Nonstationary magnetotelluric data processing with instantaneous parameter

Nonstationarity in electromagnetic data affects the computation of Fourier spectra and therefore the traditional estimation of the magnetotelluric (MT) transfer functions (TF). We provide a TF estimation scheme based on an emerging nonlinear, nonstationary time series analysis tool, called empirical mode decomposition (EMD) and show that this technique can handle nonstationary effects with which traditional methods encounter difficulties. In contrast to previous works that employ EMD for MT data processing, we argue the advantages of a multivariate decomposition, highlight the possibility to use instantaneous parameters, and define the homogenization of frequency discrepancies between data channels. Our scheme uses the robust statistical estimation of transfer functions based on robust principal component analysis and a robust iteratively reweighted least squares regression with a Huber weight function. The scheme can be applied with and without aid of any number of available remote reference stations. Uncertainties are estimated by iterating the complete robust regression, including the robust weight computation, with a bootstrap routine. We apply our scheme to synthetic and real data (Southern Africa) with and without nonstationary character and compare different processing techniques to the one presented here. As a conclusion, nonstationary noise can heavily affect Fourier‐based MT data processing but the presented nonstationary approach is nonetheless able to extract the impedances.