Essential Structure Finite Element (ESFE) Algorithm and Its Application to MT 1‐D Continuous Medium Forward Modeling

Chen, Xiaobin; Zhao, Guilin

doi:10.1002/cjg2.527

Cited by 4 publications

(6 citation statements)

References 1 publication

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“…where R is called roughness kernel matrix. A concise equation can be derived using essential structure shape function [16,17] for a continuous medium, which makes the construction of the model object function more convenient and precise. The concise equation to calculate R is…”

Section: Definition and Calculation Of Roughness Kernel Matrixmentioning

confidence: 99%

“…In this paper, ARIA is used to solve the MT-1D inversion problem with the flattest model constraint of the continuous medium for discussing its validity and advantages, which will be a foundation for multi-dimensional inversion work. The essential Structure Finite Element (ESFE) method [16,17] is used to solve the forward problem of the continuous medium, and the Jupp-Vozoff method [18] is used to obtain the Jaccobi Matrix G 0 . The logarithm of conductivity is adopted as the model parameter, that is m = ln(σ), σ is the conductivity vector of nodes in the discrete solution domain.…”

Section: Aria For Mt-1d Inversionmentioning

confidence: 99%

“…where f k,j is the ES shape function [16] of the jth node of the kth ES, f k.j0 is the ES shape function of the center node of the kth ES; S d k,j is the same with literatures [16,17], that means R can be obtained in the same time with forward computation by ESFE.…”

Section: The Minimum Of the Integrated Square Of The First Derivativementioning

confidence: 99%

See 2 more Smart Citations

The Adaptive Regularized Inversion Algorithm (ARIA) for Magnetotelluric Data

Chen

Zhao

Tang

et al. 2005

Chinese J. Geophys.

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In this paper, a new inversion method, Adaptive Regularized Inversion Algorithm (ARIA), is presented to overcome the difficulty of the determination for the regularized factor. Firstly, a new data variance disposing method, data variance normalization method, is put forward. This method uses a new way to calculate the influence matrix of data variance in inversion. Thus, the data variance only influences data fitting, and has no influence on the weight between data object function and the model constraint object function. So the influencing factors of the determination of the regularized factor are reduced. Secondly, the definition of the roughness kernel matrix is presented in the course of constructing model constraint object function, and a concise equation of it is derived. Thus the construction of the model object function becomes very simple and direct. Thirdly, two adaptive methods of the regularized factor are put forward based on the relations of data object function, the model constraint object function, and the regularized factor. Finally, ARIA is used to solve an magnetotelluric (MT) one‐dimensional inversion problem by the constraint of the flattest model. Several examples are illustrated to exemplify the effect of ARIA.

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Section: Definition and Calculation Of Roughness Kernel Matrixmentioning

confidence: 99%

Section: Aria For Mt-1d Inversionmentioning

confidence: 99%

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The Adaptive Regularized Inversion Algorithm (ARIA) for Magnetotelluric Data

Chen

Zhao

Tang

et al. 2005

Chinese J. Geophys.

Self Cite

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show abstract

“…In this paper, the non-linear conjugate gradient (NLCG) 2-D inversion [15] was used in data interpretation. In the process of 2-D inversion, a result of inversion of one-dimensional self-adaptive normalized continuous media [17] was used. A starting model for 2-D inversion was formed after interpolating the result of 1-D.…”

Section: Data Inversion and Interpretationmentioning

confidence: 99%

A Preliminary Investigation on Electric Structure of the Crust and Upper Mantle in Arxan Volcanic Area

Tang¹,

Wang²,

Chen³

et al. 2005

Chinese J. Geophys.

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Magnetotelluric (MT) measurement has been carried out at 7 sites along a profile in NNW direction in Arxan active volcanic area. The data were processed and analyzed by using a two‐dimensional (2‐D) inversion. The obtained 2‐D result shows that two volcanic zones, an older and a younger volcanic zone with their magma conduits down to depth, exist in the study area. The newly found younger active volcanic zone still maintains its higher temperature state at depth of 10~12km, where there may exist abundant fluid, and a conduit for supplying heat from the mantle to the crust exists at depth of 30~50km. But a magma conduit being in cooling may exist down to depth of 30km beneath the older volcanic zone. The two volcanic zones may have the same resource at depth.

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“…Since being extended to electromagnetic field problems (Coggon 1971), FEM has gradually become the main forward modeling method of electromagnetic method, and many scholars have improved and optimized it (Rodi 1976;Chen 1981;Pridmore et al 1981;Wannamaker et al 1986;Chen et al 2000;Mitsuhata 2000; Mehanee and Zhdanov 2002;Avdeev 2005).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Finite Element Method for Magnetotelluric Forward Modeling with Gradient Recovery

Zhang,

Ji,

Wang

et al. 2024

Preprint

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In the frequency domain electromagnetic method such as magnetotelluric method, finite element method is the main numerical simulation method because it can better simulate the complex physical distribution and complex boundary shape of the geoelectric model. In the finite element method, the quality and quantity of mesh generation will directly affect the accuracy and efficiency of the solution. Theoretically, the accuracy of the finite element solution will gradually approach the real solution with the mesh encryption, but the increase of the mesh number will directly affect the speed of the finite element solution and computer memory. In addition, in the frequency domain electromagnetic method, the decay speed of electromagnetic waves is different at different frequencies, and the use of the same grid at different frequencies will also cause errors. The adaptive finite element method based on a posteriori error estimation can overcome the above shortcomings and improve the calculation accuracy and efficiency of forward modeling. In this paper, starting from the principle of superconvergent patch recovery algorithm, the superconvergent patch recovery algorithm is deduced in detail, the processing of the boundary element in the superconvergent patch recovery algorithm is used as the indicator factor of the adaptive finite element posterior error estimation, and a mesh encryption strategy is proposed, the encryption strategy is simple and reliable. Finally, using the characteristics of the superconvergent patch recovery algorithm, it is used in the calculation of auxiliary field of magnetotelluric method. Through the forward modeling of three geoelectric models. It is verified that the adaptive finite element method based on the a posteriori error of the superconvergent patch recovery algorithm combined with the auxiliary field calculation method of the superconvergent patch recovery algorithm can effectively improve the accuracy and feasibility of the finite element forward modeling of the superconvergent patch recovery algorithm.

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Essential Structure Finite Element (ESFE) Algorithm and Its Application to MT 1‐D Continuous Medium Forward Modeling

Cited by 4 publications

References 1 publication

The Adaptive Regularized Inversion Algorithm (ARIA) for Magnetotelluric Data

The Adaptive Regularized Inversion Algorithm (ARIA) for Magnetotelluric Data

A Preliminary Investigation on Electric Structure of the Crust and Upper Mantle in Arxan Volcanic Area

Adaptive Finite Element Method for Magnetotelluric Forward Modeling with Gradient Recovery

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