An Application of High-Dimensional Statistics to Predictive Modeling of Grade Variability
| Autor: | Alexander Novikov, Igor Grigoryev, Juri Hinz |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Source code
Computer science media_common.quotation_subject Listing (computer) computer.software_genre 01 natural sciences cross-validation Cross-validation Reduction (complexity) 010104 statistics & probability Lasso (statistics) Economic viability 0101 mathematics lasso media_common 010102 general mathematics lcsh:QE1-996.5 Logical consistency prediction artificial intelligence lcsh:Geology machine learning General Earth and Planetary Sciences Data mining High-dimensional statistics computer |
| Zdroj: | Geosciences, Vol 10, Iss 4, p 116 (2020) Geosciences Volume 10 Issue 4 |
| ISSN: | 2076-3263 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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