A Novel method for processing noisy magnetotelluric data based on independence of signal sources and continuity of response functions

Ogawa, Hiroki; Asamori, Koichi; Negi, Tateyuki*; Ueda, Takumi*

A number of schemes for processing magnetotelluric (MT) data have been reported aiming at suppressing the strong effect of artificial electromagnetic noise, especially coherent noise that is correlated between electric and magnetic time series. Many of the recent denoising schemes are based on decomposing MT data into the responses of the natural signal and noise. Meanwhile, it is crucial to distinguish the natural signal from noise stably without depending on any empirical choice of parameter setting. In addition, improper subtraction of values from the separated signal can lead to the loss of useful values of the natural signal or missing noise-affected values, which may result in failure in deriving the true MT responses. We propose a novel data-processing method that applies frequency-domain independent component analysis (FDICA) to both the local MT data and the reference magnetic data. Among the separated signal, the proposed method can quantitatively distinguish the natural signal from the noise-affected components by calculating the ratio of cross-power spectrum with the reference data to the auto-power spectrum for each component. When determining which values to subtract from the separated signal, we introduce an evaluation index with respect to two characteristics of the MT response function: stationary in the time domain and smoothness in the frequency domain. We conduct the experiments both with MT time series severely contaminated by synthetic coherent noises and with MT field data interfered with DC (direct current) railways. Consequently, we confirm the superiority of the proposed method in the noise-suppression performance over the conventional methods of MT data processing.