Anna Platz, Ute Weckmann

• In Magnetotellurics (MT) natural electromagnetic field variations are recorded to study the electrical conductivity structure of the subsurface. Thereby long time-series of electromagnetic data are subdivided into smaller segments, which are Fourier transformed and typically averaged in a statistically robust manner to obtain MT transfer functions. Unfortunately, nowadays the presence of man-made electromagnetic noise sources often deteriorates a significant fraction of the recorded time-series by overprinting the desired natural field variations. Available approaches to obtain undisturbed and high quality MT results include, for example robust statistics, remote reference or multi-station analyses which aim at the removal of outliers or uncorrelated noise. However, we have observed that intermittent noise often affects a certain time span resulting in a second cluster of transfer functions in addition to the expected true MT distribution. In this paper, we present a novel criterion for the detection and pre-selection of EM noise inIn Magnetotellurics (MT) natural electromagnetic field variations are recorded to study the electrical conductivity structure of the subsurface. Thereby long time-series of electromagnetic data are subdivided into smaller segments, which are Fourier transformed and typically averaged in a statistically robust manner to obtain MT transfer functions. Unfortunately, nowadays the presence of man-made electromagnetic noise sources often deteriorates a significant fraction of the recorded time-series by overprinting the desired natural field variations. Available approaches to obtain undisturbed and high quality MT results include, for example robust statistics, remote reference or multi-station analyses which aim at the removal of outliers or uncorrelated noise. However, we have observed that intermittent noise often affects a certain time span resulting in a second cluster of transfer functions in addition to the expected true MT distribution. In this paper, we present a novel criterion for the detection and pre-selection of EM noise in form of outliers or additional clusters based on a distance measure of each data segment with regard to the centre of the data distribution. For this purpose, we utilize the Mahalanobis distance (MD) which computes the distance between two multivariate points considering the covariance matrix of the data that quantifies the shape and the size of multivariate data distributions. As the MD considers the covariance matrix, it corrects not only for different variances but also for any correlation between the data. The computation of both, the mean value and covariance matrix, is susceptible to ouliers (e.g. noise) and requires a statistically robust estimation. We tested several robust estimators, for example median absolute deviation or minimum covariance determinant algorithm and finally implemented an automatic criterion using a deterministic minimum covariance determinant algorithm. We will present results using MT data from various field experiments all over the world, which illustrate successfull data improvement. This approach is able to remove scattered data points as well as to reject complete data cluster originating from noise sources. However, like all purely statistical algorithms the criterion is limited to cases where the majority of the recorded data is well-behaved, that is noise content is below 50 per cent. If the majority of data points originates from noise sources, the new criterion will fail if used in an automatic way. In these cases, additional input by the user either manually or in an automated fashion can be utilized. We therefore suggest to use an add-on criterion to back the MD selection and subsequent robust stacking in form of a physically motivated constraint based on the magnetic incidence direction. This property indicates whether the magnetic field originates from various sources in the far field or from a strong and well defined source in the near field.…