Estimation of emerald mineralization probability using machine learning algorithms
This research proposes a machine learning (ML) model that estimates the probability of emerald mineralization in rocks of the Western Emerald Belt (CEOC). Element concentrations, lithologies and coordinates were used as input variables and productivity as the target variable (176 samples). The varia...
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| Format: | Article |
| Language: | English |
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Universidad Nacional de Colombia
2025-01-01
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| Series: | Dyna |
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| Online Access: | https://revistas.unal.edu.co/index.php/dyna/article/view/112504 |
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| author | Daniela Neva-Rodriguez Luis Hernán Ochoa-Gutierrez |
| author_facet | Daniela Neva-Rodriguez Luis Hernán Ochoa-Gutierrez |
| author_sort | Daniela Neva-Rodriguez |
| collection | DOAJ |
| description | This research proposes a machine learning (ML) model that estimates the probability of emerald mineralization in rocks of the Western Emerald Belt (CEOC). Element concentrations, lithologies and coordinates were used as input variables and productivity as the target variable (176 samples). The variables were transformed to be integrated into the model. (1) Variable selection was performed using the Boruta method and backward elimination. (2) A logistic regression, a neural network, and a support vector machine were trained. (3) Calibration was achieved with the Platt method. (4) Calibration assessment was conducted by using the Brier score and calibration curves. The model selected was a calibrated support vector machine (C = 0.19 and λ = 0.1) that included 17 geochemical variables and the coordinates. The results were presented in a 3D plot. Assigning a probability value to each sample allows the mining targets to be ranked.
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| format | Article |
| id | doaj-art-683b0c055c9a409aa0db754fbd5f422e |
| institution | DOAJ |
| issn | 0012-7353 2346-2183 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Universidad Nacional de Colombia |
| record_format | Article |
| series | Dyna |
| spelling | doaj-art-683b0c055c9a409aa0db754fbd5f422e2025-08-20T03:05:50ZengUniversidad Nacional de ColombiaDyna0012-73532346-21832025-01-019223510.15446/dyna.v92n235.112504Estimation of emerald mineralization probability using machine learning algorithmsDaniela Neva-Rodriguez0https://orcid.org/0009-0004-3254-4667Luis Hernán Ochoa-Gutierrez1https://orcid.org/0000-0002-3607-7339Universidad Nacional de Colombia, sede Bogotá, Facultad de Ciencias, Departamento de Geociencias, Bogotá, ColombiaUniversidad Nacional de Colombia, sede Bogotá, Facultad de Ciencias, Departamento de Geociencias, Bogotá, ColombiaThis research proposes a machine learning (ML) model that estimates the probability of emerald mineralization in rocks of the Western Emerald Belt (CEOC). Element concentrations, lithologies and coordinates were used as input variables and productivity as the target variable (176 samples). The variables were transformed to be integrated into the model. (1) Variable selection was performed using the Boruta method and backward elimination. (2) A logistic regression, a neural network, and a support vector machine were trained. (3) Calibration was achieved with the Platt method. (4) Calibration assessment was conducted by using the Brier score and calibration curves. The model selected was a calibrated support vector machine (C = 0.19 and λ = 0.1) that included 17 geochemical variables and the coordinates. The results were presented in a 3D plot. Assigning a probability value to each sample allows the mining targets to be ranked. https://revistas.unal.edu.co/index.php/dyna/article/view/112504gemscalibrationdrillholemineral target |
| spellingShingle | Daniela Neva-Rodriguez Luis Hernán Ochoa-Gutierrez Estimation of emerald mineralization probability using machine learning algorithms Dyna gems calibration drillhole mineral target |
| title | Estimation of emerald mineralization probability using machine learning algorithms |
| title_full | Estimation of emerald mineralization probability using machine learning algorithms |
| title_fullStr | Estimation of emerald mineralization probability using machine learning algorithms |
| title_full_unstemmed | Estimation of emerald mineralization probability using machine learning algorithms |
| title_short | Estimation of emerald mineralization probability using machine learning algorithms |
| title_sort | estimation of emerald mineralization probability using machine learning algorithms |
| topic | gems calibration drillhole mineral target |
| url | https://revistas.unal.edu.co/index.php/dyna/article/view/112504 |
| work_keys_str_mv | AT danielanevarodriguez estimationofemeraldmineralizationprobabilityusingmachinelearningalgorithms AT luishernanochoagutierrez estimationofemeraldmineralizationprobabilityusingmachinelearningalgorithms |