Inverting magnetotelluric data with distortion correction—stability, uniqueness and trade-off with model structure

Abstract

SUMMARY Galvanic distortion of magnetotelluric (MT) data is a common effect that can impede the reliable imaging of subsurface structures. Recently, we presented an inversion approach that includes a mathematical description of the effect of galvanic distortion as inversion parameters and demonstrated its efficiency with real data. We now systematically investigate the stability of this inversion approach with respect to different inversion strategies, starting models and model parametrizations. We utilize a data set of 310 MT sites that were acquired for geothermal exploration. In addition to impedance tensor estimates over a broad frequency range, the data set also comprises transient electromagnetic measurements to determine near surface conductivity and estimates of distortion at each site. We therefore can compare our inversion approach to these distortion estimates and the resulting inversion models. Our experiments show that inversion with distortion correction produces stable results for various inversion strategies and for different starting models. Compared to inversions without distortion correction, we can reproduce the observed data better and reduce subsurface artefacts. In contrast, shifting the impedance curves at high frequencies to match the transient electromagnetic measurements reduces the misfit of the starting model, but does not have a strong impact on the final results. Thus our results suggest that including a description of distortion in the inversion is more efficient and should become a standard approach for MT inversion.

Probing the Southern African Lithosphere With Magnetotellurics—Part I: Model Construction

“…However, we used different values for the regularization parameter, staring with λ = 1,000 and reducing it to λ = 1 in the final iterations, since the influence of the regularization on the inversion is different between the two algorithms. The initial iterations did not include any distortion correction, but this was enabled after the first regularization change as this has been shown to yield stable results (Moorkamp et al., 2020).…”

“…The numerical basis of the forward modeling engine and the gradient calculation is presented in Avdeev and Avdeeva (2009) and Avdeeva et al (2015). It utilizes an integral-equation-based forward engine x3d (Avdeev et al, 1997) and includes a correction for galvanic distortion at each site (Avdeeva et al, 2015;Moorkamp et al, 2020). Galvanic distortion of MT impedances is typically caused by charge accumulation at small structures (compared to the induction length scale), and it can mathematically be described as a site-specific, frequency-independent multiplication of the impedances with a real-valued matrix C (Chave & Jones, 2012).…”

“…At both stations, the diagonal elements are significantly smaller than the off‐diagonal elements and are matched better by the jif3D inversion than the ModEM inversion. It is our experience that distortion correction helps to match diagonal elements better even when these are small (Moorkamp et al., 2020). The data at site KAP045 are matched differently by the two inversions, and again the difference is more pronounced in the phase than in apparent resistivity.…”

Comparison of Different Coupling Methods for Joint Inversion of Geophysical Data: A Case Study for the Namibian Continental Margin

“…The data misfit, regularization-, and cross-gradient terms in the objective function are weighted with Lagrange multipliers (w MT , w Grav , λ MT , λ Grav , κ). We employ a cooling strategy for the two regularization weights λ MT and λ Grav , which recovers large-scale structures first and then allows for smaller-scale model variations by subsequently reducing the weights (Moorkamp et al, 2020). A setup with initial small regularization weights results in rough models with large parameter jumps and large regularization-gradient and cross-gradient terms.…”