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Solid Earth An interactive open-access journal of the European Geosciences Union

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© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Method article
15 Aug 2017
Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Solid Earth (SE) and is expected to appear here in due course.
Rapid, semi-automatic fracture and contact mapping for point clouds, images and geophysical data
Samuel T. Thiele, Lachlan Grose, Anindita Samsu, Steven Micklethwaite, Stefan A. Vollgger, and Alexander R. Cruden School of Earth, Atmosphere and Environment, Monash University, Melbourne, 3800, Australia
Abstract. Two centuries ago William Smith produced the first geological map of England and Wales, an achievement that underlined the importance of mapping geological contacts and structures as perhaps the most fundamental skill set in earth science. The advent of large digital datasets from unmanned aerial vehicle (UAV) and satellite platforms now challenges our ability to extract information across multiple scales in a timely manner, often meaning that the full value of the data is not realised. Here we adapt a least-cost-path solver and specially tailored cost-functions to rapidly extract and measure structural features from point cloud and raster datasets. We implement the method in the geographic information system QGIS and the point cloud and mesh processing software CloudCompare. Using these implementations, the method can be applied to a variety of three-dimensional (3D) and two-dimensional (2D) datasets including high-resolution aerial imagery, virtual outcrop models, digital elevation models (DEMs) and geophysical grids.

We demonstrate the algorithm with four diverse applications, where we extract: (1) joint and contact patterns in high-resolution orthophotographs; (2) fracture patterns in a dense 3D point cloud; (3) earthquake surface ruptures of the Greendale Fault associated with the Mw7.1 Darfield earthquake (New Zealand) from high-resolution light detection and ranging (LiDAR) data, and; (4) oceanic fracture zones from bathymetric data of the North Atlantic. The approach improves the objectivity and consistency of the interpretation process while retaining expert-guidance, and achieves significant improvements (35–65 %) in digitisation time compared to traditional methods. Furthermore, it opens up new possibilities for data synthesis and can quantify the agreement between datasets and an interpretation.

Citation: Thiele, S. T., Grose, L., Samsu, A., Micklethwaite, S., Vollgger, S. A., and Cruden, A. R.: Rapid, semi-automatic fracture and contact mapping for point clouds, images and geophysical data, Solid Earth Discuss.,, in review, 2017.
Samuel T. Thiele et al.
Interactive discussionStatus: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version      Supplement - Supplement
RC1: 'Comment on manuscript se-2017-83', Andrea Bistacchi, 01 Oct 2017 Printer-friendly Version 
RC2: 'Review of Thiele et al.', Thomas Scheiber, 20 Oct 2017 Printer-friendly Version 
AC1: 'Authors response', Samuel Thiele, 03 Nov 2017 Printer-friendly Version Supplement 
Samuel T. Thiele et al.

Data sets

GeoTrace and Compass rapid trace-mapping (example data)
S. Thiele, S. Vollgger, and A. Samsu
Samuel T. Thiele et al.


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Latest update: 24 Nov 2017
Publications Copernicus
Short summary
We demonstrate a new method that enhances our ability to interpret large data sets commonly used in the earth sciences, including point-clouds and rasters. Implemented as plugins for CloudCompare and QGIS, we use a least-cost-path solver to track structures and contacts through data, allowing expert-guided interpretation in a way that seamlessly utilises computing power to optimise the interpretation process and improve objectivity and consistency.
We demonstrate a new method that enhances our ability to interpret large data sets commonly used...