Numerical experiment for processing noisy magnetotelluric data based on independence of signal sources and continuity of response functions

Abstract

In the magnetotelluric (MT) method, observed data consist of the sum of several types of signals. The technique of blind source separation (BSS) has been utilized to suppress the effect of artificial noises on MT response functions. When reconstructing the noise-reduced observed data by means of BSS, it is crucial to eliminate the specific components which correspond to noise. Yet improper subtraction of values from separated signals can lead to the loss of useful values of the natural signal or missing a large amount of noise-affected values, which may result in failure in deriving the true MT responses. This study proposes a novel algorithm that utilizes frequency-domain independent component analysis (FDICA). The observed data were classified into the XY and YX modes and the components of the reference magnetic field were appended to respective modes so that both the assumed source signal and the mixed signal have same number of components. The MT response functions, which are represented as the ratio of horizontal electric to magnetic field components, are substantially constant over the relatively short period of time. Moreover, the MT response functions vary smoothly in the frequency domain. An evaluation index with respect to these two characteristics of the natural signal was introduced when determining which values to subtract prior to the reconstruction of the observed data. We verify the performance of noise suppression by applying the proposed method to a set of MT time series affected by synthetic strong noise over the whole observation time.