Applied Earth Science: Transactions of the Institutions of Mining and Metallurgy

This study focuses on the Malanjkhand Copper Mines area of Madhya Pradesh, India and compares different types of quartz, feldspar and mafic remote sensing indices used for the detection of quartz, feldspar and mafic minerals using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared (TIR) bands. Our data indicate that these lithological indices are useful for delineating quartz, feldspar or mafic minerals. Correlation matrices generated for various lithological indices indicate that both Guha's (GMI) and Ninomiya's (NMI) mafic indices yield nearly identical results. In addition, Guha's quartz index (GQI) yields significantly better results than Ninomiya's quartz index (NQI) during the identification of the quartz content. This study also shows that GQI is comparable with the Rockwall and Hofstra's quartz index (RHQI) for the identification of the quartz content. Hence, ASTER TIR data-based remote sensing indices play a key role in lithological mapping.

Mining operations demand accurate 3D geological models for resource estimation and production. The industry-wide accepted method for constructing such domains is to use exploration-based wireframes, limiting the accuracy of the domains by the sparsity of the exploration holes. Update of the domains using blast-hole data is highly desirable to increase the accuracy of the models and enable better decision making for downstream mining processes. We discuss some of the practical and theoretical problems of using existing boundaries created using exploration drill-hole data. We also propose using the blast-hole data to adjust ore to waste boundaries. The proposed methodology is evaluated on a dataset from an iron ore deposit located in the Brockman Iron Formation of the Hamersley Province, Western Australia, demonstrating important changes in multiple boundaries in this real-world mining dataset.

Mining projects often contain secondary data spatially correlated with the main variable of interest (primary). These secondary data are usually more densely sampled than the primary (heterotopic) dataset, as they are cheaper and faster to obtain. In this situation, the use of secondary data in geostatistical modelling improves the quality of the final estimated/simulated models. The main geostatistical methodology used to integrate these two types of data is cokriging, which requires the joint modelling of direct and cross variograms using the linear model of coregionalisation (LMC). This article shows a methodology for estimation/simulation with heterotopic secondary data that does not require the LMC. The spatial continuity will be described by covariance tables (direct and cross). A case study is presented to compare the proposed methodology with the estimates/simulations using the LMC. The results were satisfactory, as the estimated and simulated models with covariance tables were properly validated.

Several core plugs from a 1243 m-deep borehole penetrating the Westwater Canyon Member of the Morrison Formation in New Mexico (USA) have been investigated by various petrophysical methods to evaluate the potential of extracting deeply buried sandstone-hosted uranium deposits by in situ recovery (ISR). Petrophysics and mineralization patterns of the host rock in the target aquifer are crucial parameters for ISR amenability and were determined according to the ISR feasibility criteria. Mineralogical investigations revealed a secondary uranium mineralization mainly coating the silica grains of the arkosic sandstone and some interstitial pore fillings. The differences between mineralized and non-mineralized sandstone plugs are highlighted by nuclear magnetic resonance (NMR), X-ray microcomputed tomography, and spectral induced polarization (SIP). The NMR results show a doubling of the clay- and capillary-bound water and a decease of free-fluid porosity of mineralized samples. A correlation of mineralization grade and polarization is observed through the SIP investigations.

The precise prediction of the footwall variability of a lateritic bauxite seam is of critical importance for the quantification of ferricrete dilution and ore loss that is likely to occur during mining activity. However, the majority of bauxite deposits have economic drillhole intercepts that are too widely spaced to reflect the accurate contact variability, resulting in uncertainties in the in-situ ore volume and the characteristics of the ore being sent to the refinery. In a case study, the seam attributes were modelled using drillhole data and geophysical information through univariate and bivariate geostatistical approaches. The uncertainties in the volumes of ore, dilution and loss were assessed through conditional simulation. The results indicated that the in-situ ore volume was predicted more accurately when the secondary information was incorporated. The realisations suggested a high local variability in the footwall contact, which is the source of dilution and loss considering the selectivity and operating constraints.