3D laser imaging for joint orientation analysis

Mah, Jason; Samson, C.; McKinnon, Stephen D.

doi:10.1016/j.ijrmms.2011.04.010

“…In fact, although several authors have demonstrated the reliability of automatic and semi-automatic processing of imagery and 3-D point clouds for fracture mapping (Mah et al, 2011;Vöge et al, 2013;Assali et al, 2014;Vasuki et al, 2014), a complete manual approach was adopted in this analysis because in most cases the flat and regular morphology of quarry walls only allow photointerpretation of discontinuity traces. Moreover, final visual inspection and validation of outputs is always required, even when using codes for the semi-automatic extraction of joints .…”

Section: Discussionmentioning

confidence: 99%

Use of a remotely piloted aircraft system for hazard assessment in a rocky mining area (Lucca, Italy)

Salvini

¹

,

Mastrorocco

²

,

Esposito

³

et al. 2018

Nat. Hazards Earth Syst. Sci.

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Abstract. The use of remote sensing techniques is now common practice in different working environments, including engineering geology. Moreover, in recent years the development of structure from motion (SfM) methods, together with rapid technological improvement, has allowed the widespread use of cost-effective remotely piloted aircraft systems (RPAS) for acquiring detailed and accurate geometrical information even in evolving environments, such as mining contexts. Indeed, the acquisition of remotely sensed data from hazardous areas provides accurate 3-D models and high-resolution orthophotos minimizing the risk for operators. The quality and quantity of the data obtainable from RPAS surveys can then be used for inspection of mining areas, audit of mining design, rock mass characterizations, stability analysis investigations and monitoring activities. Despite the widespread use of RPAS, its potential and limitations still have to be fully understood.In this paper a case study is shown where a RPAS was used for the engineering geological investigation of a closed marble mine area in Italy; direct ground-based techniques could not be applied for safety reasons. In view of the re-activation of mining operations, high-resolution images taken from different positions and heights were acquired and processed using SfM techniques to obtain an accurate and detailed 3-D model of the area. The geometrical and radiometrical information was subsequently used for a deterministic rock mass characterization, which led to the identification of two large marble blocks that pose a potential significant hazard issue for the future workforce. A preliminary stability analysis, with a focus on investigating the contribution of potential rock bridges, was then performed in order to demonstrate the potential use of RPAS information in engineering geological contexts for geohazard identification, awareness and reduction.

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“…Most methods currently conduct the analysis on simplified 2.5D surface models, for example by using triangulated irregular networks (TINs) (Riquelme et al, 2014). Specifically in geological applications, however, this implication of converting 3D point clouds into 2.5D data might lead to an unwanted loss in detail and accuracy, as the representation of overhangs and concavities is not supported (Mah et al, 2011). Mah et al (2011), in this context, present a workflow involving the transformation of the point cloud into a 3D triangular mesh.…”

Section: State Of the Artmentioning

confidence: 99%

“…Specifically in geological applications, however, this implication of converting 3D point clouds into 2.5D data might lead to an unwanted loss in detail and accuracy, as the representation of overhangs and concavities is not supported (Mah et al, 2011). Mah et al (2011), in this context, present a workflow involving the transformation of the point cloud into a 3D triangular mesh. Riquelme et al (2014) recently proposed a method using the 3D information of the point cloud itself by examining every point and its local neighbors, respectively.…”

Section: State Of the Artmentioning

confidence: 99%

3D GEOLOGICAL OUTCROP CHARACTERIZATION: AUTOMATIC DETECTION OF 3D PLANES (AZIMUTH AND DIP) USING LiDAR POINT CLOUDS

Anders¹,

Hämmerle²,

Miernik³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Terrestrial laser scanning constitutes a powerful method in spatial information data acquisition and allows for geological outcrops to be captured with high resolution and accuracy. A crucial aspect for numerous geologic applications is the extraction of rock surface orientations from the data. This paper focuses on the detection of planes in rock surface data by applying a segmentation algorithm directly to a 3D point cloud. Its performance is assessed considering (1) reduced spatial resolution of data and (2) smoothing in the course of data pre-processing. The methodology is tested on simulations of progressively reduced spatial resolution defined by varying point cloud density. Smoothing of the point cloud data is implemented by modifying the neighborhood criteria during normals estimation. The considerable alteration of resulting planes emphasizes the influence of smoothing on the plane detection prior to the actual segmentation. Therefore, the parameter needs to be set in accordance with individual purposes and respective scales of studies. Furthermore, it is concluded that the quality of segmentation results does not decline even when the data volume is significantly reduced down to 10%. The azimuth and dip values of individual segments are determined for planes fit to the points belonging to one segment. Based on these results, azimuth and dip as well as strike character of the surface planes in the outcrop are assessed. Thereby, this paper contributes to a fully automatic and straightforward workflow for a comprehensive geometric description of outcrops in 3D.

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“…numerous planar surfaces, may be determined manually at the survey site. In reference to Mah et al (2011), though, the assumption is adopted that planar orientations derived from TLS acquired point cloud data may be of higher accuracy than orientation values determined by manual field measurements. Against this background and in respect of the purpose of this study, a reasonable means of validating the results of the study is the use of reference planes manually designated in the point cloud.…”

Section: Validationmentioning

confidence: 99%

3D GEOLOGICAL OUTCROP CHARACTERIZATION: AUTOMATIC DETECTION OF 3D PLANES (AZIMUTH AND DIP) USING LiDAR POINT CLOUDS

Anders

¹

,

Hämmerle

²

,

Miernik

³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Terrestrial laser scanning constitutes a powerful method in spatial information data acquisition and allows for geological outcrops to be captured with high resolution and accuracy. A crucial aspect for numerous geologic applications is the extraction of rock surface orientations from the data. This paper focuses on the detection of planes in rock surface data by applying a segmentation algorithm directly to a 3D point cloud. Its performance is assessed considering (1) reduced spatial resolution of data and (2) smoothing in the course of data pre-processing. The methodology is tested on simulations of progressively reduced spatial resolution defined by varying point cloud density. Smoothing of the point cloud data is implemented by modifying the neighborhood criteria during normals estimation. The considerable alteration of resulting planes emphasizes the influence of smoothing on the plane detection prior to the actual segmentation. Therefore, the parameter needs to be set in accordance with individual purposes and respective scales of studies. Furthermore, it is concluded that the quality of segmentation results does not decline even when the data volume is significantly reduced down to 10%. The azimuth and dip values of individual segments are determined for planes fit to the points belonging to one segment. Based on these results, azimuth and dip as well as strike character of the surface planes in the outcrop are assessed. Thereby, this paper contributes to a fully automatic and straightforward workflow for a comprehensive geometric description of outcrops in 3D.

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3D laser imaging for joint orientation analysis

Cited by 75 publications

References 16 publications

Use of a remotely piloted aircraft system for hazard assessment in a rocky mining area (Lucca, Italy)

Use of a remotely piloted aircraft system for hazard assessment in a rocky mining area (Lucca, Italy)

3D GEOLOGICAL OUTCROP CHARACTERIZATION: AUTOMATIC DETECTION OF 3D PLANES (AZIMUTH AND DIP) USING LiDAR POINT CLOUDS

3D GEOLOGICAL OUTCROP CHARACTERIZATION: AUTOMATIC DETECTION OF 3D PLANES (AZIMUTH AND DIP) USING LiDAR POINT CLOUDS

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