
- Rights
- http://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright @ 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0403 Geology; Artificial intelligence; Cross-validation; LASSO; Machine learning; Prediction
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