Advances in the Investigation and Monitoring of Rock Cliffs and Glaciers with TLS during the Last Decade: An Overview

Transcription

1 Advances in the Investigation and Monitoring of Rock Cliffs and Glaciers with TLS during the Last Decade: An Overview Andrea Tamburini (IMAGEO Srl, Torino, Italy) NEW ADVANCED GNSS and 3D SPATIAL TECHNOLOGIES - APPLICATIONS to CIVIL AND ENVIRONMENTAL ENGINEERING, GEOPHYSICS, ARCHEOLOGY and CULTURAL HERITAGE in memory of Professor Giorgio Manzoni Trieste - 18, 19, 20 February 2016

2 Main goals Assessing the ability of TLS to monitor surface displacements of unstable slopes and glaciers Extracting the attitude of discontinuity surfaces of the rock mass from an oriented point cloud Mapping of the distribution of significant parameters influencing the behaviour of the rock mass, such as discontinuity spacing, fracture density, and URV

3 Liligo Glacier snout mapping (2004) ASTER mosaic, Baltoro,

4 Liligo Glacier snout historical images V.Sella, 1909 A. Desio, 1953

5 Liligo Glacier snout TLS survey (2004)

6 Liligo Glacier snout: DEM and contour map Belò M., Mayer C., Smiraglia C., Tamburini A. (2008) The recent evolution of Liligo glacier, Karakoram, Pakistan, and its present quiescent phase. Annals of Glaciology, 48,

7 Displacement mapping from multi-temporal laser scans Laser Scanner can provide very detailed displacement maps Problem: extracting slope displacements from the comparison of multi-temporal scans is not always effective Displacement rate can be underestimated or neglected, depending on the orientation of the avg displacement vector The rototranslation matrix obtained by aligning multi-temporal scans of the same unstable area can be used to compute the x, y, z components of the mean displacement vector The same approach, applied at local scale to selected features ( natural benchmarks ), can provide local ( punctual ) displacements

8 Selection and roto-translation of natural benchmarks m before and after after roto-translation alignment residuals m

9 Liligo Glacier snout: displacement vectors

10 The Locce moraine landslide ( evolution) May /06/05 June

11 Shortest distance between multi-temporal scans 1.5 month distance along y axis 1.5 month distance along z axis

12 Distance measured along mean displacement vector stdev = 6-7 cm (1σ) Mortara G. & Tamburini A. (a cura di) (2009) Il Ghiacciaio del Belvedere e l emergenza del Lago Effimero. ed. SMI Società Meteorologica Subalpina, 192 pag.

13 Multi-temporal scans alignment residuals stdev = 2-3 cm (1σ)

14 Description of rock mass discontinuities with laser scanner Geometric features (e.g. attitude of planes) can be extracted from the point cloud Rock mass discontinuities => best fit of points selected either on the surface (if visible) or along the intersection with the digital outcrop model Automatic sw tools are presently available and can be applied at slope scale, but a supervised use is needed Semi-automatic routines based on the macro language of a commercial sw (Polyworks by Innovmetric) are generally applied in selected areas to characterize in detail potentially unstable rock volumes Comparison with traditional compass measurements showed a very good agreement with the attitude of planes extracted from the point cloud Broccolato M., Martelli D.C.G., Tamburini A. (2006) Il rilievo geomeccanico di pareti rocciose instabili difficilmente accessibili mediante impiego di laser scanner terrestre. Applicazione al caso di Ozein (Valle di Cogne, Aosta), GEAM, Anno XLIII, n. 4, dicembre 2006, 39-46

15 Workflow Tamburini A., Martelli D.C.G., Alberto W., Villa F. (2015) Geomechanical rock mass characterization with Terrestrial Laser Scanning and UAV. ARMA

16 Local orientation of topographic surface vs joint systems Joint systems Slope

17 Automatic representation of slope aspect: Coltop-3D (Jaboyedoff & Couture, 2003)

18 Example: access to Toula Glacier (Mt. Blanc)

19 Example: access to Toula Glacier (Mt. Blanc)

20 Sliding mechanism identification

21 Unstable volume calculation Volume 820 m 3

22 Raster maps of P21, Jv, VRU, SMR indexes obtained by processing the TLS point cloud with semi-automatic procedures operating in GIS environment

23 Observation window approach sin obs i S l 1 L n n n n n n n n n n n n n l l i _ Joint frequency avg joint spacing number of joints avg persistence

24 Automatic extraction and classification of joints from point cloud analysis

25 Automatic extraction and classification of joints from point cloud analysis point cloud => mesh 3D => breakline => export 2D => frac trace classification

26 Moving circular windows Umili et al., 2013 Sensitivity analysis to identify the optimal window radius

27 Calculated params P21 (Dershowitz & Herda, 1992) Total fracture trace length per unit area Jv = 1/S1 + 1/S2 + 1/S3 + + Nr/(5 A) (Palmstrom, 2005) Number of joints per unit volume Si = average joint spacing Nr = number of random joints per unit area (A) VRU = (S1 S2 S3) / (Sin α Sin β Sin γ) (Palmstrom, 2005) S1, S2, S3 average joint spacing for the considered joint systems e α, β, γ angles between considered joint systems RQD = 100e- 0.1λ (1+0.1λ) (Hudson & Priest, 1979) λ = avg number of joints per meter

28 Example: VRU distribution raster map (maps of avg joint spacing, Jv, P21 can be obtained with the same procedure)

29 Tunnel excavation front (El Teniente, Chile) TLS prism Surveying time: 5 10

30 Tunnel excavation front: point cloud

31 Tunnel excavation front: classified joint traces

32 P21, Jv, VRU, RQD raster maps Data processing time: less than 3 hrs

33 SMR (Slope Mass Rating Romana, 1985; Anbalagan et al., 1992) input from raster map automatic DEM processing input by user SMR = RMRb + (F1 x F2 x F3) F4 RMRb: (Base Rock Mass Rating, Bieniawski, 1989)

34 SMR value distribution

35 Concluding remarks TLS provides aestetically appealing 3D computer models, but proper software processing strategies are needed in order to enable the geologists to make practical use of data Repeated TLS measurements can provide surface displacement rate of landslides and glaciers Surface velocity can t be retrieved by simply comparing multitemporal scans 2D and 3D processing of the point cloud and/or the mesh obtained from TLS can provide maps of the distribution of significant parameters influencing the behaviour of the rock mass (avg joint spacing, Jv, Vb, P21, etc.) The above information can be obtained without accessing the study area Further applications are under development (e.g. extraction of features from 3D digital images)