Gauss–Newton With Preconditioned Conjugate Gradient Magnetotelluric Inversion for 3-D Axial Anisotropic Conductivities

We present a regularized inversion method for 3-D magnetotelluric (MT) data with axial anisotropic conductivities based on the edge-based finite element (FE) method. The Gauss–Newton (GN) approach is used to minimize the inversion objective function, including data misfit and regularization penaltie...

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Veröffentlicht in:IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-14
Hauptverfasser: Zhou, Junjun, Bai, Ningbo, Han, Bo, Hu, Xiangyun, Xiao, Tiaojie, Huang, Guoshu, Li, Jianping
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container_title IEEE transactions on geoscience and remote sensing
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Bai, Ningbo
Han, Bo
Hu, Xiangyun
Xiao, Tiaojie
Huang, Guoshu
Li, Jianping
description We present a regularized inversion method for 3-D magnetotelluric (MT) data with axial anisotropic conductivities based on the edge-based finite element (FE) method. The Gauss–Newton (GN) approach is used to minimize the inversion objective function, including data misfit and regularization penalties, considering both structural complexity and anisotropic penalties. The most time-intensive task in the 3-D MT inversion process is solving the large sparse system of linear equations. To speed up the inversion calculation, a hybrid direct–iterative solver combined with a block-diagonal preconditioner that has not yet been applied in anisotropic inversion is developed to accelerate the solutions for the sparse linear system resulting from forward modeling and sensitivity computations. In each GN iteration, a preconditioned conjugate gradient (PCG) method is adopted to overcome the difficulty in the sensitivity matrix storage for the anisotropic scene and obtain a model update without explicitly calculating and storing the sensitivity matrix. Before the inversion test, we use a model to demonstrate that the hybrid solver is computationally beneficial in terms of memory usage and time spent when compared with the direct solver. The good convergence properties and efficiency of the Gauss–Newton with the conjugate gradient (GN–PCG) inversion scheme are demonstrated by two synthetic models and USArray data. The proposed inversion scheme can be an important supplement to existing anisotropic inversion algorithms and provide technical support for MT data interpretation.
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subjects Algorithms
Anisotropy
Aquatic reptiles
Conjugate gradient method
Data interpretation
Fines & penalties
Finite element method
Iterative methods
Linear equations
Objective function
Regularization
Sensitivity
Solvers
Storage
Technical services
title Gauss–Newton With Preconditioned Conjugate Gradient Magnetotelluric Inversion for 3-D Axial Anisotropic Conductivities
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