Three-dimensional inversion of magnetotelluric impedance tensor data and full distortion matrix

Abstract

S U M M A R YGalvanic distortion of magnetotelluric (MT) data due to small-scale surficial bodies or due to topography is one of the major factors that prevents accurate imaging of the subsurface. We present a 3-D algorithm for joint inversion of MT impedance tensor data and a frequencyindependent full distortion matrix that circumvents this problem. We perform several tests of our algorithm on synthetic data affected by different amounts of distortion. These tests show that joint inversion leads to a better conductivity model compared to the inversion of the MT impedance tensor without any correction for distortion effects. For highly distorted data, inversion without any distortion correction results in strong artefacts and we cannot fit the data to the specified noise level. When the distortion is reduced, we can fit the data to an RMS of one, but still observe artefacts in the shallow part of the model. In contrast, in both cases our joint inversion can fit the data within the assumed noise level and the resulting models are comparable to the inversion of undistorted data. In addition, we show that the elements of the full distortion matrix can be well resolved by our algorithm. Finally, when inverting undistorted data, including the distortion matrix in the inversion only results in a minor loss of resolution. We therefore consider our new approach a promising tool for the general analysis of field MT data.

Use of ssq rotational invariant of magnetotelluric impedances for estimating informative properties for galvanic distortion

“…The det impedance has also been used in recent works. For example, Seama et al (2007) inverted the det impedances from marine MT data at each observation point to obtain 1D conductivity profiles beneath the Philippine Sea, Arango et al (2009) used the det impedance to interpret 3D MT data, and Baba et al (2010) and Avdeeva et al (2015) used the average det impedance in the same way as Berdichevsky et al (1980). However, when the det impedance was re-examined on the basis of the present knowledge of galvanic distortion by applying the Groom-Bailey model (Groom and Bailey 1989), it was found that the magnitude of the det impedance is always biased downward by the geometric distortion expressed by the shear and splitting parameters (see Gómez-Treviño et al 2013;Rung-Arunwan et al 2016).…”

“…In addition to decomposition approaches, inversion schemes that simultaneously solve the static shift (e.g., Sasaki and Meju 2006) have become feasible, but the geometric distortion is not controlled. Avdeeva et al (2015) proposed 3D inversion with the solution of the full distortion matrix, but this approach does not allow this static shift to be a free parameter.…”

“…In addition, the employment of these two types of properties allows the effect of the site gain to be separated from the effects of the twist, shear, and splitting parameters. Most importantly, we can use these parameters to determine the necessary treatment of galvanic distortion for a given dataset, such as whether or not a removal scheme should be applied in the inversion (e.g., Sasaki and Meju 2006;Avdeeva et al 2015).…”

“…The effect of these near-surface distorters is therefore a random phenomenon, and it can be expressed as a mathematical model such as the GroomBailey model of galvanic distortion, which is adopted in this work. The site gain and other distortion parameters (twist, shear, and splitting) are treated as random variables (e.g., Avdeeva et al 2015). The synthetic MT array contains 25 MT stations.…”

“…If a limited number of sites showing heavy distortion are removed, the RDI after removal is supposed to be small. Conversely, if the RDI still exhibits a high value, a proper treatment for the galvanic distortion, such as inversion including the galvanic distortion (e.g., DeGroot-Hedlin 1995; Ogawa and Uchida 1996;Sasaki and Meju 2006;Avdeeva et al 2015) or an MT data analysis (e.g., Weaver et al 2000;Caldwell et al 2004), will be essential. The combination of LDIs and RDI helps to provide insight, at least to some extent, as to which approach should be applied to a set of MT impedances obtained from observation.…”