Automated mapping of rock discontinuities in 3D lidar and photogrammetry models

Lato, Matthew J.; Vöge, Malte

doi:10.1016/j.ijrmms.2012.06.003

Cited by 162 publications

(70 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The algorithm developed in the frame of Miernik et al (2013) is improved and applied in this study and will be explained in detail in Section 3. Lato and Vöge (2012) include a data processing step in their approach prior to the actual segmentation with the purpose of eliminating surface roughness artefacts, which largely depend on the scale of the study object, its geometric features, and also the data capturing method. Lato and Vöge (2012) additionally mask out sharp edges to exclude large deviations in normal direction from the actual segmentation process.…”

Section: State Of the Artmentioning

confidence: 99%

“…Lato and Vöge (2012) include a data processing step in their approach prior to the actual segmentation with the purpose of eliminating surface roughness artefacts, which largely depend on the scale of the study object, its geometric features, and also the data capturing method. Lato and Vöge (2012) additionally mask out sharp edges to exclude large deviations in normal direction from the actual segmentation process. In their workflow, data smoothing is a powerful means of generating a more thorough representation of planar surfaces in the outcrop data and is subsequently also examined in our study.…”

Section: State Of the Artmentioning

confidence: 99%

“…Furthermore, small-scale roughness artefacts may derive from remaining errors owing to the registration of various scans from different positions (Lato and Vöge, 2012). It might, hence, be expedient to smooth the surfaces of input data before applying a plane detection algorithm.…”

Section: Smoothingmentioning

confidence: 99%

“…Moreover, TLS enables mapping of otherwise difficult or dangerous to reach and inaccessible areas (Fisher et al, 2014). It is therefore attractive not only for research in the field of geology, but also for practical use in engineering and related fields, for example for geomechanical evaluation or determination of slope instabilities Fekete and Diederichs, 2013;Lato and Vöge, 2012). An essential component of outcrop characterization and important step for further analyses is the geometric description of the outcrop surface via azimuth and dip.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous established algorithms exist for point In order to obtain optimal results in geological applications, numerous methodologies have been proposed in recent years which are specifically designed for the application on rock mass data (Riquelme et al, 2014). This diverging development is due to rock material commonly being of irregular shape, featuring varying amounts of surface roughness (on different spatial scales) and potentially occurring at any orientation within the data (Lato and Vöge, 2012).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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.

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.

show abstract

Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: Smoothingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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.

View full text Add to dashboard Cite

show abstract

Digital Outcrop Model Reconstruction and Interpretation

Bistacchi¹,

Mittempergher²,

Martinelli³

2022

3D Digital Geological Models

View full text Add to dashboard Cite

Beishan exploration tunnel surrounding rock discontinuity identification based on structure from motion photogrammetry technology

Xuan,

Zhang,

et al. 2024

Engineering Reports

View full text Add to dashboard Cite

The Beishan Exploration Tunnel (BET) is a facility built to develop technologies associated with the safety of China's first high‐level radioactive nuclear waste(HLW) disposal. The surrounding rock discontinuity identification is a key research topic in BET, which could provide essential geological data for future HLW disposal stability and integrity research. This article presents the rock discontinuity identification research progress in BET based on Structure from Motion (SfM) photogrammetry technology. The discontinuity identification algorithm is improved by introducing the region‐growing algorithm to optimize the candidate subplane. This algorithm automatically picks the seed, avoids human intervention, and thus increases the work efficiency of the discontinuity identification. The FCM method is improved by embedding with the CFSFDP algorithm in the discontinuity sets grouping. The CFSFDP algorithm coincides well with the Fisher distribution of discontinuity orientations, which is suitable for the Beishan situation. A parallel scheme is used when implementing the method, which accelerates the discontinuity calculation. This improved rock discontinuity identification method was tested on a slope above the BET and applied in the BET. The discontinuity identification results were compared with the results from the manual field measurement and the open‐source software DSE. The results show that the improved discontinuity identification method obtains reliable discontinuity data and costs less time and human workload than the other two methods. The surrounding rock discontinuity identification research provides a powerful tool for the Beishan HLW disposal geological investigation.

show abstract

Automated mapping of rock discontinuities in 3D lidar and photogrammetry models

Cited by 162 publications

References 31 publications

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

Digital Outcrop Model Reconstruction and Interpretation

Beishan exploration tunnel surrounding rock discontinuity identification based on structure from motion photogrammetry technology

Contact Info

Product

Resources

About