A Geological Structure Mapping Tool using Photogrammetric Data

Yathunanthan Vasuki; Eun-Jung Holden; Peter Kovesi; Steven Micklethwaite

doi:10.1071/ASEG2013ab144

ISSN: 2202-0586
E-ISSN:

oa A Geological Structure Mapping Tool using Photogrammetric Data
Authors Yathunanthan Vasuki^a, Eun-Jung Holden^b, Peter Kovesi^c and Steven Micklethwaite^d
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^a Centre for Exploration Targeting, The University of Western Australia, , 35 Stirling Hwy, Crawley, , WA, 6009, , Australia, [email protected] ^b Centre for Exploration Targeting, The University of Western Australia, , 35 Stirling Hwy, Crawley, , WA, 6009, , Australia, [email protected] ^c Centre for Exploration Targeting, The University of Western Australia, , 35 Stirling Hwy, Crawley, , WA, 6009, , Australia, [email protected] ^d Centre for Exploration Targeting, The University of Western Australia, , 35 Stirling Hwy, Crawley, , WA, 6009, , Australia, [email protected]
Publisher: Australian Society of Exploration Geophysicists
Source: ASEG Extended Abstracts, Volume 2013, Issue ASEG2013 - 23rd Geophysical Conference, Dec 2013, p. 1 - 4
DOI: https://doi.org/10.1071/ASEG2013ab144
- Published online: 01 Dec 2013

Abstract

Accurate and efficient identification and mapping of geological structures has broad application across the minerals industry. Recent advances in data acquisition technologies using Unmanned Aerial Vehicles (UAV), have led to a growing interest in capturing high- resolution rock surface images and analysing those datasets remotely. However due to the large volumes of data that can be captured in a short flight, efficient analyses of these data brings new challenges.

We propose a semi-automated method that allows efficient mapping of geological structures using photogrammetry of rock surface data collected by UAV. Our method harnesses advanced automated image analysis techniques and human data interactions to identify structures and calculate dip and dip angles of structures. Geological features were detected in two dimensional (2D) images and the corresponding three dimensional (3D) features were automatically identified from 3D surface models. The location, dip and dip angle of geological features were then calculated.

A feature map generated by our semi-automated method correlates well with a fault map resulting from visual interpretation by an expert. Some advantages of our semi-automatic method include the following: Firstly; it generates results in few minutes whilst manual interpretation took around an hour, thus contributing significantly in time saving. Secondly; unlike manual interpretation, our software technology provides objective and consistent results that can be reproduced.

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References

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Article Type: Research Article

Keyword(s): Feature Detection; Image analysis; Photogrammetry

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