Random forest rock type classification with integration of geochemical and photographic data
Systematic manual and algorithmic classification workflows to characterize rock types are increasingly applied in the mineral exploration and mining industry, leveraging large systematically collected datasets. The aim of these are robust and repeatable classifications to aid more traditional visual...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | Applied Computing and Geosciences |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S259019742200012X |
| _version_ | 1811216030321606656 |
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| author | McLean Trott Matthew Leybourne Lindsay Hall Daniel Layton-Matthews |
| author_facet | McLean Trott Matthew Leybourne Lindsay Hall Daniel Layton-Matthews |
| author_sort | McLean Trott |
| collection | DOAJ |
| description | Systematic manual and algorithmic classification workflows to characterize rock types are increasingly applied in the mineral exploration and mining industry, leveraging large systematically collected datasets. The aim of these are robust and repeatable classifications to aid more traditional visual logging practices. This study uses random forest algorithms to examine the impacts of integrating distinct datasets with complementary characteristics; chemistry to enable compositional distinctions, and photography to enable textural distinctions. We use a random forest classifier to examine the accuracy metrics of models producing rock type classifications using these two data types independently and integrated together. Prediction accuracy, measured using 10-fold cross validation, was 87% for geochemical-only inputs, 85% for photographic-only inputs, and 90% for mixed inputs from both datasets. A mining and exploration project in the Late Miocene to early Pliocene porphyry belt in Chile is the site of this case study, where datasets were systematically acquired using in-field methods on historical drill-cores. Results indicate that classification of lithology is improved by integration of photography-based and composition-based feature inputs. We infer that the benefits of integration would increase in proportion with increasing compositional similarity between rock types. This approach might also be applied to similar geological problems, such as alteration or metallurgical classifications; and with somewhat distinct datatypes, such as geochemical interval data and photographic metric extraction from coincident intervals in core photos. |
| first_indexed | 2024-04-12T06:32:40Z |
| format | Article |
| id | doaj.art-93873a6ac3604edfb804990d7456330a |
| institution | Directory Open Access Journal |
| issn | 2590-1974 |
| language | English |
| last_indexed | 2024-04-12T06:32:40Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Applied Computing and Geosciences |
| spelling | doaj.art-93873a6ac3604edfb804990d7456330a2022-12-22T03:43:59ZengElsevierApplied Computing and Geosciences2590-19742022-09-0115100090Random forest rock type classification with integration of geochemical and photographic dataMcLean Trott0Matthew Leybourne1Lindsay Hall2Daniel Layton-Matthews3Department of Geological Sciences and Geological Engineering, Queen's University, 36 Union Street, Kingston, Ontario, K7L 3N6, Canada; GoldSpot Discoveries Corp., 64 Yonge Street, Suite 1010, Toronto, Ontario, M5B 1S8, Canada; Corresponding author. Department of Geological Sciences and Geological Engineering, Queen's University, 36 Union Street, Kingston, Ontario, K7L 3N6, Canada.Department of Geological Sciences and Geological Engineering, Queen's University, 36 Union Street, Kingston, Ontario, K7L 3N6, Canada; Arthur B. McDonald Canadian Astroparticle Physics Research Institute, Department of Physics, Engineering Physics & Astronomy, Queen's University, Kingston, Ontario K7L 3N6, CanadaGoldSpot Discoveries Corp., 64 Yonge Street, Suite 1010, Toronto, Ontario, M5B 1S8, CanadaDepartment of Geological Sciences and Geological Engineering, Queen's University, 36 Union Street, Kingston, Ontario, K7L 3N6, Canada; Arthur B. McDonald Canadian Astroparticle Physics Research Institute, Department of Physics, Engineering Physics & Astronomy, Queen's University, Kingston, Ontario K7L 3N6, CanadaSystematic manual and algorithmic classification workflows to characterize rock types are increasingly applied in the mineral exploration and mining industry, leveraging large systematically collected datasets. The aim of these are robust and repeatable classifications to aid more traditional visual logging practices. This study uses random forest algorithms to examine the impacts of integrating distinct datasets with complementary characteristics; chemistry to enable compositional distinctions, and photography to enable textural distinctions. We use a random forest classifier to examine the accuracy metrics of models producing rock type classifications using these two data types independently and integrated together. Prediction accuracy, measured using 10-fold cross validation, was 87% for geochemical-only inputs, 85% for photographic-only inputs, and 90% for mixed inputs from both datasets. A mining and exploration project in the Late Miocene to early Pliocene porphyry belt in Chile is the site of this case study, where datasets were systematically acquired using in-field methods on historical drill-cores. Results indicate that classification of lithology is improved by integration of photography-based and composition-based feature inputs. We infer that the benefits of integration would increase in proportion with increasing compositional similarity between rock types. This approach might also be applied to similar geological problems, such as alteration or metallurgical classifications; and with somewhat distinct datatypes, such as geochemical interval data and photographic metric extraction from coincident intervals in core photos.http://www.sciencedirect.com/science/article/pii/S259019742200012X |
| spellingShingle | McLean Trott Matthew Leybourne Lindsay Hall Daniel Layton-Matthews Random forest rock type classification with integration of geochemical and photographic data Applied Computing and Geosciences |
| title | Random forest rock type classification with integration of geochemical and photographic data |
| title_full | Random forest rock type classification with integration of geochemical and photographic data |
| title_fullStr | Random forest rock type classification with integration of geochemical and photographic data |
| title_full_unstemmed | Random forest rock type classification with integration of geochemical and photographic data |
| title_short | Random forest rock type classification with integration of geochemical and photographic data |
| title_sort | random forest rock type classification with integration of geochemical and photographic data |
| url | http://www.sciencedirect.com/science/article/pii/S259019742200012X |
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