Structural recognition and rock mass characterization in underground mines: A UAV and LiDAR map ping bas ed appr oach

Authors: Canales, CC; Sellers, ES

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This paper is hosted with the kind permission of the Universidad de Chile, Eighth International Conference & Exhibition on Mass Mining, 2020.


DOI https://doi.org/10.36487/ACG_repo/2063_97

Cite As:
Canales, CC & Sellers, ES 2020, 'Structural recognition and rock mass characterization in underground mines: A UAV and LiDAR map ping bas ed appr oach', in R Castro, F Báez & K Suzuki (eds), MassMin 2020: Proceedings of the Eighth International Conference & Exhibition on Mass Mining, University of Chile, Santiago, pp. 1302-1312, https://doi.org/10.36487/ACG_repo/2063_97

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Abstract:
The current geotechnical challenges in underground mines create the necessity for tools that can make safe and quick geological and geotechnical assessments, especially in hazard zones such as open stopes, in environments that are increasingly deep and confined. The purpose of this work was to test and validate the use of a new technology called Hovermap (HM). This tool combines the autonomous management of a drone (UAV) with the ability to generate 3D surface representations using LiDAR. This research shows the results of two case studies. The first case study is a comparison of data collected with Hovermap of a medium-quality scan with traditional methods such as Cavity Monitoring Surveys (CMS) and Core Logging to assess surface quality representation for geotechnical purposes. The results showed discordance between the RQD calculated with HM data and RQD calculated with traditional methods. Nevertheless, the origin of the problem is clear, and the solutions are also displayed. Once these results were validated, a second case study was performed with a higher quality scan and easier structural visualization for structural identification. The results were analysed to estimate the potential for use in underground mines for stability analysis. Furthermore, a series of drone flight parameters were estimated to perform scans depending on different purposes, such as velocity and wall distance and Sample Effort Variable (SEV).

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