| Title | How subsurface voxel modelling and uncertainty analysis contribute to habitat-change prediction and monitoring |
| Download | Download (whole publication) |
| |
| Licence | Please note the adoption of the Open Government Licence - Canada
supersedes any previous licences. |
| Author | Van Lancker, V; Kint, L; Montereale-Gavazzi, G; Terseleer, N; Chademenos, V; Missiaen, T; De Mol, R; De Tré, G; van Heteren, S; van Maanen, P P; Stafleu, J |
| Source | Program and abstracts: 2017 GeoHab Conference, Dartmouth, Nova Scotia, Canada; by Todd, B J ; Brown, C J; Lacharité, M; Gazzola, V; McCormack, E; Geological Survey of Canada, Open File 8295, 2017 p. 116, https://doi.org/10.4095/305937  Open Access |
| Links | GeoHab 2017
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| Year | 2017 |
| Publisher | Natural Resources Canada |
| Meeting | 2017 GeoHab: Marine Geological and Biological Habitat Mapping; Dartmouth, NS; CA; May 1-4, 2017 |
| Document | open file |
| Lang. | English |
| Media | on-line; digital |
| Related | This publication is contained in Program and abstracts: 2017
GeoHab Conference, Dartmouth, Nova Scotia, Canada |
| File format | pdf |
| Subjects | marine geology; surficial geology/geomorphology; environmental geology; geophysics; economic geology; mapping techniques; oceanography; marine environments; coastal studies; conservation; marine
organisms; marine ecology; resource management; biological communities; environmental studies; ecosystems; modelling; bedrock geology; lithostratigraphy; marine sediments; sedimentary environment; aggregates; bathymetry; boreholes; Biology;
monitoring; Decision making |
| Illustrations | photographs; 3-D models |
| Program | Offshore Geoscience |
| Released | 2017 09 26 |
| Abstract | For long-term predictions of geological resource quantity and quality, a voxel model was built for the subsurface of the Belgian part of the North Sea (Belgian Science Policy 'TILES'). The 3D voxels
contain lithostratigraphic information over the entire data volume (up to -70 m), but also sediment characteristics and a suite of sediment-dynamic parameters in the upper voxel (i.e., the seabed). Derivative data products include probability maps of
sediment type and resource-suitability maps that reflect a combination of user-specific criteria.
From stakeholder consultation, it became clear that the model has numerous potential applications, provided that resolution requirements from very
small-scale to large-scale can be met (e.g., assessing local aggregate quality, but also decision-making on long-term resource use). To accommodate these varying needs, and also to enhance computational speed, voxel models were post-processed to vary
the size of the voxels dependent on user demand. As such, data density, but also geological heterogeneity can steer the voxel size.
Uncertainty is parameterised to generate data products with confidence limits. Uniquely, the quality of each of
the data fields in the databases is quantified in order to be propagated in the voxel model. Additionally, interpolation-related uncertainty, as well as uncertainty in the mapping of the lithological class and stratigraphic uncertainty (i.e. the
geological layer to which a so-called lithoclass belongs) is incorporated. Visualisation of these uncertainties is highly challenging, and is addressed through variation in the sizes of the voxels or through transparency.
An ample spectrum of
benefits exists for habitat mapping. Firstly, the geology of the shallow subsurface is accounted for, being highly relevant in predicting and constraining habitat change in long-term projections of resource use. The approach is also pertinent to
assist present-day seabed mapping. Uncertainty mapping is critical to judge on the accuracy of seabed maps, and it assists in interpreting habitat changes within an envelope of natural variability, imposed by both the geological nature of the seabed
and the highly dynamic sedimentary environment. |
| Summary | (Plain Language Summary, not published)
The sixteenth annual GeoHab Conference was held this year (2017) at the Waterfront Campus of the Nova Scotia Community College in Dartmouth, Nova Scotia,
Canada. |
| GEOSCAN ID | 305937 |
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