مورد إلكتروني
Machine Learning-Based Delineation of Geodomain Boundaries: A Proof-of-Concept Study Using Data from the Witwatersrand Goldfields
| العنوان: | Machine Learning-Based Delineation of Geodomain Boundaries: A Proof-of-Concept Study Using Data from the Witwatersrand Goldfields |
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| بيانات النشر: | Luleå tekniska universitet, Mineralteknik och metallurgi Geological Survey of Canada, 601 Booth Street, Ottawa, ON, K1A 0E8, Canada; Wits Mining Institute, University of the Witwatersrand, 1 Jan Smuts Ave., Johannesburg, 2000, South Africa Wits Mining Institute, University of the Witwatersrand, 1 Jan Smuts Ave., Johannesburg, 2000, South Africa Geological Survey of Canada, 601 Booth Street, Ottawa, ON, K1A 0E8, Canada; Wits Mining Institute, University of the Witwatersrand, 1 Jan Smuts Ave., Johannesburg, 2000, South Africa School of Chemistry, University of Lincoln, LN6 7TS, Lincoln, United Kingdom Department of Geology, University of the Free State, Bloemfontein, 9301, South Africa 2023 |
| تفاصيل مُضافة: | Zhang, Steven E. Nwaila, Glen T. Bourdeau, Julie E. Ghorbani, Yousef Carranza, Emmanuel John M. |
| نوع الوثيقة: | Electronic Resource |
| مستخلص: | Machine-aided geological interpretation provides an opportunity for rapid and data-driven decision-making. In disciplines such as geostatistics, the integration of machine learning has the potential to improve the reliability of mineral resources and ore reserve estimates. In this study, inspired by existing geostatistical approaches that use radial basis functions to delineate domain boundaries, we reformulate the problem into a machine learning task for automated domain boundary delineation to partition the orebody. We use an actual dataset from an operating mine (Driefontein gold mine, Witwatersrand Basin in South Africa) 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/models may be more desirable than others, beyond merely cross-validation performance metrics. 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 algorithm is also functionally the most resemblant of current approaches that makes use of radial basis functions. The delineated domains were subsequently used to demonstrate the effectiveness of domain delineation by comparing domain-based estimation versus non-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 the machine 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. Validerad;2023;Nivå 2;2023-07-20 (sofila);Funder: Department of Science and Innovation (DSI)-National Research Foundation (NRF) Thuthuka Grant (UID: 121973); DSI-NRF CIMERA; Wits Mining Institute (WMI) |
| مصطلحات الفهرس: | Domain delineation, Geodomains, Geometallurgy, Gold deposits, Machine learning, Resource estimation, Spatial data analytics, Computer Sciences, Datavetenskap (datalogi), Article in journal, info:eu-repo/semantics/article, text |
| DOI: | 10.1007.s11053-023-10159-7 |
| URL: | Natural Resources Research, 1520-7439, 2023, 32:3, s. 879-900 |
| الإتاحة: | Open access content. Open access content info:eu-repo/semantics/openAccess |
| ملاحظة: | application/pdf English |
| أرقام أخرى: | UPE oai:DiVA.org:ltu-95861 0000-0002-3952-3728 0000-0002-5228-3888 doi:10.1007/s11053-023-10159-7 ISI:000942687400001 Scopus 2-s2.0-85149260078 1399553835 |
| المصدر المساهم: | UPPSALA UNIV LIBR From OAIster®, provided by the OCLC Cooperative. |
| رقم الأكسشن: | edsoai.on1399553835 |
| قاعدة البيانات: | OAIster |
| DOI: | 10.1007.s11053-023-10159-7 |
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