A finite-volume approach for 2D magnetotellurics modeling with arbitrary topographies

Du, Huibin; Ren, Zhengyong; Tang, Jingtian

doi:10.1007/s11200-014-1041-9

Cited by 12 publications

(6 citation statements)

References 42 publications

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“…Deviation of the apparent resistivities from 100 Ωm reflects the topographic effect. The spike in the TM mode apparent resistivity occurs at the points where the slope changes because of the discontinuity of 𝐸 [24] [25]. In the TE mode, the apparent resistivity anomaly is positive over the hill due to the lack of lateral current flow [24].…”

Section: -D Trapezoidal Hill Modelmentioning

confidence: 99%

Two-dimensional magnetotelluric inversion using unstructured triangular mesh implemented in Julia

Diba,

Nurhasan,

Uyeshima

et al. 2024

J. Phys.: Conf. Ser.

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This study presents a 2-D magnetotelluric inversion code tailored for unstructured triangular meshes, developed using Julia, a high-level, high-performance programming language designed for scientific and numerical computation. The forward modeling engine utilizes a node-based finite element method to solve electromagnetic fields throughout the modeling domain. The inversion process employs the Gauss-Newton optimization algorithm, iteratively updating the model via the minimization of a regularized least-squares objective function. We verified the accuracy of the forward modeling using two reference models and performed a synthetic inversion experiment to validate our inversion method and stress the necessity of accurately handling topography-influenced data. Through its application to constructing an electrical resistivity model beneath the northwestern end of Sumatra Island, Indonesia, we demonstrate the practicality of our code for inverting field datasets.

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Section: -D Trapezoidal Hill Modelmentioning

confidence: 99%

Two-dimensional magnetotelluric inversion using unstructured triangular mesh implemented in Julia

Diba,

Nurhasan,

Uyeshima

et al. 2024

J. Phys.: Conf. Ser.

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“…Therefore, we only give some references e.g. Zeng et al 2015, Zhang et al (2013), Neitzel (2010), Neitzel and Schaffrin (2016), Snow and Schaffrin (2012), Shen et al (2011), Schaffrin et al (2014), Schaffrin and Felus (2008), Mahboub (2012Mahboub ( , 2014Mahboub ( , 2016, Mahboub et al (2012Mahboub et al ( , 2015, Mahboub and Sharifi (2013a, b), Paláncz and Awange (2012), Amiri-simkooei and Jazaeri (2012), Fang (2011Fang ( , 2013Fang ( , a, b c, 2015, Fang et al ( , 2016, Lu et al (2014), Zhou and Fang (2015) and Fang and Wu (2015) etc. In the rest of this paper we define these two parts for a DEIV model.…”

Section: Dynamic Errors-in-variables (Deiv) Modelmentioning

confidence: 99%

A solution to dynamic errors-in-variables within system equations

2017

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We noticed that if INS data is used as system equations of a Kalman filter algorithm for integrated direct geo-referencing, one encounters with a dynamic errors-invariables (DEIV) model. Although DEIV model has been already considered for observation equations of the Kalman filter algorithm and a solution namely total Kalman filter (TKF) has been given to it, this model has not been considered for system equations (dynamic model) of the Kalman filter algorithm. Thus, in this contribution, for the first time we consider DEIV model for both observation equations and system equations of the Kalman filter algorithm and propose a least square prediction namely integrated total Kalman filter in contrast to the TKF solution of the previous approach. The variance matrix of the unknown parameters are obtained. Moreover, the residuals for all variables are predicted. In a numerical example, integrated direct geo-referencing problem is solved for a GPS-INS system. Keywords Dynamic errors-in-variables Á System equations Á Integrated total Kalman filter Á Direct geo-referencing

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“…The FE method is more accurate for modeling a complicated medium, especially for topography, because of the greater flexibility of mesh discretization. Meanwhile, there are some different approaches to the numerical approximation of the 2D magnetotelluric forward problem [15][16][17][18]. There are some comprehensive reviews of these numerical methods and their advantages and disadvantages [19,20].…”

Section: Introductionmentioning

confidence: 99%

Finite Difference Algorithm on Non-Uniform Meshes for Modeling 2D Magnetotelluric Responses

2018

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A finite-difference approach with non-uniform meshes was presented for simulating magnetotelluric responses in 2D structures. We presented the calculation formula of this scheme from the boundary value problem of electric field and magnetic field, and compared finite-difference solutions with finite-element numerical results and analytical solutions of a 1D model. First, a homogeneous half-space model was tested and the finite-difference approach can provide very good accuracy for 2D magnetotelluric modeling. Then we compared them to the analytical solutions for the two-layered geo-electric model; the relative errors of the apparent resistivity and the impedance phase were both increased when the frequency was increased. To conclude, we compare our finite-difference simulation results with COMMEMI 2D-0 model with the finite-element solutions. Both results are in close agreement to each other. These comparisons can confirm the validity and reliability of our finite-difference algorithm. Moreover, a future project will extend the 2D structures to 3D, where non-uniform meshes should perform especially well.

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A finite-volume approach for 2D magnetotellurics modeling with arbitrary topographies

Cited by 12 publications

References 42 publications

Two-dimensional magnetotelluric inversion using unstructured triangular mesh implemented in Julia

Two-dimensional magnetotelluric inversion using unstructured triangular mesh implemented in Julia

A solution to dynamic errors-in-variables within system equations

Finite Difference Algorithm on Non-Uniform Meshes for Modeling 2D Magnetotelluric Responses

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