Published March 15, 2023 | Version v1
Conference paper Open

Mapping historical tailings dams using electrical resistivity tomography: a case study from the Tommy Burns mine, North Queensland

  • 1. Fender Geophysics, james.daniell@fendergeophysics.com.au
  • 2. Dover Castle Metals, mhaindl@dovercastlemetals.com.au
  • 3. Dover Castle Metals, awalsh@dovercastlemetals.com.au
  • 4. Fender Geophysics, andrew.sloot@fendergeophysics.com.au

Description

The Tommy Burns is a historical Tin/Tungsten mine located approximately 100km south-west of Cairns. The Tommy Burns mine was mined up until the mid-1980s with the grade of ore reaching 13% near the surface with decreasing Sn and increasing W at depth. LiDAR data from Tommy Burns indicates that there are approximately 20 tailings dams on site ranging in size from 25,000m2 to 200m2. Additional dams may also exist but are thought to be comparatively old and eroded and therefore not easily identified in the LiDAR data. Sand tailings from the Tommy Burns mine are known to contain significant concentrations of tin. Accurate mapping of the depth and extent of the tailings dams could be used to inform resource estimates and lead to the possible future reprocessing of these materials. Dover Castle Metals contracted Fender Geophysics to undertake an Electrical Resistivity Tomography (ERT) survey of the tailings dams to measure their depth to assess their resource potential. Fender Geophysics used a ZZgeo Universal 64 resistivity meter to acquire resistivity profiles over 20 of the tailings dams. Resistivity data were post-processed and underwent inversion using the Res2Dinv software. Most of the tailings were highly resistive and provided a significant contrast to the underlying alluvium. Dam depths estimated from the resistivity survey were up to 5m and agreed well with trenches that had been dug throughout the survey area. The success of the ERT survey over the tailings indicates that the technique may be useful for identifying additional tailings deposits that are not easily identified in the LiDAR data.

Notes

Open-Access Online Publication: May 29, 2023

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