der Christian-Albrechts-Universität zu Kiel

ii One of the most important steps in magnetotellurics (MT) studies is the derivation of frequency-based impedances from the measured time-varying electromagnetic fields. Ac-curate estimations of impedances, which depends on the quality of the collected data sets, can greatly improve the reliability of the subsurface resistivity structure model. Most cur-rent MT data processing methods use Fourier transform based procedures to estimate power spectral densities and, therefore, assume that the signals are stationary over the analyzed time segments. However, stationarity in geomagnetic data is not always ensured given the variety of source mechanisms causing the geomagnetic variations at different time and spatial scales. Additional complication and bias may arise from the presence of noise in the recorded electric and magnetic fields, which is particularly evident in marine MT measurements. I explore a new heuristic method for dealing with the non-stationarity of MT time series based on Empirical Mode Decomposition (EMD). It is a dynamic time series analysis method, in which time series containing stationary and / or non-stationary components can be decom