Authors: Rees, KM; Graaf, J


DOI https://doi.org/10.36487/ACG_rep/1308_18_Rees

Cite As:
Rees, KM & Graaf, J 2013, 'Structural geology modelling: a summary on data integrity and modelling methods', in PM Dight (ed.), Slope Stability 2013: Proceedings of the 2013 International Symposium on Slope Stability in Open Pit Mining and Civil Engineering, Australian Centre for Geomechanics, Perth, pp. 315-323, https://doi.org/10.36487/ACG_rep/1308_18_Rees

Download citation as:   ris   bibtex   endnote   text   Zotero


Abstract:
The creation of a structural geology model greatly improves the geotechnical understanding of a deposit by giving meaning to the thousands of defect measurements collected. Structural geology models are all about geometry; geometry of the various rock packages and geometry of the overriding structural framework. Geotechnical models on the other hand deal in the details; the statistical defect set orientations, the spacing between the defects and their infill and surface characteristics. In order to get an overall picture of the rock mass, the geotechnical model needs to be assessed in association with the structural geology model and vice versa. Structural geology models incorporate a vast array of data types, including drillhole, televiewer, conventional geological mapping, photogrammetric geological mapping, etc., but how accurate and complete is all of this data? The integration of multiple data types into a structural geology model can be fraught with complications. Varying levels of accuracy occur between different datasets and these variants need to be assessed prior to structural geology modelling. Where conflict exists between multiple data types, the most accurate data should be used, thus it is important to understand data accuracy. Drillhole data for example can be affected by inaccurate orientation marks and survey pickups, leading to inaccurate structural measurements. Furthermore, the spacing and orientation of drillholes often does not provide a representative sample of the rock mass as the drill design is generally tailored to the geometry of the ore body for resource modelling. These factors need to be understood when using the data to create a structural geology model. While orientated drillhole data provides a good basis, integrating other data types such as geological mapping provides for a more robust model, however conventional geological modelling comes with its own set of issues. Advances in data collection techniques can enable many of the issues associated with poor data quality to be overcome. Televiewer data can be used to validate drill core logging data, while photogrammetry techniques can enable accurate digital geological maps to be created. However, regardless of the method employed, it is paramount that adequately trained personal carry out the mapping to ensure high quality data. The purpose of this paper is to discuss the quality of data used in structural geology modelling and present reasons for applying new technologies to your mining operation or resource assessment.

References:
2iC Australia Pty Ltd (2013a) Ezy-Mark, .
2iC Australia Pty Ltd (2013b) Verti-Ori, .
ARANZ Geo Ltd (2013) Leapfrog, .
Ballmark Chile (2013) Ballmark, .
Chalke, T., McGaughey, J. and Perron, G. (2012) 3D software technology for structural interpretation and modelling, in Proceedings Structural Geology and Resources 2012 Conference, J. Vearncombe (ed), 26–27 September 2012, Kalgoorlie, Australia, Australian Institute of Geoscientists, Brisbane, pp. 16–20.
Davis, B.K. and Cowan, E.J. (2012) Orientated Core – What the…?, in Proceedings Structural Geology and Resources 2012 Conference, J. Vearncombe (ed), 26–27 September 2012, Kalgoorlie, Australia, Australian Institute of Geoscientists, Brisbane, pp. 61–63.
Golder Associates (2013) FracMan version 7.4, .
Holcombe, R., Caughlin, T. and Oliver, N. (2012) Orientated Drillcore: Measurement and calculation procedures for structural and exploration geologists, HolcombeCoughlinOliver, viewed 15 July 2013,
downloads/HCO_oriented_core_procedures.pdf.
Imdex Limited (2013) Reflex, .
Paradigm Ltd (2013) GOCAD, .
Pashin, J.C., Jin, G., Zheng, C., Chen, S. and McIntyre, M.R. (2008) Discrete Fracture Network Models for Risk Assessment of Carbon Sequestration in Coal, Final Technical Report, Geological Survey of Alabama, viewed 9 August 2013,
bridge/servlets/purl/941131-PKaMA3/941131.pdf.
Rees, K. (2012) The benefits of using photogrammetry in the geological interpretation of the Cosmos nickel mine, in Proceedings Narrow Vein Mining 2012 Conference, S. Dominy (ed), 26–27 March 2012, Perth, Australia, The Australasian Institute of Mining and Metallurgy, Carlton, 145 p.
Schlumberger Ltd. (2013) Petrel, .




© Copyright 2022, Australian Centre for Geomechanics (ACG), The University of Western Australia. All rights reserved.
Please direct any queries or error reports to repository-acg@uwa.edu.au