| Titre | Machine learning-based delineation of geodomain boundaries: a proof-of-concept study using data from the Witwatersrand Goldfields |
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| Auteur | Zhang, S E ;
Nwaila, G T; Bourdeau, J E; Ghorbani, Y; Carranza, E J M |
| Source | Natural Resources Research vol. 32, 2023 p. 879-900, https://doi.org/10.1007/s11053-023-10159-7  Accès ouvert |
| Image |  |
| Année | 2023 |
| Séries alt. | Ressources naturelles Canada, Contribution externe 20230126 |
| Éditeur | Springer |
| Document | publication en série |
| Lang. | anglais |
| DOI | https://doi.org/10.1007/s11053-023-10159-7 |
| Media | papier; numérique; en ligne |
| Formats | pdf |
| Lat/Long OENS | 26.0000 28.0000 -26.0000 -28.0000 |
| Sujets | domaines structuraux; or; gisements minéraux; estimation des ressources; essais métallurgiques; Bassin de Witwatersrand ; l'apprentissage machine; minéraux métalliques; Sciences et technologie |
| Illustrations | cartes de localisation; tableaux; graphiques |
| Diffusé | 2023 03 02 |
| Résumé | (disponible en anglais seulement)
Machine-aided geological interpretation provides an opportunity for rapid and data-driven decisionmaking. Indisciplines such asgeostatistics, the
integrationofmachine learninghas thepotential toimprove the reliability of mineral resourcesandore reserve estimates. Inthis study, inspiredby existing geostatistical approaches that use radial basis functions to delineate domain boundaries, we
reformulate the problem into amachine learning task for automateddomainboundarydelineation topartition theorebody.Weuse an actual dataset fromanoperatingmine(Driefontein goldmine,WitwatersrandBasininSouthAfrica)to showcase our new method.Using
various machine learning algorithms, domain boundaries were created. We show that based on a combination of in-discipline requirements and heuristic reasoning, some algorithms/ modelsmaybemoredesirable
thanothers,beyondmerelycross-validationperformancemetrics. In particular, the support vector machine algorithm yielded simple (low boundary complexity) but geologically realistic and feasible domain boundaries. In addition to the empirical results,
the support vector machine algorithmis also functionally themost resemblant of current approaches that makes use of radial basis functions. The delineated domains were subsequently used to demonstrate the effectiveness of domain
delineationbycomparingdomain-based estimation versusnon-domain-based estimation using an identical automated workflow. Analysis of estimation results indicate that domain-based estimation is more likely to result in better metal reconciliation as
compared with non-domained based estimation. Through the adoption of themachine learning framework, we realized several benefits including: uncertainty quantification; domain boundary complexity tuning; automation; dynamic updates of models using new
data; and simple integration with existing machine learning-based workflows. |
| Sommaire | (Résumé en langage clair et simple, non publié)
Inspirés par les approches géostatistiques existantes qui utilisent des fonctions de base radiale pour délimiter les limites de domaine, nous
reformulons le problème en une tâche d'apprentissage automatique pour la délimitation automatisée des limites de domaine afin de partitionner un corps minéralisé. |
| GEOSCAN ID | 332002 |
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