Three dimensional inversion of multisource time domain electromagnetic data

Oldenburg, Douglas W.; Haber, Eldad; Shekhtman, R.

doi:10.1190/geo2012-0131.1

Cited by 186 publications

(70 citation statements)

References 28 publications

(31 reference statements)

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“…Magnetic fields generally have little sensitivity to resistive objects (e.g., Oldenburg et al, 2013). In contrast, electric field components are sensitive to resistors and conductors (e.g., Weiss and Constable, 2006).…”

Section: Data Selectionmentioning

confidence: 99%

3D inversion and resolution analysis of land-based CSEM data from the Ketzin CO2 storage formation

2014

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We evaluated 3D inversion of land controlled-source electromagnetic (CSEM) data collected across the Ketzin CO 2 storage formation. A newly developed, parallel and distributed 3D inversion code, which is based on a direct forward solver, has been used. This inversion scheme allowed us to calculate the Jacobian matrix explicitly within a reasonable time and use it to calculate regularization parameters, inspect survey coverage, and carry out resolution analysis. After demonstrating that the magnetic field components are sensitive to conductors only, whereas the electric field components are sensitive to all features of interest, we continued to work with electric field data only. Estimates of data uncertainty obtained from robust processing were used for automated data preselection and weighting during inversion. We tested different regularization techniques and a range of starting models to explore the model space and assess the influence of regularization on the inversion images. We further demonstrated an approach for handling numerical singularities due to sources located inside the inversion domain. We estimated survey coverage, horizontal and vertical resolution, and depth penetration using cumulative sensitivity, point spread functions, and depth-resolution plots. Based on data fit analysis, we determined a preferred subsurface conductivity model, which we compared to an independent regional structural geologic model, and we provided an interpretation for the structures resolved. The inversion approach we used provides robust results in good agreement with known geology, offers new possibilities for model assessment, and should be transferable to other CSEM data sets.

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Section: Data Selectionmentioning

confidence: 99%

3D inversion and resolution analysis of land-based CSEM data from the Ketzin CO2 storage formation

2014

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“…Our inversion will methodology will largely follow that described in (Oldenburg et al, 2013). We apply a GaussNewton procedure to recover a model for the Debye chargeability, η.…”

Section: Inversion Methodologymentioning

confidence: 99%

Recovery of 3D IP distribution from airborne time-domain EM

Marchant

Haber

Oldenburg

2013

ASEG Extended Abstracts

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“…The formed linear equations in (4) given by the discretization of eq. (3) are solved by a multifrontal direct solver MUMPS 5.0.2 parallelized by OpenMP (Amestoy et al 2001(Amestoy et al , 2012, which could avoid uncertainties in pre-conditioning and convergence for iterative solutions, especially for low frequencies (Farquharson & Miensopust 2011;Oldenburg et al 2013). In this section, we will focus on the implementation of CFS-PML in details.…”

Section: Implementation Of Cfs-pmlmentioning

confidence: 99%

Application of the perfectly matched layer in 3-D marine controlled-source electromagnetic modelling

Han

et al. 2017

Geophysical Journal International

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SUMMARYIn this study, the complex frequency-shifted perfectly matched layer (CFS-PML) in stretching Cartesian coordinates, is successfully applied to three-dimensional (3D) frequency-domain marine controlled-source electromagnetic (CSEM) field modelling.The Dirichlet boundary, which is usually used within the traditional framework of EM modeling algorithms, assumes the electric or magnetic field values are zero at the boundaries. This requires the boundaries be sufficiently far away from the sources in the area of interest. To mitigate the boundary artifacts, a large modelling area may be necessary even though cell sizes are allowed to grow toward the boundaries due to the diffusion of the electromagnetic wave propagation. Compared with the conventional Dirichlet boundary, the PML boundary is preferred as the modelling area of interest could be restricted to the target region and only a few absorbing layers surrounding can effectively depress the artificial boundary effect without losing the numerical accuracy. Furthermore, for joint inversion of seismic and marine CSEM data, if we used the PML for CSEM field simulation instead of the conventional Dirichlet, the modeling area for these two different geophysical data collected from the same survey area could the same, which is convenient for joint inversion grid Downloaded from https://academic.oup.com/gji/article-abstract/doi/10.1093/gji/ggx382/4157794/Application-of-the-perfectly-matched-layer-in-3D by GEOMAR Bibliothek Helmholtz-Zentrum fuer Ozeanforschung user on 22 September 2017 2 G. Li and B. Li matching. We apply the CFS-PML boundary to 3D marine CSEM modelling by using the staggered finite-difference (SFD) discretization. Numerical test indicates that the modeling algorithm using the CFS-PML also shows good accuracy compared to the Dirichlet. Furthermore, the modeling algorithm using the CFS-PML shows advantages in computational time and memory saving than that using the Dirichlet boundary. For the 3D example in this study, the memory saving using the PML is nearly 42 % and the time saving is around 48% compared to using the Dirichlet.

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Three dimensional inversion of multisource time domain electromagnetic data

Cited by 186 publications

References 28 publications

3D inversion and resolution analysis of land-based CSEM data from the Ketzin CO2 storage formation

3D inversion and resolution analysis of land-based CSEM data from the Ketzin CO2 storage formation

Recovery of 3D IP distribution from airborne time-domain EM

Application of the perfectly matched layer in 3-D marine controlled-source electromagnetic modelling

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