No abstract

Ground-penetrating radar (GPR) offers an inexpensive and rapid method for delineating the laterite profiles by acquiring fine-scale data from the ground. In a case study, a GPR survey was conducted at the Weipa bauxite mine in Australia, in which numerous pick points corresponding to the depth to the bauxite/ironstone boundary were acquired from the ground. These pick points were subsequently merged with the available exploration borehole data using four prediction algorithms, including standard linear regression (SLR), simple kriging with varying local means (SKLM), Bayesian integration (BAY), and ordinary co-located cokriging (OCCK). The required structural inputs for the aforementioned algorithms were derived from the modelled auto and cross-semi-variograms. The cross-validation results suggest that the SKLM approach yielded the most robust estimates. The comparison of these estimates with the actual mine floor also indicates that the inclusion of ancillary GPR data substantially improved the estimation quality.

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.