Geological layers detection and characterisation using high resolution 3D point clouds: example of a box-fold in the Swiss Jura Mountains

被引:22
作者
Humair, Florian [1 ]
Abellan, Antonio [1 ]
Carrea, Dario [1 ]
Matasci, Battista [1 ]
Epard, Jean-Luc [1 ]
Jaboyedoff, Michel [1 ]
机构
[1] Univ Lausanne, Inst Earth Sci, CH-1015 Lausanne, Switzerland
关键词
3D point clouds; geological layers; fold modelling; lithology discrimination; semi-automatic segmentation; STRUCTURE-FROM-MOTION; LIDAR; PHOTOGRAMMETRY; INTENSITY; UAV; VISUALIZATION; INTEGRATION; MULTISCALE; SURFACES; GEOMETRY;
D O I
10.5721/EuJRS20154831
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
The detection and characterisation of geological layers, as well as the precise quantification of their geometrical properties, is of primary interest in several domains of geology. Nevertheless, geological data gathering is commonly limited by access issues during fieldwork investigations. Here we present complementary and alternative tools aimed at allowing the investigation of areas with limited accessibility, such as vertical rock faces. We used 3D point clouds acquired from Terrestrial Laser Scanning and terrestrial photogrammetry to: 1) identify and model (reconstruct) the geometry of the geological layers and 2) semi-automatically segment the different lithologies according to their intensity signature. Our results show that the presented procedures are fast, reliable and efficient compared to traditional fieldwork. In particular, the geometrical analysis led to a very detailed characterisation of the bedding planes, which couldn't be targeted with sufficient precision based on fieldwork alone. The lithological mapping procedure, which was obtained through a semi-automatic segmentation process of a single intensity channel, is highly capable since nearly all the layers were correctly attributed to their corresponding lithology. The performance of the segmentation script was closely related to our prior fieldwork investigation, yielding validation of our semi-automatic point cloud segmentation.
引用
收藏
页码:541 / 568
页数:28
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