Effective noise separation for magnetotelluric single site data processing using a frequency domain selection scheme

Magnetotelluric (MT) response function estimates can be severely disturbed by the effects of cultural noise. Methods to isolate and remove these disturbances are typically based on time-series editing, robust statistics, remote reference processing, or some combination of the above. Robust remote reference processing can improve the data quality at a local site, but only if synchronous recordings of at least one additional site are available and if electromagnetic noise between these sites is uncorrelated. If these prerequisites are not met, we suggest an alternative approach for noise removal, based on a combination of frequency domain editing with subsequent single site robust processing. The data pre-selection relies on a thorough visual inspection of a variety of statistical parameters such as spectral power densities, coherences, the distribution of response functions and their errors, etc. Extreme outliers and particularly noisy data segments are excluded from further data processing by setting threshold values for individual parameters. Examples from Namibia and Jordan illustrate that this scheme can improve data quality significantly. However, the examples also suggest that it is not possible to establish generally valid rules for selection as they depend strongly oil the local noise conditions. High coherence, for example, can indicate a good signal-to-noise ratio or strongly correlated noise. However, we found that strong polarization of the magnetic field channels and the distribution of response function errors are two important parameters for noise detection.