Vom 20.12.2025 bis 11.01.2026 ist die Universitätsbibliothek geschlossen. Ab dem 12.01.2026 gelten wieder die regulären Öffnungszeiten. Ausnahme: Medizinische Hauptbibliothek und Zentralbibliothek sind bereits ab 05.01.2026 wieder geöffnet. Weitere Informationen

Treffer: An Integrated Geodata Science Workflow for Resource Estimation: A Case Study from the Merensky Reef, Bushveld Complex.

Title:
An Integrated Geodata Science Workflow for Resource Estimation: A Case Study from the Merensky Reef, Bushveld Complex.
Source:
Natural Resources Research; Jun2025, Vol. 34 Issue 3, p1301-1329, 29p
Database:
Complementary Index

Weitere Informationen

Integrated workflows for mineral resource estimation from exploration to mining must be able to process typical geodata (e.g., borehole data), perform data engineering (e.g., geodomaining), and spatial modeling (e.g., block modeling). Several methods exist, however they can only handle individual subtasks, and are either semi or fully automatable. Thus, an integrated workflow has not been established, which is needed to handle bigger geodata sets, perform remote monitoring, or provide short-term operational feedback. Bigger (more voluminous, higher velocity and higher dimensional) geodata sets are both emerging and anticipated in future exploration and mining operations, necessitating a geodata science counterpart to traditional, segregated, and routinely manual geostatistical workflows for resource estimation. In this paper, we demonstrate a prototype that integrates various data processing, pointwise geodomaining, domain boundary delineation, combinatorics-based visualization, and geostatistical modeling methods to create a modern resource estimation workflow. For the purpose of geodomaining, we employed a fully semi-automated, machine learning-based workflow to perform spatially aware geodomaining. We demonstrate the effectiveness of the method using actual mining data. This workflow makes use of methods that are properly geodata science-based as opposed to merely data science-based (explicitly leverages the spatial aspects of data). The workflow achieves these benefits through the use of objective metrics and semi-automated modeling practices as part of geodata science (e.g., cross-validation), enabling high automation potential, practitioner-agnosticism, replicability, and objectivity. We also evaluate the integrated resource estimation workflow using a real dataset from the platiniferous Merensky Reef of the Bushveld Complex (South Africa) known for its high nugget effect. [ABSTRACT FROM AUTHOR]

Copyright of Natural Resources Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)