Regularized focusing inversion of 3‐D gravity tensor data

Zhadanov, Michael S.; Eliss, Robert G.; Mukherjee, Souvik; Pavlov, D. A.

doi:10.1190/1.1817366

Cited by 11 publications

(17 citation statements)

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“…Portniaguine & Zhdanov (1999, 2002 proposed a focusing geophysical inversion using the minimum gradient support (MGS) functional and used the MGS functional in gravity and magnetic inversion (Zhdanov et al 2004). These algorithms provide smooth solutions, but few are suitable for clearly imaging geo-electrical interfaces.…”

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confidence: 99%

A regularized three-dimensional magnetotelluric inversion with a minimum gradient support constraint

Zhang¹,

Koyama²,

Utada³

et al. 2012

Geophysical Journal International

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SUMMARY Most 3‐D magnetotelluric (MT) inversions are classified as a regularized inversion with a smoothness constraint. These inverse algorithms provide smooth solutions but cannot clearly image sharp geo‐electrical interfaces. In this paper, we introduce the minimum gradient support (MGS) functional to regularize the 3‐D MT inverse problem. This functional has a property whereby the functional seeks a structure with minimum volume containing large conductivity gradients. Therefore, the MGS functional can be used to search for a model with a sharp boundary. We apply the MGS functional to 3‐D MT inversion to obtain a clear and accurate image of geo‐electrical interfaces. In addition, the modified scattering equation approach introduced in the modified iterative dissipative method (MIDM) is applied to forward calculation, which is based on integral equation (IE) formulation and allows us to efficiently reduce the time required for forward calculation with high accuracy. The quasi‐Newton iterative method is used to optimize the objective functional. It is a kind of iterative method with simplified calculation of the inverse Hessian matrix using Broyden–Fletcher–Goldfarb–Shanno (BFGS) update. The convergence of this iterative method is guaranteed with inexact line searches. We also modify the adaptive approach for optimum selection of the regularization parameter so as to fit the inverse algorithm of this study. Three synthetic models are investigated, and the obtained results are compared with those obtained by a smoothing inversion. Based on the comparison, we confirm that the MGS inversion can provide higher resolution when geo‐electrical interfaces are sharp. This property will help us to determine reliable electrical structures by the MT exploration method.

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confidence: 99%

A regularized three-dimensional magnetotelluric inversion with a minimum gradient support constraint

Zhang¹,

Koyama²,

Utada³

et al. 2012

Geophysical Journal International

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“…proposed an inversion scheme to trace 3D density interfaces using gravity grids. Zhdanov et al (2002) conducted 3D density inversion of gravity data based on the focusing inversion method and obtained good results. Compared with conventional inversion of gravity data, however, inversion of high spatial resolution gravity gradient tensor data can better establish the boundaries of underground anomalies and recover the 3D density distribution.…”

Section: Introductionmentioning

confidence: 91%

“…Many have studied the depth weighting function and most studies have focused on the decaying resolution with depth ( (Li and Oldenbrug, 1998;Zhdanov, 200s). These methods basically operate on the boundary constraints functional (Li and Oldenbrug, 1998;Zhdanov, 2002). Using gravity gradient data and the depth weighting function of Commer (2011), we propose a modifi ed depth weighting function based on the gravity gradient eigenvector that directly acts on the misfi t function…”

Section: Modifi Ed Depth Weighting Functionmentioning

confidence: 99%

3D density inversion of gravity gradient data using the extrapolated Tikhonov regularization

Liu

Liang

et al. 2015

Appl. Geophys.

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We use the extrapolated Tikhonov regularization to deal with the ill-posed problem of 3D density inversion of gravity gradient data. The use of regularization parameters in the proposed method reduces the deviations between calculated and observed data. We also use the depth weighting function based on the eigenvector of gravity gradient tensor to eliminate undesired effects owing to the fast attenuation of the position function. Model data suggest that the extrapolated Tikhonov regularization in conjunction with the depth weighting function can effectively recover the 3D distribution of density anomalies. We conduct density inversion of gravity gradient data from the Australia Kauring test site and compare the inversion results with the published research results. The proposed inversion method can be used to obtain the 3D density distribution of underground anomalies.

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“…For this, we considered only the data processed by the Fourier‐domain method. For further comparison, we inverted g zz data using 3D regularized inversion (Zhdanov et al . 2004).…”

Section: Case Study–broken Hill Falcon Surveymentioning

confidence: 99%

“…2D or 3D inversion of gravity gradiometry data to 2D or 3D density models is presented as the only practical tool for quantitative interpretation. A number of publications have discussed 3D inversion with smooth (e.g., Li 2001), and focusing (e.g., Zhdanov, Ellis and Mukherjee 2004) regularization. However, an interpretation workflow including 3D inversion can be complicated and time consuming because it is dependent on a priori models and other geological constraints.…”

Section: Introductionmentioning

confidence: 99%

Potential field migration for rapid imaging of gravity gradiometry data

Zhdanov

Liu

Wilson

et al. 2011

Geophysical Prospecting

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A B S T R A C TThe geological interpretation of gravity gradiometry data is a very challenging problem. While maps of different gravity gradients may be correlated with geological structures present, maps alone cannot quantify 3D density distributions related to geological structures. 3D inversion represents the only practical tool for the quantitative interpretation of gravity gradiometry data. However, 3D inversion is a complicated and time-consuming procedure that is very dependent on the a priori model and constraints used. To overcome these difficulties for the initial stages of an interpretation workflow, we introduce the concept of potential field migration, and demonstrate how it can be applied for rapid 3D imaging of entire gravity gradiometry surveys. This method is based on a direct integral transformation of the observed gravity gradients into a subsurface density distribution that can be used for interpretation or as an a priori model for subsequent 3D regularized inversion. For large-scale surveys, we show how migration runs on the order of minutes compared to hours for 3D regularized inversion. Moreover, the results obtained from potential field migration are comparable to those obtained from regularized inversion with smooth stabilizers. We present a case study for the 3D imaging of FALCON airborne gravity gradiometry data from Broken Hill, Australia. We observe good agreement between results obtained from potential field migration and those generated by 3D regularized inversion.

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Regularized focusing inversion of 3‐D gravity tensor data

Cited by 11 publications

References 0 publications

A regularized three-dimensional magnetotelluric inversion with a minimum gradient support constraint

A regularized three-dimensional magnetotelluric inversion with a minimum gradient support constraint

3D density inversion of gravity gradient data using the extrapolated Tikhonov regularization

Potential field migration for rapid imaging of gravity gradiometry data

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