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Geosphere; April 2008; v. 4; no. 2; p. 418-428; DOI: 10.1130/GES00145.1
© 2008 Geological Society of America
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Automatic detection of anisotropic features on rock surfaces

Bodey R. Baker1, Klaus Gessner2, Eun-Jung Holden3 and Andrew P. Squelch4

1 iVEC ‘The hub of advanced computing in Western Australia,’ The University of Western Australia, Crawley, Western Australia 6009, Australia
2 Centre for Exploration Targeting, School of Earth & Geographical Sciences, The University of Western Australia, Crawley, Western Australia 6009, Australia, and Computational Geoscience, Commonwealth Scientific and Industrial Research Organisation (CSIRO) Exploration and Mining, Kensington, Western Australia 6151, Australia
3 Centre for Exploration Targeting, School of Earth & Geographical Sciences, The University of Western Australia, Crawley, Western Australia 6009, Australia
4 iVEC ‘The hub of advanced computing in Western Australia,’ Australian Resources Research Centre, Kensington, Western Australia 6151, Australia, and Exploration Geophysics, Curtin University of Technology, Bentley, Western Australia 6102, Australia

Surface roughness is an important rock property that is measured for structural geology and engineering purposes. We have developed an automatic technique to detect anisotropic features on rock faces based on fractal analysis. The analysis method has been applied to synthetic surfaces, and to digitally mapped point clouds of natural rock surfaces shaped by weathering, fault wear, and mining. We illustrate the technique using field examples from Permian sandstones containing brittle shear zones in northeast England, the surface of a neotectonic fault in Turkey, Proterozoic quartzite from central Australia, and Devonian Quartzite in an aggregate quarry in Germany. Roughness analysis of these natural examples suggests that a significant change of roughness value, anisotropy, and anisotropy direction can exist across scale. Our analysis method represents a step toward developing a toolkit to automatically detect and interpret surface characteristics from digitally acquired data sets. It has widespread potential for applications in rock engineering and the geosciences.

Keywords: digital mapping • roughness • fault planes • photogrammetry • fractures







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