Comparison of 2D MT inversion approaches using spatially constant and locally 
varying regularization parameters

Jamie, M.; Oskooi, B.; Becken, Michael

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Conference Paper

Comparison of 2D MT inversion approaches using spatially constant and locally varying regularization parameters

Authors

Jamie, M.
24. Kolloquium, 2011, Schmucker-Weidelt-Kolloquium für Elektromagnetische Tiefenforschung, External Organizations;

Oskooi, B.
24. Kolloquium, 2011, Schmucker-Weidelt-Kolloquium für Elektromagnetische Tiefenforschung, External Organizations;

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Becken, Michael
24. Kolloquium, 2011, Schmucker-Weidelt-Kolloquium für Elektromagnetische Tiefenforschung, External Organizations;
2.2 Geophysical Deep Sounding, 2.0 Physics of the Earth, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

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http://gfzpublic.gfz-potsdam.de/pubman/item/escidoc:65290
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Citation

Jamie, M., Oskooi, B., Becken, M. (2011): Comparison of 2D MT inversion approaches using spatially constant and locally varying regularization parameters - Protokoll über das 24. Schmucker-Weidelt-Kolloquium für Elektromagnetische Tiefenforschung, 24. Schmucker-Weidelt-Kolloquium für Elektromagnetische Tiefenforschung (Neustadt a. d. Weinstraße 2011), 94-104.

Cite as: https://gfzpublic.gfz.de/pubman/item/item_65314

Abstract

Inversion of magnetotelluric (MT) data is a non-linear ill-posed inverse problem, and is commonly solved with an iterative linearized approach. In order to solve the minimization problem a regularization parameter is employed to balance between the norm of the data misfit and the norm of the model. Determination of a suitable regularization parameter is necessary to achieve both resolution and stability in inversion. In most inversion schemes, the regularization parameter is applied globally to the entire model. In this study, we test the apability of active constraint balancing (ACB) approach, which was introduced by Yi et al. (2003). The approach determines a spatially varying regularization parameter via Backus-Gilbert spread function analysis for model parameters. Here, we use the 2D MT inversion code developed by Lee et al., (2009) to test the ACB on 2D synthetic and field MT datasets.