Two‐dimensional magnetotelluric inversion using reflection seismic data as constraints and application in the COSC project

Yan, Ping; Kalscheuer, Thomas; Hedin, Peter; Juanatey, María de los Garcia

doi:10.1002/2017gl072953

Cited by 29 publications

(17 citation statements)

References 43 publications

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“…One remedy to this non-uniqueness problem is to introduce additional model constraints or model regularisation (Jackson 1979;Tikhonov et al 1995;Zhdanov 2002;Menke 2012). The model constraints can enforce the conductivity distribution to vary smoothly (Constable et al 1987;de Groot-Hedlin and Constable 1990;Kalscheuer et al 2010), they can include prior geological knowledge, restrict the conductivity distribution by rock sampling tests, or be based on structural constraints (Gallardo and Meju 2011;Yan et al 2017b) and petrophysical constraints (Moorkamp 2017; Haber and Holtzman Gazit 2013) using information offered by other geophysical data sets (such as seismic, gravity, magnetic and logging data). Using a properly designed model constraint, the inversion algorithm can converge to a model that is close to the true model.…”

Section: Methods Of Model Analysismentioning

confidence: 99%

“…a regular model weighting matrix m for which an inverse exists) introduce non-unimodularity, whereas singular first-or second-order smoothness constraints give unimodular resolving kernels. As a consequence, we advocate construction of a preferred inversion model using smoothness constraints with local modifications to account for known structural contrasts (e.g., Yan et al 2017b), if applicable. Other types of regularisation may introduce unintended bias and, in the absence of compelling prior evidence, reference models should only be used for hypothesis testing.…”

Section: Effects Of Model Regularisation On Model Uncertainty and Resmentioning

confidence: 99%

See 1 more Smart Citation

Uncertainty and Resolution Analysis of 2D and 3D Inversion Models Computed from Geophysical Electromagnetic Data

Ren

Kalscheuer

2019

Surv Geophys

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A meaningful solution to an inversion problem should be composed of the preferred inversion model and its uncertainty and resolution estimates. The model uncertainty estimate describes an equivalent model domain in which each model generates responses which fit the observed data to within a threshold value. The model resolution matrix measures to what extent the unknown true solution maps into the preferred solution. However, most current geophysical electromagnetic (also gravity, magnetic and seismic) inversion studies only offer the preferred inversion model and ignore model uncertainty and resolution estimates, which makes the reliability of the preferred inversion model questionable. This may be caused by the fact that the computation and analysis of an inversion model depend on multiple factors, such as the misfit or objective function, the accuracy of the forward solvers, data coverage and noise, values of trade-off parameters, the initial model, the reference model and the model constraints. Depending on the particular method selected, large computational costs ensue. In this review, we first try to cover linearised model analysis tools such as the sensitivity matrix, the model resolution matrix and the model covariance matrix also providing a partially nonlinear description of the equivalent model domain based on pseudo-hyperellipsoids. Linearised model analysis tools can offer quantitative measures. In particular, the model resolution and covariance matrices measure how far the preferred inversion model is from the true model and how uncertainty in the measurements maps into model uncertainty. We also cover nonlinear model analysis tools including changes to the preferred inversion model (nonlinear sensitivity tests), modifications of the data set (using bootstrap re-sampling and generalised cross-validation), modifications of data uncertainty, variations of model constraints (including changes to the trade-off parameter, reference model and matrix regularisation operator), the edgehog method, mostsquares inversion and global searching algorithms. These nonlinear model analysis tools try to explore larger parts of the model domain than linearised model analysis and, hence, may assemble a more comprehensive equivalent model domain. Then, to overcome the bottleneck of computational cost in model analysis, we present several practical algorithms to accelerate the computation. Here, we emphasise linearised model analysis, as efficient computation of nonlinear model uncertainty and resolution estimates is mainly determined by fast forward and inversion solvers. In the last part of our review, we present applications of model analysis to models computed from individual and joint inversions of electromagnetic data; we also describe optimal survey design and inversion grid design as important Extended author information available on the last page of the article applications of model analysis. The currently available model uncertainty and resolution analyses are mainly for 1D and 2D problems due to the limitation...

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Section: Methods Of Model Analysismentioning

confidence: 99%

Section: Effects Of Model Regularisation On Model Uncertainty and Resmentioning

confidence: 99%

Uncertainty and Resolution Analysis of 2D and 3D Inversion Models Computed from Geophysical Electromagnetic Data

Ren

Kalscheuer

2019

Surv Geophys

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“…Further improvements in models of mineral deposits can be obtained by including resistivity borehole logs as local prior information (e.g., Yan et al 2017a), by transferring structural boundaries observed in reflection seismic images or seismic velocity models to 2D or 3D inversion models of MT data (e.g., Le et al 2016b;Yan et al 2017b;Moorkamp 2017) or by performing joint inversions of MT, seismic and potential field data using petrophysical or structural coupling (e.g., Moorkamp et al 2011;Gallardo and Meju 2011;Haber and Gazit 2013;Takam Takougang et al 2015;Moorkamp 2017). Strictly speaking, well logs only represent the resistivity structure up to a few metres off the borehole and may be affected by borehole fluids and the invaded zone around the borehole.…”

Section: Resultsmentioning

confidence: 99%

Two-Dimensional Magnetotelluric Modelling of Ore Deposits: Improvements in Model Constraints by Inclusion of Borehole Measurements

Kalscheuer

Juhojuntti

Vaittinen³

2017

Surv Geophys

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A combination of magnetotelluric (MT) measurements on the surface and in boreholes (without metal casing) can be expected to enhance resolution and reduce the ambiguity in models of electrical resistivity derived from MT surface measurements alone. In order to quantify potential improvement in inversion models and to aid design of electromagnetic (EM) borehole sensors, we considered two synthetic 2D models containing ore bodies down to 3000 m depth (the first with two dipping conductors in resistive crystalline host rock and the second with three mineralisation zones in a sedimentary succession exhibiting only moderate resistivity contrasts). We computed 2D inversion models from the forward responses based on combinations of surface impedance measurements and borehole measurements such as (1) skin-effect transfer functions relating horizontal magnetic fields at depth to those on the surface, (2) vertical magnetic transfer functions relating vertical magnetic fields at depth to horizontal magnetic fields on the surface and (3) vertical electric transfer functions relating vertical electric fields at depth to horizontal magnetic fields on the surface. Whereas skin-effect transfer functions are sensitive to the resistivity of the background medium and 2D anomalies, the vertical magnetic and electric field transfer functions have the disadvantage that they are comparatively insensitive to the resistivity of the layered background medium. This insensitivity introduces convergence problems in the inversion of data from structures with strong 2D resistivity contrasts. Hence, we adjusted the inversion approach to a three-step procedure, where (1) (2) this inversion model from surface impedances is used as the initial model for a joint inversion of surface impedances and skin-effect transfer functions and (3) the joint inversion model derived from the surface impedances and skin-effect transfer functions is used as the initial model for the inversion of the surface impedances, skin-effect transfer functions and vertical magnetic and electric transfer functions. For both synthetic examples, the inversion models resulting from surface and borehole measurements have higher similarity to the true models than models computed exclusively from surface measurements. However, the most prominent improvements were obtained for the first example, in which a deep small-sized ore body is more easily distinguished from a shallow main ore body penetrated by a borehole and the extent of the shadow zone (a conductive artefact) underneath the main conductor is strongly reduced. Formal model error and resolution analysis demonstrated that predominantly the skin-effect transfer functions improve model resolution at depth below the sensors and at distance of $ 300-1000 m laterally off a borehole, whereas the vertical electric and magnetic transfer functions improve resolution along the borehole and in its immediate vicinity. Furthermore, we studied the signal levels at depth and provided specifications of borehole magnetic and elect...

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“…Many algorithms have been suggested to solve equation (5) (Van Beusekom et al, 2011;Candansayar, 2008;Mehanee and Zhdanov, 2002;Singh et al, 2017;Siripunvaraporn and Egbert, 2007;Yan P et al, 2017). In this paper, the smoothness-constrained leastsquares inversion method is adopted for solving the regularized inversion problems (Negi et al, 2013;Lee et al, 2009).…”

Section: Regularized Inversion Methodsmentioning

confidence: 99%

Two-dimensional regularized inversion of AMT data based on rotation invariant of Central impedance tensor

Xiao-zhong¹,

Liu²,

Li³

2018

Earth and Planetary Physics

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Considering the uncertainty of the electrical axis for two‐dimensional audo‐magnetotelluric (AMT) data processing, an AMT inversion method with the Central impedance tensor was presented. First, we present a calculation expression of the Central impedance tensor in AMT, which can be considered as the arithmetic mean of TE‐polarization mode and TM‐polarization mode in the two‐dimensional geo‐electrical model. Second, a least‐squares iterative inversion algorithm is established, based on a smoothness‐constrained model, and an improved L‐curve method is adopted to determine the best regularization parameters. We then test the above inversion method with synthetic data and field data. The test results show that this two‐dimensional AMT inversion scheme for the responses of Central impedance is effective and can reconstruct reasonable two‐dimensional subsurface resistivity structures. We conclude that the Central impedance tensor is a useful tool for two‐dimensional inversion of AMT data.

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Two‐dimensional magnetotelluric inversion using reflection seismic data as constraints and application in the COSC project

Cited by 29 publications

References 43 publications

Uncertainty and Resolution Analysis of 2D and 3D Inversion Models Computed from Geophysical Electromagnetic Data

Uncertainty and Resolution Analysis of 2D and 3D Inversion Models Computed from Geophysical Electromagnetic Data

Two-Dimensional Magnetotelluric Modelling of Ore Deposits: Improvements in Model Constraints by Inclusion of Borehole Measurements

Two-dimensional regularized inversion of AMT data based on rotation invariant of Central impedance tensor

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