3D Digital Geological Models E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
3D DIGITAL GEOLOGICAL MODELS
Discover the practical aspects of modeling techniques and their applicability on both terrestrial and extraterrestrial structures
A wide overlap exists in the methodologies used by geoscientists working on the Earth and those focused on other planetary bodies in the Solar System. Over the course of a series of sessions at the General Assemblies of the European Geosciences Union in Vienna, the intersection found in 3D characterization and modeling of geological and geomorphological structures for all terrestrial bodies in our solar system revealed that there are similar datasets and common techniques for the study of all planets—Earth and beyond—from a geological point-of-view. By looking at Digital Outcrop Models (DOMs), Digital Elevation Models (DEMs), or Shape Models (SM), researchers may achieve digital representations of outcrops, topographic surfaces, or entire small bodies of the Solar System, like asteroids or comet nuclei.
3D Digital Geological Models: From Terrestrial Outcrops to Planetary Surfaces has two central objectives, to highlight the similarities that geological disciplines have in common when applied to entities in the Solar System, and to encourage interdisciplinary communication and collaboration between different scientific communities. The book particularly focuses on analytical techniques on DOMs, DEMs and SMs that allow for quantitative characterization of outcrops and geomorphological features. It also highlights innovative 3D interpretation and modeling strategies that allow scientists to gain new and more advanced quantitative results on terrestrial and extraterrestrial structures.
3D Digital Geological Models: From Terrestrial Outcrops to Planetary Surfaces readers will also find:
- The first volume dedicated to this subject matter that successfully integrates methodology and applications
- A series of methodological chapters that provide instruction on best practices involving DOMs, DEMs, and SMs
- A wide range of case studies, including small- to large-scale projects on Earth, Mars, the 67P/Churyumov-Gerasimenko comet, and the Moon
- Examples of how data collected at surface can help reconstruct 3D subsurface models
3D Digital Geological Models: From Terrestrial Outcrops to Planetary Surfaces is a useful reference for academic researchers in earth science, structural geology, geophysics, petroleum geology, remote sensing, geostatistics, and planetary scientists, and graduate students studying in these fields. It will also be of interest for professionals from industry, particularly those in the mining and hydrocarbon fields.
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Table of Contents
Cover
Title Page
Copyright
List of Contributors
Preface
1 3D Digital Geological Models: From Terrestrial Outcrops to Planetary Surfaces
1.1 Introduction
1.2 DOM/SM Reconstruction and Interpretation Workflows
1.3 Morphometric Analysis Across Different Scales and Planets
1.4 3D Modelling of the Subsurface from Surface Data
1.5 Summary and Perspectives
Acknowledgments
References
Part I: DOM and SM Reconstruction and Interpretation Workflows
2 Digital Outcrop Model Reconstruction and Interpretation
2.1 Introduction
2.2 Photogrammetric Surveys and Processing for DOMs
2.3 Point‐Cloud vs. Textured‐Surface DOMs
2.4 Geological Interpretation of DOMs
2.5 Discussion and Conclusion
2.6 Summary and Perspectives
Acknowledgments
References
3 The PRoViDE Framework: Accurate 3D Geological Models for Virtual Exploration of the Martian Surface from Rover and Orbital Imagery
3.1 Introduction
3.2 Components and Methods
3.3 Geological Interpretations of DOMs
3.4 Conclusions
Acknowledgments
References
4 Vombat: An Open Source Tool for Creating Stratigraphic Logs from Virtual Outcrops
4.1 Introduction
4.2 Vombat
4.3 Examples
4.4 Discussion
4.5 Conclusions
Acknowledgment
References
5 Interpretation and Mapping of Geological Features Using Mobile Devices in Outcrop Geology: A Case Study of the Saltwick Formation, North Yorkshire, UK
*
5.1 Introduction
5.2 The Geological Setting: The Saltwick Formation
5.3 From Geological Surface Interpretation to Statistical Subsurface 3D Models
5.4 Mobile Interpretation Using Image‐to‐Geometry Techniques
5.5 Model Construction
5.6 Multiple Point Statistics Simulation of the Saltwick Formation
5.7 Discussion
Acknowledgments
References
Note
6 Image Analysis Algorithms for Semiautomatic Lineament Detection in Geological Outcrops
6.1 Introduction
6.2 The DOMStudioImage Toolbox
6.3 Lineament Detection Workflow
6.4 Results on Geological Images
6.5 Discussion
6.6 Conclusions
References
Part II: Morphometric Analysis Across Different Scales and Planets
7 Mapping Coastal Erosion of a Mediterranean Cliff with a Boat‐Borne Laser Scanner: Performance, Processing, and Cliff Erosion Rate
7.1 Introduction
7.2 Test Site and Study Setting
7.3 Datasets
7.4 Point Cloud: Quality Assessment
7.5 LiDAR Data Processing
7.6 Results
7.7 Discussion
7.8 Conclusion
Acknowledgments
Appendix. Script for Unfolding Point Clouds (R)
References
8 A DEM‐Based Volume Extraction Approach: From Micro‐Scale Weathering Forms to Planetary Lava Tubes
8.1 Introduction
8.2 Micro‐elevation Maps and DEMs Production
8.3 Volumes Extraction
8.4 Results and Discussion
8.5 Conclusions
References
9 Robust Detection of Circular Shapes on 3D Meshes Based on Discrete Curvatures: Application to Impact Craters Recognition
9.1 Introduction
9.2 Related Work
9.3 Basic Notions
9.4 Approach Based on Ring Propagation
9.5 Approach Based on Circle Fitting
9.6 Conclusion
Acknowledgments
References
Part III: 3D Modelling of the Subsurface from Surface Data
10 Remote Sensing and Field Data Based Structural 3D Modelling (Haslital, Switzerland) in Combination with Uncertainty Estimation and Verification by Underground Data
10.1 Introduction
10.2 Geological Setting
10.3 Methodology
10.4 Results and Discussion
10.5 Summary Discussion and Conclusions
Acknowledgments
Appendix A: Topography Effect
Appendix B: Lineament Map from Remote Sensing Data Acquisition
Appendix C : Intersection Analysis at Tunnel Level
References
11 Application of Implicit 3D Modelling to Reconstruct the Layered Structure of the Comet 67P
11.1 Introduction
11.2 A Modelling Strategy for Onion‐Like Layers
11.3 Model Fitting
11.4 Visualization and Validation of the Models
11.5 Conclusions
Acknowledgments
References
Index
End User License Agreement
Guide
Cover
Table of Contents
Title Page
Copyright
List of Contributors
Preface
Begin Reading
Index
End User License Agreement
List of Illustrations
Chapter 2
Figure 2.1 Structural interpretation on a DOM: comparison of manually digiti...
Figure 2.2 Principles of SFM photogrammetry: (a) Image projection as a funct...
Figure 2.3 Photogrammetric image collection schemes: (a) Aerial survey with ...
Figure 2.4 Terrestrial image fans shooting scheme for a DOM of a subvertical...
Figure 2.5 Drone survey shooting scheme with multiple photo strips at variab...
Figure 2.6 Construction of a TS‐DOM by image projection onto a triangulated ...
Figure 2.7 Example of a PC‐DOM (ca. 100 m × 300 m) with color representation...
Figure 2.8 Example of a TS‐DOM (ca. 5 m × 15 m) textured with the original h...
Chapter 3
Figure 3.1 Components of the PRoViDE framework and the data flow. PRoGIS is ...
Figure 3.2 Schematic view of the relational database PRoDB.
Figure 3.3 PRoDB data query example.
Figure 3.4 Hierarchical organization of OPCs. Patches are further subdivided...
Figure 3.5 Examples of the 25 cm HiRISE image (a) and 5 cm SRR image (b) pro...
Figure 3.6 A zoom‐in view of the MER‐A Homeplate 25 cm HiRISE image (a) and ...
Figure 3.7 Example of the MER‐A Homeplate 25 cm HiRISE image (a) and the 5 c...
Figure 3.8 GPT SRR products for MSL were integrated into PRoGIS 1.0 to give ...
Figure 3.9 PRoGIS screenshot showing the path of the Opportunity rover along...
Figure 3.10 PRoGIS framework layout.
Figure 3.11 PRoGIS high‐level system architecture.
Figure 3.12 This depiction of the Victoria crater illustrates the use of dif...
Figure 3.13 HiRISE, HiRISE SRR (super‐resolution restoration), Pancam stereo...
Figure 3.14 (a) Rendering of the rim of the Victoria crater without texture....
Figure 3.15 (a) The individual geometric measurement primitives range from a...
Figure 3.16 Full‐scale geological analysis of the Cape Desire outcrop. This ...
Figure 3.17 Super‐resolution HiRISE image (processed by UCL‐MSSL) of the Vic...
Figure 3.18 (a) 3D rendering of an interpreted scene of Duck Bay in PRo3D, h...
Figure 3.19 Interpreted 3D scene of Cape Desire in PRo3D. The stratigraphy h...
Figure 3.20 HiRISE image of Yellowknife Bay in the Gale crater, where MSL Cu...
Figure 3.21 Full interpretation of a merged Mastcam dataset of the exposed s...
Chapter 4
Figure 4.1 The Vombat toolbar as it appears in CloudCompare. Each icon corre...
Figure 4.2 A screenshot of CloudCompare with the Vombat plugin. Basic tools ...
Figure 4.3 Example of a TLS point cloud representing an undeformed stratigra...
Figure 4.4 Vombat can compute the attitude of a layered sequence by using a ...
Figure 4.5 An SRF can be viewed as a plane in space: its orientation is dete...
Figure 4.6 Representation of a faulted sequence for which two different SRFs...
Figure 4.7 At right, a small extract of a TLS point cloud, colored by the in...
Figure 4.8 An ROI in the case of point clouds can be defined as a 2D polylin...
Figure 4.9 (a) TLS scan of an outcrop of cherty limestone (Lower Cretaceous ...
Figure 4.10 (a) The pelagic succession at the Smirra quarry, which comprises...
Chapter 5
Figure 5.1 Geological map of north‐east Yorkshire, UK, exported on a handhel...
Figure 5.2 Large overview of the Whitby cliff section, spanning a distance o...
Figure 5.3 The GRIT main interface, including a preview panel (top left), a ...
Figure 5.4 The integrated GNSS reception indicator, showing the current rece...
Figure 5.5 Image collection illustrating the registration procedure: first, ...
Figure 5.6 Example field photographs of the Whitby outcrop cliff section, co...
Figure 5.7 Image collage of three successive images covering equal parts of ...
Figure 5.8 Illustration of the geometrical modelling of cross‐section surfac...
Figure 5.9 Photo showing the use of the mobile GRIT application and its 3D d...
Figure 5.10 Image collage illustrating the resulting mismatch of 3D interpre...
Figure 5.11 This rendering shows the imported line segments from the tablet ...
Figure 5.12 This experimental rendering shows the match between a photograph...
Figure 5.13 Outcrop rendering panel showing the corrected line segment inter...
Figure 5.14 Key stratigraphic surfaces defining the reservoir zone displayed...
Figure 5.15 Visually interrogating sedimentary logs (created in Strataledge)...
Figure 5.16 Above: conceptual training image for simulation with multiple‐po...
Figure 5.17 Comparison of the virtual outcrop and object model used as the t...
Figure 5.18 MPS realization 1 (top) and realization 2 (bottom).
Figure 5.19 Connected bodies of realization 1 (top) and the corresponding we...
Figure 5.20 The current stage of a mobile device‐integrated reservoir modell...
Chapter 6
Figure 6.1 The DOMStudioTS workflow for building and interpreting photogramm...
Figure 6.2 The link between field pictures and the DOM triangulated surface....
Figure 6.3 Image of Outcrop 1 (a), converted to grayscale and equalized usin...
Figure 6.4 Example of an image of Outcrop 2 processed with the MRF‐ICM segme...
Figure 6.5 Details of lineaments in outcrop images. (a) Detail of the pictur...
Figure 6.6 Output of the DoG filter on an image of Outcrop 1. (a) The origin...
Figure 6.7 Detail of the output of the PhSym line detection algorithm. (a) D...
Figure 6.8 (a) Isotropic tiling scheme for a wavelet. (b) Anisotropic tiling...
Figure 6.9 Output of the complex shearlet phase congruency on an image of Ou...
Figure 6.10 Examples of skeletonization workflows applied to a detail of the...
Figure 6.11 Comparison of the lineament traces obtained on the three outcrop...
Chapter 7
Figure 7.1 Study site of Carry‐le‐Rouet along the micro‐tidal Mediterranean ...
Figure 7.2 Quality assessment principle applied to a planar object. Point pr...
Figure 7.3 Point precision (PP) assessment. (a) Frequency density (bandwidth...
Figure 7.4 Inter‐point assessment determined by a projection of 2.5D grids f...
Figure 7.5 Flow diagram of LiDAR data processing (from data acquisition to e...
Figure 7.6 Projection method applied to a complex sinuous point cloud, an ex...
Figure 7.7 Unfolding method of curvilinear section defined by a circle. Unfo...
Figure 7.8 Semi‐automatic classification by mask application to an unfolded ...
Figure 7.9 Point cloud processed (unfolded and classified). Example of the e...
Figure 7.10 Overlay‐map generation in two steps. (a) Point presence mask cre...
Figure 7.11 Examples of the Digital Surface Model of erosion (DSMe) overlaid...
Figure 7.12 Average erosion rates integrated along‐shore (a) and vertically ...
Figure 7.A-1 Sketch of the unfolding procedure. The initial point cloud is f...
Chapter 8
Figure 8.1 Examples of CLSM 3‐D elevation maps (area is about 2.5 × 2.5 mm):...
Figure 8.2 In a) the location of the studied Hadriaca lava tube section, on ...
Figure 8.3 in a) the 5m LIDAR topography and highlighted the profile trace o...
Figure 8.4 schematic representation of the process of creating a reference s...
Figure 8.5 detail of the process used to create the synthetic surfaces that ...
Figure 8.6 Example of dataset used for validation of the surface difference ...
Figure 8.7 Correlation between the average grain size of the different textu...
Figure 8.8 Plot showing in log/log the Volumes vs width of the Martian and E...
Chapter 9
Figure 9.1 1872 craters with a diameter superior to 2 km were detected in th...
Figure 9.2 Results obtained on an image of the Moon with the method of Sawab...
Figure 9.3 A representation of
K
1
, the maximal curvature, and
K
2
, the minima...
Figure 9.4 Using the signs of
K
and
H
, we can define eight types of fundamen...
Figure 9.5 Visualization of the mean curvature on a high‐resolution surface ...
Figure 9.6 Visualization of the mean curvature on a low‐resolution surface (...
Figure 9.7 Visualization of the detection of concave areas. Each connected c...
Figure 9.8 Illustration of the process of updating a point
p
with a distance...
Figure 9.9 Graph of the average of the maximal curvature
k
1
(on the Y‐axis) ...
Figure 9.10 Visualization of the mean curvature map of the surface (300,000 ...
Figure 9.11 Visualization of the result of the automatic detection. Green ci...
Figure 9.12 Pruning of the skeleton of an area of interest on the model of a...
Figure 9.13 Determination of the center of a circle and outline propagation....
Figure 9.14 Detection obtained from the 3D model of the asteroid Vesta. The ...
Chapter 10
Figure 10.1 Geological maps of the study area (a, b). (a) Tectonic sketch sh...
Figure 10.2 Characterization of mechanical discontinuities in host rocks tha...
Figure 10.3 Link between km‐scale surface incisions and outcrop‐scale mechan...
Figure 10.4 Four‐step workflow applied to generate 3D planes from 2D lineame...
Figure 10.5 Influence of DEM resolution on digitization accuracy and the rep...
Figure 10.6 Variation of strike along the shear zone trace due to bandwidth ...
Figure 10.7 Representation of topography based on point density along a faul...
Figure 10.8 Calculation of the 3D orientation of a plane based on surface in...
Figure 10.9 Variation of the strike along the outcrop trace of GPSZ 10. Hist...
Figure 10.10 (a) Stereographic projections showing variability in dip and di...
Figure 10.11 Uncertainty estimates for the extrapolation of surface structur...
Figure 10.12 Projection of surface structures to depth and correlation with ...
Figure 10.13 Correlation of NE–SW striking surface incisions with ductile sh...
Figure 10.14 Shear zone map and structural 3D model of the Haslital. (a) Fiv...
Figure A.1 Artificial digital elevation model, cross‐cut by five artificial ...
Figure A.2 Topography effect demonstrates the influence of dip angle on the ...
Figure B.1 Lineament map consisting of 178,000 standardized segments of 25 m...
Figure C.1 Histograms for extrapolation of all surface structures, based on ...
Chapter 11
Figure 1.11 A topographic profile obtained by cutting the shape model of Chu...
Figure 11.2 One of the geological cross‐sections realized by Massironi et al...
Figure 11.3 The geological cross‐sections realized by Massironi et al. (2015...
Figure 11.4 Three different modelling functions for roundish concentric laye...
Figure 11.5 The 3D shape model of Churyumov‐Gerasimenko (67P) (Preusker et a...
Figure 11.6 Histograms and statistics for the angular residuals for three di...
Figure 11.7 The angular residuals resulting from the model can be visualized...
Figure 11.8 Distributions of the ellipsoidal model center
[cx, cy, cz]
...
Figure 11.9 The center of mass of the modelled lobe would be expected to cor...
Figure 11.10 Comparison of a cross‐section. On the left, the section number ...
Figure 11.11 To overlay the RES contour lines on a real OSIRIS image, the ge...
Figure 11.12 (a) Render of the shape‐model made with Blender: the cliffs (re...
List of Tables
Chapter 2
Table 2.1 Ground resolution and image calculation for a Nikon D700 SLR camer...
Table 2.2 A comparison of laser scanning vs. photogrammetric data acquisitio...
Table 2.3 A comparison of point‐cloud DOMs (PC‐DOMs) vs. textured surface DO...
Chapter 6
Table 6.1 Summary of the performances expressed as precision and recall of e...
Chapter 7
Table 7.1 Technical specifications of the three boat mobile mapping systems ...
Table 7.2 Synthesis of point clouds quality. (a) Single epoch quality of the...
Chapter 8
Table 8.1 Width and volumes derived with the hole filling technique in each ...
Table 8.2 Comparison of the volume values obtained by surface difference and...
Chapter 11
Table 11.1 Some of the functions that were tested as a modelling function fo...
Table 11.2 Center of mass of the lobes, as computed by Jorda et al. (2016), ...
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3D Digital Geological Models
From Terrestrial Outcrops to Planetary Surfaces
Edited by
Andrea Bistacchi
Department of Environmental and Earth Sciences
University of Milano‐Bicocca
Milan, Italy
Matteo Massironi
Department of Geosciences
University of Padua
Padua, Italy
Sophie Viseur
Aix‐Marseille University
Marseille, France
TThis edition first published 2022
© 2022 John Wiley & Sons, Inc.
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The right of Andrea Bistacchi, Matteo Massironi, and Sophie Viseur to be identified as the editors of the editorial material in this work has been asserted in accordance with law.
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Library of Congress Cataloging‐in‐Publication Data
Names: Bistacchi, Andrea, editor. | Massironi, M. (Matteo), editor. | Viseur, Sophie, editor.
Title: 3D digital geological models : from terrestrial outcrops to planetary surfaces / edited by Andrea Bistacchi, Matteo Massironi, Sophie Viseur.
Description: Hoboken, NJ : Wiley, 2022. | Includes bibliographical references and index.
Identifiers: LCCN 2021050370 (print) | LCCN 2021050371 (ebook) | ISBN 9781119313885 (hardback) | ISBN 9781119313915 (adobe pdf) | ISBN 9781119313892 (epub)
Subjects: LCSH: Three-dimensional imaging in geology.
Classification: LCC QE26.3 .T15 2022 (print) | LCC QE26.3 (ebook) | DDC 550.28/4—dc23
LC record available at https://lccn.loc.gov/2021050370
LC ebook record available at https://lccn.loc.gov/2021050371
Cover Design: Wiley
Cover Image: Courtesy of Riccardo Pozzobon (front cover); Courtesy of Andrea Bistacchi (back cover)
List of Contributors
Sarah Bali
Aix‐Marseille University
Marseille
France
Robert Barnes
Imperial College London
London
UK
Roland Baumberger
Institute of Geological Sciences
University of Bern
Bern
Switzerland
and
Federal Office of Topography
Swiss Geological Survey
Wabern
Switzerland
Florian Beguet
Aix‐Marseille University
Marseille
France
Ivano Bertini
Department of Physics and Astronomy “Galileo Galilei”
University of Padova
Padova
Italy
Andrea Bistacchi
Dipartimento di Scienze dell'Ambiente e della Terra
Università degli Studi di Milano - Bicocca
Milano
Italy
Sylvain Bouley
Université Paris‐Saclay
Orsay
France
Simon J. Buckley
NORCE Norwegian Research Centre AS
Bergen
Norway
and
Department of Earth Science
University of Bergen
Bergen
Norway
Pamela Cambianica
INAF
Astronomical Observatory of Padova
Padova
Italy
Thomas J. B. Dewez
BRGM
Marseille
France
Sabrina Ferrari
Centre of Studies and Activities for Space “G. Colombo”
University of Padua
Padua
Italy
Marco Franceschi
Department of Geosciences
University of Padua
Padua
Italy
Elisa Frattin
INAF
Astronomical Observatory of Padova
Padova
Italy
and
Department of Physics and Astronomy “Galileo Galilei”
University of Padova
Padova
Italy
Robert Gaskell
Planetary Science Institute
Tucson
Arizona
USA
Robert L. Gawthorpe
Department of Earth Science
University of Bergen
Bergen
Norway
Jérémy Giuliano
GeoConseil
Risk and Geological Consulting
Le Val
France
and
Université de Nice Sophia Antipolis
Valbone
France
and
Aix‐Marseille Université
Marseille
France
Vincent Godard
Aix‐Marseille Université
Marseille
France
Sanjeev Gupta
Imperial College London
London
UK
Marco Herwegh
Institute of Geological Sciences
University of Bern
Bern
Switzerland
Gerd Hesina
Zentrum für Virtual Reality und Visualisierung (VRVis)
Forschungs‐GmbH
Vienna
Austria
John A. Howell
Department of Geology and Petroleum Geology
University of Aberdeen
Aberdeen
UK
Laurent Jorda
Aix‐Marseille University
Marseille
France
Christian Kehl
Uni Research AS
Bergen
Norway
and
Department of Earth Science
University of Bergen
Bergen
Norway
Edi Kissling
Institute of Geophysics
ETH Zürich
Zürich
Switzerland
Fiorangela La Forgia
Department of Physics and Astronomy “Galileo Galilei”
University of Padova
Padova
Italy
Thomas Lebourg
University of Nice Sophia Antipolis,
Valbone
France
Alice Luccetti
INAF
Astronomical Observatory of Padova
Padova
Italy
Nathalie Marçot
BRGM
Marseille
France
Jean‐Luc Mari
Aix‐Marseille University
Marseille
France
Mattia Martinelli
Dipartimento di Scienze dell'Ambiente e della Terra
Universita' degli Studi di Milano - Bicocca
Milano
Italy
Matteo Massironi
Dipartimento di Geoscienze
Università degli Studi di Padova
Padua
Italy
Claudio Mazzoli
Department of Geosciences
University of Padua
Padua
Italy
Silvia Mittempergher
Dipartimento di Scienze dell'Ambiente e della Terra
Universita' degli Studi di Milano - Bicocca
Milano
Italy
and
Dipartimento di Scienze Chimiche e Geologiche
Università degli Studi di Modena e Reggio Emilia
Modena
Italy
Jan‐Peter Muller
University College London
London
UK
James R. Mullins
Department of Geology and Petroleum Geology
University of Aberdeen
Aberdeen
UK
Jennifer Muscato
Aix‐Marseille University
Marseille
France
Giampiero Naletto
Centre of Studies and Activities for Space “G. Colombo”
University of Padua
Padua
Italy
and
CNR‐IFN UOS Padua LUXOR
Padua
Italy
and
Department of Physics and Astronomy “Galileo Galilei”
University of Padua
Padua
Italy
Thomas Ortner
Zentrum für Virtual Reality und Visualisierung (VRVis)
Forschungs‐GmbH
Vienna
Austria
Gerhard Paar
Joanneum Research
Graz
Austria
Maurizio Pajola
INAF
Astronomical Observatory of Padova
Padova
Italy
Luca Penasa
Centre of Studies and Activities for Space “G. Colombo”
University of Padua
Padua
Italy
and
Dipartimento di Geoscienze
University of Padua
Padua
Italy
Riccardo Pozzobon
Department of Geosciences
University of Padua
Padua
Italy
Mélody Prémaillon
BRGM
Montpellier
France
Nereo Preto
Dipartimento di Geoscienze
University of Padua
Padua
Italy
Frank Preusker
Institute of Planetary Research
German Aerospace Center
Berlin
Germany
Silvia Salvini
Department of Geosciences
University of Padua
Padua
Italy
Tommaso Santagata
Vigea
Reggio Emilia
Italy
Francesco Sauro
Department of Biological, Geological, and Environmental Sciences
University of Bologna
Bologna
Italy
Martin‐Pierre Schmidt
Aix‐Marseille University
Marseille
France
Frank Scholten
Institute of Planetary Research
German Aerospace Center
Berlin
Germany
Holger Sierks
Max Planck Institute for Solar System Research
Göttingen
Germany
Emanuele Simioni
Astronomical Observatory of Padua
National Institute of Astrophysics
Padua
Italy
and
CNR‐IFN UOS Padua LUXOR
Padua
Italy
Yu Tao
University College London
London
UK
Christoph Traxler
Zentrum für Virtual Reality und Visualisierung (VRVis)
Forschungs‐GmbH
Vienna
Austria
Sophie Viseur
Aix‐Marseille University
Marseille
France
Konrad Willner
Institute of Planetary Research
German Aerospace Center
Berlin
Germany
Preface
A series of sessions was hosted at General Assemblies of the European Geosciences Union in Vienna, focused on techniques allowing geoscientists with various backgrounds to collect 3D geological and geomorphological quantitative data on Digital Outcrop Models (DOMs), Digital Elevation Models (DEMs), or Shape Models (SMs): digital representations of outcrops, topographic surfaces, or whole small bodies of the Solar System (asteroid or comet nuclei) respectively. These sessions were based on the assumption that the two scientific communities, working on the Earth and on planetary bodies of the Solar System, are indeed using very similar datasets and techniques for studying 3D models from the geological point of view. During those meetings the members of the two communities, often unaware of each other's work, had the chance to share their knowhow, procedures, and methodologies, and this volume represents a natural prosecution of that experience. In particular, the volume has two main aims: disclosing the numerous points that geological disciplines have in common, when applied on the Earth and on planetary bodies, and favoring the communication and collaboration between different scientific communities.
The first chapters focus on techniques used to reconstruct DOMs in diverse environments (with examples on the Earth and on Mars) and with different remote sensing techniques (i.e. laser scanning vs. photogrammetry), and propose modern approaches for DOM analysis and interpretation, including semi‐automatic image and mesh processing techniques. The second block of chapters presents case studies of quantitative geomorphological analysis on the Earth, Mars, and the Moon. In the final block, some examples on how data collected at the surface can be used to reconstruct 3D subsurface models are discussed.
Reading these chapters, authored by experts in different fields, it will become apparent that (i) the fundamental techniques allowing the production of DEMs, DOMs, and SMs (e.g. photogrammetry, laser scanning, radar interferometry) are well consolidated, and are almost seamlessly shared between the communities of scientists working on the Earth and on other bodies of the Solar System; (ii) the way these techniques are applied in different geological environments may change and, in some cases, can influence the quality of the results; (iii) the development of new techniques for DOM, DEM, and SM processing, elaboration, and analysis is under way and thus highly subject to continuous improvements; and (iv) the production of subsurface geological models based (also) on surface data will be an active field of research in the years to come.
Further challenges will arise with the increase of DOM, DEM, and SM acquisitions, such as: (i) developing integrated routines for the automated analysis and interpretation of topographic datasets (meshes or point‐clouds) or imagery (e.g. high‐resolution orthophotos); (ii) managing the numerous and huge datasets acquired, for instance, as time series of successive acquisitions for geological risk monitoring; or (iii) providing access to web‐platforms for sharing outcrop datasets in geosciences, as already commonly done in astronomy.
We hope that this volume will foster collaborative efforts towards 3D data exploitation and will be able to inspire, with its introductive and general chapters, young researchers interested in 3D data analysis and senior scientists with the more advanced case studies.
Andrea Bistacchi
Dipartimento di Scienze dell'Ambiente e della Terra
Università degli Studi di Milano Bicocca
Matteo Massironi
Dipartimento di Geoscienze
Università degli Studi di Padova
Sophie Viseur
Centre Européen de Recherche et d'Enseignement des Géosciences de l'Environnement (CEREGE)
Aix‐Marseille Université
and
Centre National de la Recherche Scientifique (CNRS)
Institut de Recherche pour le Développement (IRD)
Institut National de Recherche pour l'Agriculture
l'alimentation et l'Environnement (INRAE)
Collège de France
13D Digital Geological Models: From Terrestrial Outcrops to Planetary Surfaces
Andrea Bistacchi1, Matteo Massironi2, and Sophie Viseur3
1Dipartimento di Scienze dell'Ambiente e della Terra, Università degli Studi di Milano - Bicocca, Piazza della Scienza, 4, 20126 Milano
2Dipartimento di Geoscienze, Università degli Studi di Padova, Via Gradenigo 6, 35131 Padova
3Aix Marseille Univ, CNRS, IRD, INRAE, Coll France, CEREGE., Case 67, 3 place Victor Hugo, 13331 Marseille CEDEX 03, France
Abstract
Collecting quantitative data to support geological analysis and modelling is nowadays a fundamental requirement in all geology disciplines, including structural geology, stratigraphy, and geomorphology, on the Earth and on planetary bodies of the Solar System. In many cases the answer to this need is a Digital Outcrop Model (DOM), a Digital Elevation Model (DEM), or a Shape Model (SM): this can be a digital representation of an outcrop or topographic surface, or of a whole small body (asteroid or comet nucleus) for an SM, generally combined with imagery, that can be quantitatively visualized and studied in 3D, with the goal of obtaining quantitative measurements.
3D datasets and models for geological purposes include different complementary products: DEMs, DOMs, SMs, and subsurface models. The main differences among these different products are: (i) their nature, since DEMs, DOMs, and SMs represent relief surfaces showing outcropping geological structures that are completely accessible to characterization (up to some precision/resolution), while subsurface models reproduce inaccessible subsurface geological structures with some unavoidable level of uncertainty (hence they are models); and (ii) their topology/dimensionality, as DEMs are actually 2.5D surfaces, generally covering large areas, DOMs are truly 3D surfaces, including multivalued reliefs (e.g. complex or overhanging reliefs, cliffs, caves, etc.), but are generally limited to smaller‐scale outcrops, and SMs are closed surfaces covering a whole small body, where subsurface models are essentially volumetric.
In this volume we collect various examples of methods and techniques used to collect, analyze, and model 3D datasets, based on one or more supports (DEM, DOM, SM, subsurface model), and on different software tools, remote sensing, and modelling techniques. Reading the chapters authored by experts in different fields, it will become apparent that (i) the fundamental techniques allowing the production of DEMs, DOMs, and SMs through photogrammetry, laser scanning devices, and radar interferometry are well consolidated, and are almost seamlessly shared between the community of scientists working on the Earth and on planetary bodies of the Solar System; (ii) the particular way these techniques are applied in specific geological environments may change and, for instance, acquisition schemes in photogrammetry still represent a potentially critical issue; (iii) DOM, DEM, and SM processing, elaboration and analysis, including the analysis of image data associated with these surfaces, are active fields of research that are subject to continuous improvements; and (iv) the production of subsurface geological models based (also) on surface data is still not very common, particularly in planetary geology contexts.
One of the aims of this volume is to disclose the numerous points that geological disciplines have in common in applications on the Earth and on planetary bodies of the Solar System, and to favor the communication and collaboration between different scientific communities.
1.1 Introduction
Collecting 3D quantitative data is a fundamental requirement in many structural geology, stratigraphy, sedimentology, geomorphology, and engineering geology projects both on the Earth and on planetary bodies of the Solar System (e.g. Bistacchi et al., 2011; Simioni et al., 2015; Jones et al., 2016; Tavani et al., 2016; Martinelli et al., 2017, 2020; Penasa et al., 2017; Triantafyllou et al., 2019; Siddiqui et al., 2019; Caravaca et al., 2020; Crane, 2020; De Toffoli et al., 2020; Le Mouélic et al., 2020). This can be achieved using different and complementary 3D datasets: Digital Elevation Models (DEMs), Digital Outcrop Models (DOMs), and Shape Models (SMs).
DEMs are 2.5D representations of topographic surfaces (e.g. Jones et al., 2008), generally with regional to global extension, produced through consolidated approaches from photogrammetry, laser scanning devices, and radar interferometry. DEMs are generally stored as 2D regular grids, but triangulated surfaces can be used in some applications, and are called Triangulated Irregular Networks (TINs) in GIS systems.
DOMs (e.g. Bellian et al., 2005) are instead digital high‐resolution representations of outcrops, or of the topographic surface at smaller scale. DOMs, represented as triangulated surfaces or point clouds, can represent multivalued reliefs (e.g. cliffs, caves, highly rough or overhanging reliefs) and can be really considered as 3D geometrical representations (Jones et al., 2008). DOMs can be textured if stored as triangulated surfaces (Catmull, 1974) and colored in case of point clouds (e.g. with RGB, LiDAR intensity). The textures mapped onto the surfaces may be single photos or, more recently, texture atlases (Lévy et al., 2002). DOMs can be visualized and studied, with dedicated software, with the final goal of obtaining quantitative measurements of sedimentary, stratigraphic, intrusive, tectonic, or geomorphological structures, or mapping lithology or alteration halos, etc.
SMs are produced for irregular small bodies of the Solar System, mainly using photogrammetric approaches (e.g. Carry et al., 2012; Preusker et al., 2012, 2015; Willner et al., 2014). Their particularity is that they are closed surfaces since they represent a whole small body. Apart from this, they share most other properties with DOMs.
3D geological models are 3D reconstructions of the subsurface geology and, historically, they have been produced based on geophysical and borehole/well datasets, mainly in oil and mining exploration contexts (e.g. Mallet, 2002, and references therein). More recently, they have been used also in other contexts, such as academic research projects or engineering geology, and, thanks to the emergence of high‐resolution DEMs and DOMs, they are also based on surface datasets. Subsurface geological models are often represented as a set of interface surfaces (e.g. faults, stratigraphic surfaces), termed as 3D Structural Models (Caumon et al., 2009), and sometimes as 3D structured or unstructured grids or meshes (Mallet, 2002) for specific applications (e.g. flow simulation, mechanical modelling, geostatistics). On planetary bodies of the Solar System, subsurface models have been reconstructed using surface data collected on DEMs, DOMs, or SMs (Penasa et al., 2017; Pozzobon et al., 2020; Franceschi et al., 2020), and also on subsurface geophysical datasets such as radargrams (Yuan et al., 2017).
Using the same techniques when reconstructing and analyzing DOMs, DEMs, SMs, and 3D subsurface geological models on the Earth and planets can be considered a fair example of replicable science, which helps to reduce the uncertainty when interpreting geological features on planetary surfaces without any help from manned surveys.
The goal of this volume is to review and discuss a collection of techniques and workflows that can be applied to study DEMs and DOMs and to retrieve 3D geomodels on the Earth and on planetary bodies, starting from data collected with different instruments and platforms (e.g. laser scanning vs. photogrammetry, aerial and orbital vs. terrestrial), in different environmental and logistic conditions, at different scales, and for different purposes. The volume is organized in three sections. The five chapters of the first section focus on techniques used to reconstruct DOMs in different environments and with different remote sensing techniques (i.e. laser scanning vs. photogrammetry) and propose modern approaches for DOM analysis and interpretation. The three chapters of the second section propose examples of morphometric analysis at different scales on the Earth, on Mars, and on the Moon. Finally, the two chapters of the final section show how data collected at the surface can be used to reconstruct 3D models of the subsurface.
1.2 DOM/SM Reconstruction and Interpretation Workflows
In the last 10 years, many papers have been published based on the analysis of DOM and SM datasets, with interesting results in many fields of the Earth and planetary sciences. Considering structural analysis, many contributions appeared where the authors use DOMs to collect large datasets on fracture networks (e.g. Martinelli et al., 2020, and references therein) and the increase in dataset size was so huge that it resulted in a renewed interest in techniques used to characterize fracture statistics (e.g. Guerriero et al., 2011; Marrett et al., 2018; Bistacchi et al., 2020). Other applications include the larger‐scale characterization of fault zones (e.g. Tavani et al., 2016) and folds (e.g. Vollgger and Cruden, 2016). The opportunity to collect huge datasets for fracture network characterization is appealing also in rock mechanics and engineering projects, both on natural slopes and rock faces (e.g. Jaboyedoff et al., 2007) and on man‐made mine faces and roadcuts (e.g. Sturzenegger and Stead, 2009).
In stratigraphy and sedimentology the main applications of DOMs are those where quantitative data are collected to provide data for facies distribution models, particularly in clastic systems (e.g. Buckley et al., 2013; Siddiqui et al., 2019). Special applications of SM analyses allowed Massironi et al. (2015) to reconstruct the stratigraphy of the 67P Churyumov–Gerasimenko comet, with implications concerning mechanisms of comet accretion and evolution, as well as the works of Matonti et al. (2019) concerning fault interpretations in the neck of the comet and Simioni et al. (2015) retrieving the 3D fracture pattern of Phobos grooves.
Another growing field of application for DOMs is the high‐resolution mapping of compositional features, either in terms of lithology, mineralogy, or chemical alteration. Typical examples on the Earth deal with mapping of hydrothermal dolostone bodies (Kurz et al., 2013; Bistacchi et al., 2015).
In addition to allowing collecting quantitative data, DOMs also allow these measurements to be performed remotely. This is an advantage in situations where accessing an outcrop can be dangerous (e.g. steep rock walls liable to rockfall) or the fieldwork would be very time‐consuming (e.g. large outcrops exposing thousands of joints) or even impossible (e.g. on planetary bodies). Virtual reality environments called Virtual Outcrops (VOs) represent the only way to perform some sort of fieldwork on planetary bodies and, in fact, an important impetus to develop advanced applications in this field came from recent rover missions on Mars (e.g. Barnes et al., 2018; Caravaca et al., 2020).
The efficient, time‐ and cost‐effective, and precise reconstruction and analysis of DOMs does not result from a single and simple receipt but is a combination of multiple ingredients that can be mixed in different ways, depending on the goals of the study, on logistical and environmental conditions, on the availability of instruments and software, and on the expertise and personal inclination of the geologist.
Chapter 2 by Bistacchi et al. (2021) discusses photogrammetric and laser‐scanning techniques used to reconstruct DOMs on the Earth, both with terrestrial and aerial drone platforms, particularly for outcrops ranging from a few square meters to about 1 km2. These authors suggest a best‐practice workflow in photogrammetric projects and provide a review of common pitfalls, which can make the difference between successful and unreliable photogrammetric processing. Luckily, it turns out that obtaining a successful photogrammetric reconstruction, with high accuracy and low noise, is more a matter of using proper acquisition schemes and software than costly cameras, lenses, drones, and hardware. Also, free software is very competitive with respect to commercial competitors. This means that well‐trained geologists could collect high‐quality photogrammetric DOMs with limited expense, and this is one key to understand the rapid growth of DOM projects in many geological disciplines.
Chapter 3 by Traxler et al. (2021) provides a top‐notch example of the integration of rover and orbiter images of the surface of Mars, aimed at reconstructing multiresolution DOMs to be inserted in Virtual Reality environments. The availability of this kind of data is seeding a revolution in planetary geology, allowing planetary geologists to perform multiscale analyses that are the standard on the Earth, but were not possible on terrestrial planets just a few years ago. The chapter itself shows examples of geological data extraction on sites explored by the Opportunity and Curiosity rovers.
In any case, these two chapters highlight how, in completely different environments, the fundamental starting point in any DOM project is to implement a correct acquisition scheme, which must be at the same time cost‐ and time‐effective, prone to generate accurate reconstructions, and tailored to the requirements of the study in terms of resolution, imagery products, etc.
If the acquisition and reconstruction of a DOM, either with laser scanning or photogrammetry, is a task shared with many other disciplines (e.g. architecture, archeology, civil engineering, environmental sciences, etc.), what is specific to geology, and to particular disciplines in geology (e.g. structural geology, stratigraphy, sedimentology, geomorphology, etc.), is DOM interpretation. Probably the most important obstacle that has slowed down the growth of quantitative analysis of DOM datasets is the almost complete lack of dedicated software. The solution to this problem consists in either borrowing, in some “creative” way, software originally developed for other tasks, or developing new dedicated software. In the first case the price to pay is, almost always, to have not optimal or missing functions. In the second case, relevant time (and money) must be invested, but in the end software tools with perfectly tailored functions could be developed.
Chapter 4 by Penasa et al. (2021) discusses the Vombat plugin (github.com/luca-penasa/vombat) that adds tools for geological interpretation and measurement of stratigraphic logs to the well‐known and very efficient CloudCompare 3D point cloud processing software (cloudcompare.org). The development of a plugin (i.e. a software component that extends the capabilities of a computer program) is an interesting shortcut that allows developing specific functions with a limited time (and economic) effort if a base program exists, which implements a useful set of base functions. In this case, CloudCompare is amongst the best and more efficient programs to visualize and analyze 3D point clouds and Vombat adds specific geological and stratigraphic interpretation functions.
Chapter 5 by Kehl et al. (2021) discusses a software tool that is somehow similar to Vombat, but runs on a handheld device (Android tablet). This means that the analysis can be performed in the field in a sort of augmented reality way, which is very interesting since it allows the breadth of the remote‐sensing based DOM dataset to combine with detailed and focused observations or cross‐checks that can be performed just in person, directly on the outcrop.
Finally, Chapter 6 by Mittempergher and Bistacchi (2021) addresses what is becoming a key issue due to the growing size and resolution of DOM datasets: (semi‐)automatic interpretation. It is nowadays a common practice to collect photogrammetric DOMs covering a few hundred square meters with submillimeter/pixel ground resolution, or up to 1–2 km2 with 1–5 cm/pixel resolution. This corresponds to some gigabytes of image data, and the manual interpretation of these images could be extremely time‐consuming or sometimes unfeasible, hindering some important advantages of DOM analysis. Even if the problem is far from being solved in all geological situations and outcrop conditions, Mittempergher and Bistacchi (2021) propose a choice of algorithms that could help the analyst at least in some standard situations (e.g. fracturing in sedimentary rocks).
1.3 Morphometric Analysis Across Different Scales and Planets
DEMs and DOMs are fundamental datasets for geomorphological interpretations used to analyze and model surface phenomena (crater detection for relative dating, landslides, erosional features, etc.). Quite often these interpretations are still performed manually, which becomes very time‐consuming or even impossible when dealing with large areas including many geological and geomorphological structures at different scales. Automatic approaches were proposed to extract features from 2D images (Csillag, 1982; Blondel et al., 1992; Yésou et al., 1993; Koike et al., 1995; Mugglestone and Renshaw, 1998; Reid and Harrison, 2000), but in the case of geological applications, this often leads to distortions of the interpreted structures and morphologies that are 3D in nature rather than 2D. With the emergence of high‐resolution DEMs and DOMs, automatic approaches were proposed to help solve these problems. The implementation of these approaches depends on three factors: (i) the nature of the structures to be interpreted, (ii) their surface expressions, and (iii) the dataset that is available for the analysis.
On the one hand, geological structures include surfaces such as faults or stratigraphic surfaces but also volumes such as layers or sedimentary bodies. Their intersections with the topographic surface leads respectively to: (i) lineaments (e.g. fracture or stratigraphy traces) or surfaces (e.g. fracture planes or structural surfaces) belonging to the relief; (ii) partitions of the topographic surfaces, which correspond to a geological mapping onto the numerical outcrop. On the other hand, geomorphological features such as impact craters, lava flows, volcanic cones, fluvial terraces, landslides, and glacial/periglacial forms are expressed on the topographic surface and their interpretation leads to a geomorphological mapping (i.e. a surface partition in terms of geomorphological units). In geomorphology, a particular attention is also devoted to quantitatively estimate the volume and morphometric parameters (i.e. slope and curvatures) of geological and geomorphological features as well as quantitatively assess their evolution in the time frame of successive acquisitions (4D data analysis).
Most approaches used to extract morphometric parameters from DEMs or DOMs rely on differential geometry and especially on the computation of the different normal curvatures (minimum, maximum, mean, and Gaussian). In Kudelski et al. (2011) approaches are proposed for extracting continuous lineaments (fracture traces or stratigraphic planes) despite the roughness of the outcrop surfaces. A similar approach has been used for extracting the impacts of crater rims by Mari et al. (2021) and many other different algorithms have been proposed for the same aim (Bandeira et al., 2012; Cohen and Ding, 2014; Salamunićcar et al., 2014; Christoff et al., 2020) in order to find the crater size frequency distribution in the most reliable way to indirectly date planetary surfaces (e.g. Neukum et al., 2001; Marchi et al., 2009; Le Feuvre and Wieczorek, 2011). Morphometric approaches for geomorphological mapping on extra‐terrestrial surfaces also deal with other structures such as lava tube pits and skylights (e.g. Sauro et al., 2020), mounds and mud volcanoes (Pozzobon et al., 2020), volcanic rootless cones, and transverse aeolian ridges (Palafox et al., 2017).
The use of triangulated surfaces for representing DOMs generally leads to decimation of the dataset due to performance issues. Even if algorithms have been proposed to optimize triangle resolution according to surface curvature (Nivoliers et al., 2015), applying them on DOMs still remains tricky as features associated to subtle surface roughness, such as fractures or bedding, may be obliterated. Therefore, detection algorithms have been developed to directly deal with point clouds. Among them, many approaches applied to laser scanning data use the LiDAR intensity as a proxy for lithology and many corrected estimations have been established for enhancing the resulting facies or lithological mapping (Franceschi et al., 2009; Burton et al., 2011; Penasa et al., 2014; Carrea et al., 2016). The same goals have been achieved by combining DOM acquisition with hyperspectral data (Hartzell et al., 2014).
In this volume, a sample of up‐to‐date techniques for automatic feature extraction and/or quantitative data analysis on DEMs are presented.
Chapter 9 by Mari et al. (2021) presents an approach for extracting morphometric parameters of impact craters from extra‐terrestrial surfaces represented as full 3D meshes. In this work, the algorithms are based on vertex labelling using mean and Gaussian curvature thresholds for automatically extracting crater rims and floors. In particular, two techniques have been proposed and applied on 3D meshes representing asteroid reliefs (e.g. Lutetia observed by the ROSETTA space probe and Vesta imaged by the DAWN mission). The results have afterwards been compared and validated.
Chapter 7 by Giuliano et al. (2021) proposes an approach based on laser scanning acquisitions for estimating erosion rates through time in a coastal environment. The authors discuss the use of repeated boat‐borne laser scanning surveys to quantify cliff erosion in micro‐tidal environments as well as the performance and resolution of this proposed processing. The huge dataset was projected onto a series of vertical planes and cylinder arcs in order to process the point clouds efficiently into a 2.5D GIS software. Thanks to this workflow, the average cliff recession rate was estimated with a good confidence level as well as erosion rates in the function of rock types.
Chapter 8 by Pozzobon et al. (2021) presents a multiscale quantitative approach for estimating volume variations from the microscopic scale of rock samples to the kilometer scale of volcanic features using the same approach. The microscopic analysis is performed on the carbonate rock samples analogue to historical buildings materials. Stone surface models were successively acquired from a confocal laser scanning microscope between series of immersion cycles simulating erosion and recession due to rainwater aggression. The key point of this study is to consider this microscopic surface model as a DEM in a GIS software and to use a reference surface for calibrating the different acquisitions of the same sample, retrieving the lost volumes and the related recession rates. The same strategy was used at a kilometer scale for quantifying the real volume of collapsed sections of lava tubes from DEMs of Earth, Moon, and Mars.
1.4 3D Modelling of the Subsurface from Surface Data
One of the goals of DOMs, DEMs, and SMs is to provide surface geological data that can be used to reconstruct 3D models of the subsurface (e.g. Bistacchi et al., 2010, 2015; Penasa et al., 2017; Franceschi et al., 2020; Pozzobon et al., 2020). Since reconstructing 3D models of the subsurface from surface data always requires some sort of extrapolation (e.g. Bistacchi et al., 2008), at least the quality of the input data must be verified very strictly, and for this reason a quantitative and high‐resolution topography such as that provided by a DOM, a high‐resolution DEM, or an SM is invaluable. This requirement is even more pronounced when the goal is to model complicated geological structures.
In Chapter 10 by Baumberger et al. (2021), a model of fault zones in the Aar Massif (Central Alps) is discussed. Noteworthy is the fact that the authors developed a methodology allowing an estimation to be made of the uncertainty in the modelling, demonstrating that, even if extrapolation from a DEM or DOM is not completely free from uncertainty, the modelling is in any case some orders of magnitude more reliable than the extrapolation of structural measurements collected using traditional methods in the field (e.g. compass/clinometer). In addition, the uncertainty can be further reduced, or adapted to the goals of the study, by tuning the resolution of the topographic and surface‐geology dataset. As recently pointed out by Bistacchi et al. (2020), Fondriest et al. (2020), and Martinelli et al. (2020), and also Baumberger et al. (2021), show how DOM analysis allows many aspects of a fault and fracture network, such as fault and fracture connectivity, to be quantitatively characterized in a way that cannot be attained with traditional surveys (e.g. Viseur et al., 2020).
Chapter 11 by Penasa et al. (2021) provides a detailed explanation of how the 3D geological model of the lobes of the 67P Churyumov–Gerasimenko comet has been reconstructed from an SM dataset. This is a recent and major achievement in the geology of small bodies of the Solar System, since it allowed some important constraints to be placed on the accretion and evolution of a comet (Massironi et al., 2015; Penasa et al., 2017) as well as on its overall mechanical behavior (Franceschi et al., 2020). In this case, the subellipsoidal geometry of the lobes of the comet and their complete accessibility (in remote sensing terms) reduces the uncertainty in 3D modelling, since the authors are interpolating the internal structure based on a complete mapping of the outer surface on an SM. On the other hand, the authors describe how they used an implicit surface modelling algorithm to tackle the curvilinear geometries of the comet layering.
1.5 Summary and Perspectives
To our knowledge this volume represents the first attempt at collecting contribution from Earth and planetary science communities working on 3D datasets in diverse contexts, scales, and for different purposes. It is high time that different techniques, software, workflows, and methodologies are shared among these communities. We hope our volume will foster collaborative efforts towards 3D data exploitation, inspiring young researchers interested in 3D data analysis with its introductive and general chapters and senior scientists with the advanced case studies presented here.
Acknowledgments
First of all, we would like to warmly thank all the authors of the very interesting chapters in this volume. The volume was inspired by sessions at the EGU General Meetings, and all contributors to these sessions are also acknowledged. We would like to thank particularly Claudio Rosenberg, who encouraged us to convene these sessions. Finally, we would like to acknowledge the editorial staff at Wiley for assisting us in this effort.
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