InCorr: Interactive Data-Driven Correlation Panels for Digital Outcrop Analysis

Information

• Publication Type: Journal Paper with Conference Talk
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: November 2020
• Journal: IEEE Transactions on Visualization and Computer Graphics
• Volume: 21
• Open Access: yes
• Lecturer: Thomas Ortner
• Event: VAST 2020: IEEE Symposium on Visual Analytics Science and Technology
• DOI: 10.1109/TVCG.2020.3030409
• Call for Papers: Call for Paper
• Conference date: October 2020
• Pages: 1 – 10

Abstract

Geological analysis of 3D Digital Outcrop Models (DOMs) for reconstruction of ancient habitable environments is a key aspect of the upcoming ESA ExoMars 2022 Rosalind Franklin Rover and the NASA 2020 Rover Perseverance missions in seeking signs of past life on Mars. Geologists measure and interpret 3D DOMs, create sedimentary logs and combine them in ‘correlation panels’ to map the extents of key geological horizons, and build a stratigraphic model to understand their position in the ancient landscape. Currently, the creation of correlation panels is completely manual and therefore time-consuming, and inflexible. With InCorr we present a visualization solution that encompasses a 3D logging tool and an interactive data-driven correlation panel that evolves with the stratigraphic analysis. For the creation of InCorr we closely cooperated with leading planetary geologists in the form of a design study. We verify our results by recreating an existing correlation analysis with InCorr and validate our correlation panel against a manually created illustration. Further, we conducted a user-study with a wider circle of geologists. Our evaluation shows that InCorr efficiently supports the domain experts in tackling their research questions and that it has the potential to significantly impact how geologists work with digital outcrop representations in general.

Additional Files and Images

Weblinks

• Entry in reposiTUm (TU Wien Publication Database)
• DOI: 10.1109/TVCG.2020.3030409

BibTeX

@article{Ortner2020,
  title =      "InCorr: Interactive Data-Driven Correlation Panels for
               Digital Outcrop Analysis",
  author =     "Thomas Ortner and Andreas Walch and Rebecca Nowak and Robert
               Barnes and Thomas H\"{o}llt and Eduard Gr\"{o}ller",
  year =       "2020",
  abstract =   "Geological analysis of 3D Digital Outcrop Models (DOMs) for
               reconstruction of ancient habitable environments is a key
               aspect of the upcoming ESA ExoMars 2022 Rosalind Franklin
               Rover and the NASA 2020 Rover Perseverance missions in
               seeking signs of past life on Mars. Geologists measure and
               interpret 3D DOMs, create sedimentary logs and combine them
               in ‘correlation panels’ to map the extents of key
               geological horizons, and build a stratigraphic model to
               understand their position in the ancient landscape.
               Currently, the creation of correlation panels is completely
               manual and therefore time-consuming, and inflexible. With
               InCorr we present a visualization solution that encompasses
               a 3D logging tool and an interactive data-driven correlation
               panel that evolves with the stratigraphic analysis. For the
               creation of InCorr we closely cooperated with leading
               planetary geologists in the form of a design study. We
               verify our results by recreating an existing correlation
               analysis with InCorr and validate our correlation panel
               against a manually created illustration. Further, we
               conducted a user-study with a wider circle of geologists.
               Our evaluation shows that InCorr efficiently supports the
               domain experts in tackling their research questions and that
               it has the potential to significantly impact how geologists
               work with digital outcrop representations in general.",
  month =      nov,
  journal =    "IEEE Transactions on Visualization and Computer Graphics",
  volume =     "21",
  doi =        "10.1109/TVCG.2020.3030409",
  pages =      "1--10",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2020/Ortner2020/",
}