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

Erten, Oktay; Kizil, Mehmet S.; Topal, Erkan; McAndrew, Lachlan

doi:10.1007/s11053-013-9210-z

Cited by 13 publications

(6 citation statements)

References 27 publications

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“…In the first step, top elevation samples (main variable) from the sparse drillholes and the topographic surface points (secondary variable) derived from the available digital surface (or terrane) model are jointly modelled using ordinary cokriging. In the second step, the predicted values of the top elevation from step 1 are used as a secondary variable and the bauxite thickness samples from the sparse drillholes are used as a main variable in the cokriging system Erten et al (2013Erten et al ( , 2015. incorporated the complementary ground-penetrating radar (GPR) data into the prediction of the footwall contact variability measured at the sparsely spaced drillholes, and indicated that providing the GPR data is robust, the prediction of the shape of the contact becomes more accurate.…”

Section: Introductionmentioning

confidence: 99%

Modelling of bauxite seam attributes and quantifying in-situ ore volume uncertainty in the presence of geophysical information

Erten

Renard

McAndrew

2020

Applied Earth Science

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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.

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Section: Introductionmentioning

confidence: 99%

Modelling of bauxite seam attributes and quantifying in-situ ore volume uncertainty in the presence of geophysical information

Erten

Renard

McAndrew

2020

Applied Earth Science

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“…Subsequent investigations focused on highwall and open cut coal mining [24][25][26][27]. GPR has also been utilized to predict the subsurface horizon of an open cut bauxite mine in Weipa, Australia, to minimize ore dilution in the mining process [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

“…Erten et al [28] utilized an UltraGPR system with 80 MHz antenna and integrated RTK-GPS to acquire GPR data in a lateritic bauxite mine in Weipa, Australia. The purpose of that activity was to accurately estimate bauxite ore volume for the mine plan from sparsely spaced bore holes and improve grade control during the mining process.…”

Section: Introductionmentioning

confidence: 99%

Development of Digital Subterranean Models for Real-Time Open Cut Horizon Control

Strange

Jecny

2020

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A reliable coal seam sensing system is required to improve the productivity of selective mining in open-cut mining operations. A prototype system based upon commercial ground penetrating radar equipment, which measures coal thickness from the top of an exposed surface down to an underlying coal-interburden interface and generates digital subterranean models of the subsurface seam boundaries, was developed for this purpose. The models can be deployed to commercially available in-cab assistive guidance systems for bulldozers and other mining machinery in existing production processes, and can further contribute to the databases required for remote operation and control in a complete digital mine scenario. The system was evaluated at a production open cut coal mine in Queensland, Australia, with promising results. The benefits reported by operational personnel who evaluated the digital surface model in the mining environment provide strong motivation for ongoing technology development.

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“…This is mainly because the prediction performance of multivariate prediction techniques is based heavily on the strength of the linear correlation between the samples of primary and secondary variables at co-located locations (Erten, 2012). The complementary geophysical information can be incorporated into the kriging procedure using multivariate geostatistical modelling techniques, such as simple kriging with varying local means (SKLM), ordinary co-located cokriging (OCCK), ordinary cokriging (OCK), kriging with external drift (KED), and Bayesian integration (BAY) (Bardossy et al , 1986; Bourennane and King, 2003; Dowd and Pardo-Iguzquiza, 2006; Doyen, 1988; Erten et al , 2013; Kay and Dimitrakopoulos, 2000; Xu et al , 1992).…”

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

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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.

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

Cited by 13 publications

References 27 publications

Modelling of bauxite seam attributes and quantifying in-situ ore volume uncertainty in the presence of geophysical information

Modelling of bauxite seam attributes and quantifying in-situ ore volume uncertainty in the presence of geophysical information

Development of Digital Subterranean Models for Real-Time Open Cut Horizon Control

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

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