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| Titel |
3D model of the Bernese Part of the Swiss Molasse Basin: visualization of
uncertainties in a 3D model |
| VerfasserIn |
Samuel Mock, Robin Allenbach, Lance Reynolds, Philip Wehrens, Eva Kurmann-Matzenauer, Pascal Kuhn, Michael Salomè, Gennaro Di Tommaso, Marco Herwegh |
| Konferenz |
EGU General Assembly 2016
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250127200
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| Publikation (Nr.) |
 EGU/EGU2016-7046.pdf |
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| Zusammenfassung |
| The Swiss Molasse Basin comprises the western and central part of the North Alpine
Foreland Basin. In recent years it has come under closer scrutiny due to its promising
geopotentials such as geothermal energy and CO2 sequestration. In order to adress these
topics good knowledge of the subsurface is a key prerequisite. For that matter, geological 3D
models serve as valuable tools.
In collaboration with the Swiss Geological Survey (swisstopo) and as part of the project
GeoMol CH, a geological 3D model of the Swiss Molasse Basin in the Canton of Bern has
been built. The model covers an area of 1810 km2and reaches depth of up to 6.7 km. It
comprises 10 major Cenozoic and Mesozoic units and numerous faults. The 3D model is
mainly based on 2D seismic data complemented by information from few deep wells.
Additionally, data from geological maps and profiles were used for refinement
at shallow depths. In total, 1163 km of reflection seismic data, along 77 seismic
lines, have been interpreted by different authors with respect to stratigraphy and
structures. Both, horizons and faults, have been interpreted in 2D and modelled in
3D using IHS’s Kingdom Suite and Midland Valley’s MOVE software packages,
respectively.
Given the variable degree of subsurface information available, each 3D model is subject
of uncertainty. With the primary input data coming from interpretation of reflection
seismic data, a variety of uncertainties comes into play. Some of them are difficult to
address (e.g. author’s style of interpretation) while others can be quantified (e.g.
mis-tie correction, well-tie). An important source of uncertainties is the quality of
seismic data; this affects the traceability and lateral continuation of seismic reflectors.
By defining quality classes we can semi-quantify this source of uncertainty. In
order to visualize the quality and density of the input data in a meaningful way, we
introduce quality-weighted data density maps. In combination with the geological
3D model, these contour maps serve as a good tool to define areas of sparse data
and low data quality providing one way of visualizing uncertainty in 3D models. |
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