Authors: Howard, EJ; Bettison, MD

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Howard, EJ & Bettison, MD 2019, 'Selection of a representative dolerite waste sample for use in erosion assessments using remote sensing techniques', in AB Fourie & M Tibbett (eds), Mine Closure 2019: Proceedings of the 13th International Conference on Mine Closure, Australian Centre for Geomechanics, Perth, pp. 1139-1148,

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Rehabilitation of the W39 Overburden Storage Area (OSA) is currently being planned by BHP. The W39 OSA is located at the Mt Whaleback iron ore mine site in the Pilbara region of Western Australia. The proposed OSA batter design utilises Banded Iron Formation (BIF) waste to sheet the majority of the batter slopes except for a 20 m high section that is to be sheeted with durable, competent dolerite waste rock currently stockpiled on an adjacent OSA. The stockpile is 25 ha in size and contains dolerite with a range of particle sizes. Key to the erosion stability of the rehabilitation design is the erosion resistance of the dolerite, which in turn is largely a function of the dolerite’s particle size distribution. It is therefore critical that the materials selected for erosion testing from the 25 ha stockpile are representative of the material as a whole. In order to select a representative waste sample, a combination of remote sensing techniques was employed. Ortho-rectified imagery was sourced from a drone survey. The drone image was then divided into smaller 1-ha cells, and digital photographic techniques were used to determine the particle size distribution and mean rock size for each of the cells. The information for each cell was then combined to produce a map of changing mean rock size over the entire stockpile. From that mapping, representative locations were selected for testing, and areas of finer particle size were identified for exclusion from future rehabilitation works. This paper outlines the importance of selecting representative samples, details the methods used to select a representative dolerite waste sample, and provides results of the study.

Keywords: sampling, representativeness, drone imagery, remote sensing

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