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3D MT Anisotropic Inversion Based on Unstructured Finite-element Method

Authors: Xiaoyue Cao; Xin Huang; Changchun Yin; Liangjun Yan; Bo Zhang;

3D MT Anisotropic Inversion Based on Unstructured Finite-element Method

Abstract

ABSTRACT The conventional 3D magnetotelluric (MT) forward modeling and inversions generally assume an isotropic earth model. However, wrong results can be obtained when using an isotropic model to interpret the data influenced by the anisotropy. To effectively model and recover the earth structures including anisotropy, we develop a 3D MT inversion framework for a triaxial anisotropic model. We use the unstructured finite-element method for our forward modeling. This offers more possibility to simulate more complex underground geology and topography. To solve the inverse modeling problem, we use a limited-memory quasi-Newton algorithm (L-BFGS) with a parallel direct solver for optimization that avoids the explicit calculation of the Hessian matrix and saves the memory and computational time. We validate our algorithm via numerical experiments on both synthetic data and MT survey data from the US Array project.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Top 10%
Average
Top 10%
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