 |
| Titel |
Integration of Geophysical Data into Structural Geological Modelling through
Bayesian Networks |
| VerfasserIn |
Miguel de la Varga, Florian Wellmann, Ruth Murdie |
| Konferenz |
EGU General Assembly 2016
|
| Medientyp |
Artikel
|
| Sprache |
en
|
| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250135781
|
| Publikation (Nr.) |
 EGU/EGU2016-16686.pdf |
|
|
|
|
|
| Zusammenfassung |
| Structural geological models are widely used to represent the spatial distribution of relevant
geological features. Several techniques exist to construct these models on the basis of
different assumptions and different types of geological observations (e.g. Jessell et al., 2014).
However, two problems are prevalent when constructing models: (i) observations and
assumptions, and therefore also the constructed model, are subject to uncertainties, and (ii)
additional information, such as geophysical data, is often available, but cannot be considered
directly in the geological modelling step. In our work, we propose the integration of all
available data into a Bayesian network including the generation of the implicit geological
method by means of interpolation functions (Mallet, 1992; Lajaunie et al., 1997; Mallet,
2004; Carr et al., 2001; Hillier et al., 2014). As a result, we are able to increase the
certainty of the resultant models as well as potentially learn features of our regional
geology through data mining and information theory techniques. MCMC methods
are used in order to optimize computational time and assure the validity of the
results.
Here, we apply the aforementioned concepts in a 3-D model of the Sandstone Greenstone
Belt in the Archean Yilgarn Craton in Western Australia. The example given, defines the
uncertainty in the thickness of greenstone as limited by Bouguer anomaly and the
internal structure of the greenstone as limited by the magnetic signature of a banded
iron formation. The incorporation of the additional data and specially the gravity
provides an important reduction of the possible outcomes and therefore the overall
uncertainty.
References
Carr, C. J., K. R. Beatson, B. J. Cherrie, J. T. Mitchell, R. W. Fright, C. B. McCallum,
and R. T. Evans, 2001, Reconstruction and representation of 3D objects with radial
basis functions: Proceedings of the 28th annual conference on Computer graphics
and
interactive techniques, 67–76.
Jessell, M., Aillères, L., de Kemp, E., Lindsay, M., Wellmann, F., Hillier, M., ... &
Martin, R. (2014). Next Generation Three-Dimensional Geologic Modeling and
Inversion.
Lajaunie, C., G. Courrioux, and L. Manuel, 1997, Foliation fields and 3D cartography
in
geology: Principles of a method based on potential interpolation: Mathematical
Geology,
29, 571–584.
Mallet, J.-L., 1992, Discrete smooth interpolation in geometric modelling:
Computer-Aided
Design, 24, 178–191
Mallet, L. J., 2004, Space-time mathematical framework for sedimentary geology:
Mathematical Geology, 36, 1–32. |
|
|
|
|
|
|