A new joint inversion algorithm applied to the ...

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XVI International Conference on Computational Methods in Water Resources (CMWR-XVI) Ingeniørhuset

A new joint inversion algorithm applied to the interpretation of dc resistivity and refraction data
Paper
Author:Thomas Günther <t.guenther@gga-hannover.de> (Leibniz Institute for Applied Geosciences)
Laurence Bentley <lbentley@ucalgary.ca> (University of Calgary)
Presenter:Thomas Günther <t.guenther@gga-hannover.de> (Leibniz Institute for Applied Geosciences)
Date: 2006-06-18     Track: Special Sessions     Session: Hydrogeophysical data fusion
DOI:10.4122/1.1000000399
DOI:10.4122/1.1000000400

A new joint inversion algorithm that can be applied to independent physical parameters has been developed. The method allows the interchange of structural information between otherwise independent inversions. It is based on the principles of model-sided robust modeling and the structures in the opposing inversion model are supported and not strictly enforced. The algorithm uses unstructured meshes that allow the incorporation of topography and other information on the geometry of the subsurface. A synthetic study on a simple model structure is done using refraction tomography and dc resistivity inversion. On one hand, an existing boundary in one parameter does not enforce the same structure in the other. However, the inversion results are significantly improved yielding a clearer separation of units by means of cluster analysis. The algorithm will be applied to the Pine Creek site in Calgary, Alberta, Canada. The site is underlain by approximately 6 m of unsaturated gravel overlying a shaly, weathered sandstone bedrock. Repeated profiles using electrical resistivity, seismic refraction and ground penetrating radar surveys were conducted in support of a groundwater study. The joint electrical resistivity-seismic refraction inversion will be constrained using data from well logs and GPR reflection events. The joint inversion reduces the ambiguity in the inversions and consequently improves the final stratigraphic model. Improved stratigraphic models will result in an improved ability to model groundwater flow and transport.