Noise reduction in magnetotelluric time-series with a new signal-noise separation method and its application to a field experiment in the Saxonian Granulite Massif

Details Noise reduction in magnetotelluric time-series with a new signal-noise separation method and its application to a field experiment in the Saxonian Granulite Massif Noise reduction in magnetotelluric time-series with a new signal-noise separation method and its application to a field experiment in the Saxonian Granulite Massif

Sep 2001

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001geoji.146..659o&link_type=abstract

Geophysical Journal International, Volume 146, Issue 3, pp. 659-669.

Astronomy and Astrophysics

Astronomy

5

Data Processing, Electromagnetic Methods, Electromagnetic Noise, Magnetotellurics

Scientific paper

In the presence of large and continuous correlated noise signals in measured electric and magnetic time-series, even robust remote-reference methods give erroneous estimates of MT transfer functions. If clean remote time-series are available, it is possible to separate MT and correlated noise signals and to derive unbiased MT transfer functions with the signal-noise separation method (SNS) (Larsen et al. 1996). In practice, the remote time series also contain some noise and the results can be improved by using a second remote data set and the SNS-remote-reference technique. We tested this method with data from the Saxonian Granulite Massif (SGM), Germany, where strong correlated noise signals were detected. We used remote data which were recorded 350km away and, for short periods, data from sites of the profile across the SGM itself (distance 5km). To show the efficiency of the signal-noise separation we first determined a `true' MT transfer function from time-series with low noise level. In a second step we reproduced the results from processing very noisy data sections. We were able to determine useful MT transfer functions even when the MT variations have less than 10 per cent share of the measured variations. We identified dominant noise in the measured time-series from pipelines and trains.

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