Using GPR to Detect Bauxite Horizons in Laterite Deposits of Amazon Basin, Brazil

Barsottelli-Botelho, M.A.; Luiz, José Gouvêa

doi:10.3997/2214-4609.20148949

Cited by 3 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Of the geophysical subsurface imaging techniques, ground-penetrating radar (GPR), in particular, has recently gained greater acceptance in the mining industry because of its high-resolution and fast data acquisition capabilities (Davis and Annan, 1989). GPR has increasingly been used to acquire fine-scale complementary information about bauxite and nickel laterite profiles (Barsottelli-Botelho and Luiz, 2011; Francke, 2012a; Francke and Nobes, 2000; Francke and Parkinson, 2000; Watts, 1997). Once GPR data have been acquired from the ground of a survey area, the raw data must be processed and converted from the time domain to the depth domain (Keary and Brooks, 1994).…”

Section: Introductionmentioning

confidence: 99%

Incorporating fine-scale ground-penetrating radar data into the mapping of lateral variability of a laterite-type bauxite horizon

et al. 2014

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Incorporating fine-scale ground-penetrating radar data into the mapping of lateral variability of a laterite-type bauxite horizon

et al. 2014

View full text Add to dashboard Cite

show abstract

“…Another way to reduce the uncertainty in the contact surface models is the use of geophysical methods to contribute to the orebody delineation (Campbell 1994;Fallon et al 1997). Among the geophysical methods, ground penetrating radar (GPR) has been efficiently used to improve the delineation of ore/waste boundaries in lateritic ore bodies (Francke 2010(Francke , 2012bFrancké and Nobes 2000;Francke and Parkinson 2000;Francke and Utsi 2009;Francké and Yelf 2003;Barsottelli-Botelho and Luiz 2011;Dagasan et al 2018). However, GPR surveys alone cannot replace the traditional drilling due to their lack of accuracy.…”

mentioning

confidence: 99%

Pilot Point Optimization of Mining Boundaries for Lateritic Metal Deposits: Finding the Trade-off Between Dilution and Ore Loss

et al. 2018

View full text Add to dashboard Cite

Geological contacts in lateritic metal deposits (footwall topographies) often delineate the orebody boundaries. Spatial variations seen in such contacts are frequently higher than those for the metal grades of the deposit. Therefore, borehole spacing chosen based on the grade variations cannot adequately capture the geological contact variability. Consequently, models created using such boreholes cause high volumetric uncertainties in the actual and targeted ore extraction volumes, which, in turn, lead to high unplanned dilution and ore losses. In this paper, a method to design optimum ore/mining boundaries for lateritic metal deposits is presented. The proposed approach minimizes the dilution/ore losses and comprises two main steps. First, the uncertainty on the orebody boundary is represented using a set of stochastic realizations generated with a multiple-point statistics algorithm. Then, the optimal orebody boundary is determined using an optimization technique inspired by a model calibration method called Pilot Points. The pilot points represent synthetic elevation values and they are used to construct smooth mining boundaries using the multilevel B-Spline technique. The performance of a generated surface is evaluated using the expected sum of losses in each of the stochastic realizations. The Simulated Annealing algorithm is used to iteratively determine the pilot point values which minimize the expected losses. The results show a significant reduction in the dilution volume as compared to those obtained from the actual mining operation.

show abstract

Spatial Prediction of Lateral Variability of a Laterite-Type Bauxite Horizon Using Ancillary Ground-Penetrating Radar Data

et al. 2013

View full text Add to dashboard Cite

Using GPR to Detect Bauxite Horizons in Laterite Deposits of Amazon Basin, Brazil

Cited by 3 publications

References 0 publications

Incorporating fine-scale ground-penetrating radar data into the mapping of lateral variability of a laterite-type bauxite horizon

Incorporating fine-scale ground-penetrating radar data into the mapping of lateral variability of a laterite-type bauxite horizon

Pilot Point Optimization of Mining Boundaries for Lateritic Metal Deposits: Finding the Trade-off Between Dilution and Ore Loss

Spatial Prediction of Lateral Variability of a Laterite-Type Bauxite Horizon Using Ancillary Ground-Penetrating Radar Data

Contact Info

Product

Resources

About