A hybrid framework for modelling domains using quantitative covariates. (December 2022)

• Record Type: Journal Article
• Authors: Abildin, Yerniyaz; Xu, Chaoshui; Dowd, Peter; Adeli, Amir
• Is Part Of: Applied computing and geosciences. Volume 16(2022)
• Publication Date: 2022-12
• Subjects: Domain modelling for resource estimation -- Geological uncertainty -- Geostatistical simulation -- Machine learning -- Classification -- Noise filtering; Earth sciences -- Data processing -- Periodicals; 550.285
• DOI: 10.1016/j.acags.2022.100107
• Languages: English
• ISSNs: 2590-1974

Abstract

Abstract: Domains define the boundaries of mineralisation zones, within which the grade distribution of the target minerals can be quantified via an established mineral resource estimation procedure. Available domain modelling techniques include manual interpretation, implicit modelling and advanced geostatistical approaches. In mining applications, the most commonly used method is manual domaining, which is labour-intensive and prone to subjective judgement errors. In addition, the output is largely deterministic and ignores the significant uncertainty associated with the domain interpretation and boundary definitions. There is, therefore, a need for a more objective framework that can automatically define mineral domains and quantify the associated uncertainty. This paper describes such a framework, which consists of a hybrid approach based on simulated grade distributions and a machine learning (ML) classification technique, XGBoost, trained on lithological properties. Data from an Iron Oxide Copper Gold (IOCG) deposit are used as a case study to demonstrate the application of the proposed method. The study shows that the approach can handle complex multi-class problems with imbalanced features, and it can quantify the uncertainty of domain boundaries. A noise filtering method is used as a pre-processing step to improve the performance of the ML classification, especially in the case of problematic classes where domain boundaries are difficult to predict due to the similarity in geological characteristics. Highlights: Data-driven noise filtering is an effective tool against geological misclassification. Tree-based classification methods perform well for geological prediction. Imbalance problem between similar geological responses.