The value of imperfect borehole information in mineral resource evaluation

Abstract In mineral resource evaluation a careful analysis and assessments of the geology, assay data and structural data is performed. One critical question is where to position the exploration boreholes that render it possible to classify as much of the deposit as possible as a measured or indicated resource. Another important question is what method to use when analyzing the grade in the collected material. For the deposit we consider, a challenge is to assess whether one should analyze the collected core samples with accurate and expensive XRF equipment or the less accurate and less expensive XMET equipment. A dataset of 1,871 XMET and 103 XRF observations is available, along with relevant explanatory variables. At the 103 sites where XRF data is acquired, 103 XMET measurements are also available. We first derive estimates of the regression and covariance parameters of a Gaussian random field model for the log XMET and log XRF data. Next, the model is used to predict the decisive grade parameter on block support. To improve the predictions, the mining company has planned to drill and collect 265 core samples along new boreholes. The associated reduction in prediction variance, with XRF or XMET data collection, is studied. Moreover, we compute the value of the XRF or XMET information using the statistical model, the expected development costs and revenues. The value of information is a useful diagnostic here, comparing the actual price of the XRF or XMET data with its added value

Geometallurgical Approach to the Element-to-Mineral Conversion for the Nabbaren Nepheline Syenite Deposit

Nabbaren nepheline syenite, a silica-deficient intrusive rock with low Fe content, was the industrial mineral deposit study case in this study. The quality of industrial mineral products are generally based on their bulk chemistry, which are directly related to their modal mineralogy and mineral chemistry; however, these are costly and time-consuming to determine. A geometallurgical-based methodology, known as element-to-mineral conversion (EMC), was applied to estimate its modal mineralogy based on its given bulk and mineral chemistry. EMC is a convenient and cost-effective technique, which can be used to quickly estimate modal mineralogy. Two EMC methodologies were applied: one least square based, LS-XRD, and one regression based, R-XRD. Additionally, average and specific mineral chemistries were used during estimations. The R-XRD method, a method not yet used for EMC purposes, gave better modal mineralogy estimations than LS-XRD. Considering the restrictions in the method, R-XRD shows potential for improvement and implementation at operational scale, making it a valuable geometallurgical tool for increasing resource performance, easing decision-taking processes, and reducing risks. The use of different mineral chemistries did not influence the modal mineralogy estimation, unlike the method used for it

Integrated Spatiotemporal Analysis of Vegetation Condition in a Complex Post-Mining Area: Lignite Mine Case Study

The motivation for this study arises from the need to monitor the condition of a rehabilitated post-mining areas even decades after the end of the recovery phase. This can be facilitated with satellite derived spectral vegetation indices and Geographic Information System (GIS) based spatiotemporal analysis. The study area described in this work is located in Western Poland and has unique characteristics, as it was subjected to the combined underground and open pit mining of lignite deposits that had been shaped by glaciotectonic processes. The mining ended in early 1970’ties and the area was subjected to reclamation procedures that ended in the 1980’ties. We used the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) spectral indices derived from Sentinel-2 data for the 2015–2022. period. Then, we applied a combination of GIS-based map algebra statistics (local, zonal and combinatorial) and GI* spatial statistics (hot spot and temporal hot spot) for a complex analysis and assessment of the vegetation cover condition in a post-mining area thought to be in the rehabilitated phase. The mean values of NDVI and EVI for the post-mining study area range from 0.48 to 0.64 and 0.24 to 0.31 and are stable in the analyzed 8 year period. This indicates general good condition of the vegetation and post-recovery phase of the area of interest. However, the combination of spatiotemporal analysis allowed us to identify statistically significant clusters of higher and lower values of the vegetation indices and change of vegetation cover classes on 3% of the study area. These clusters signify the occurrence of local processes such as, the encroachment of aquatic vegetation in waterlogged subsidence basins, and growth of low vegetation in old pits filled with waste material, barren earth zones on external waste dumps, as well as present-day forest management activities. We have confirmed that significant vegetation changes related to former mining occur even five decades later. Furthermore, we identified clusters of the highest values that are associated with zones of older, healthy forest and deciduous tree species. The results confirmed applicability of Sentinel-2 derived vegetation indices for studies of post-mining environment and for the detection of local phenomena related to natural landscaping processes still taking place in the study area. The methodology adopted for this study consisting of a combination of GIS-based data mining methods can be used in combination or separately in other areas of interest, as well as aid their sustainable management

Underwater Hyperspectral Imaging Using a Stationary Platform in the Trans-Atlantic Geotraverse Hydrothermal Field

Underwater hyperspectral imaging is a relatively new method for characterizing seafloor composition. To date, it has been deployed from moving underwater vehicles, such as remotely operated vehicles and autonomous underwater vehicles. While moving vehicles allow relatively rapid surveying of several 10-1000 m², they are subjected to short-term variations in vehicle attitude that often compromise image acquisition and quality. In this study, we tested a stationary platform that was landed on the seabed and used an underwater hyperspectral imager (UHI) on a vertical swinging bracket. The imaged seafloor areas have dimensions of 2.3 m x 1 m and are characterized by very stable UHI data of high spatial resolution. The study area was the Trans-Atlantic Geotraverse hydrothermal field at the Mid-Atlantic Ridge (26° N) in water depths of 3530-3660 m. UHI data were acquired a 12 stations on an active and an inactive hydrothermal sulfide mound. Based on supervised classification, 24 spectrally different seafloor materials were detected, including hydrothermal and non-hydrothermal materials, and benthic fauna. The results show that the UHI data are able to spectrally distinguish different types of surface materials and benthic fauna in hydrothermal areas, and may therefore represent a promising tool for high-resolution seafloor exploration in potential future deep-sea mining areas