In the last decades, the magnetotelluric (MT) method has been proved to be a useful geophysical tool in different contexts, from geothermal reservoir characterization to crustal structures studies. Nevertheless, the MT method is very sensitive to the presence of noise. Indeed, as the method is based on the measurement of both the electric and magnetic components of the natural electromagnetic (EM) field, it fails when these components are affected by noises of different nature. In particular, in industrialized and urbanized context, the MT time series could be strongly affected by man-made noise and, as a consequence, the impedance tensor estimates, given by the ratio between the electric and magnetic components of the MT field, could be unreliable. To improve the reliability of these estimates, most of the proposed approaches rely on the robust evaluation of the impedance tensor as well as on the use of remote reference MT stations or on the combination of both approaches. However, these methods are not always effective. The robust methods fail when most of the data are affected by noise, giving as result a biased impedance tensor, while the remote reference approach is ineffective when the noise is correlated between reference and local MT station. In recent years, alternative procedures have been proposed to obtain reliable estimates of the MT impedance tensor. In the present work, a different approach based on the use of Discrete Wavelet Transform (DWT) and Self-Organizing Map (SOM) neural network analysis is proposed for improving the magnetotelluric impedance tensor estimates. The approach has been tested by changing type, level and window length of the noise affecting MT time series. Furthermore, in order to identify the most reliable apparent resistivity and phase values among the different impedance tensors clusters provided by the SOM analysis for each analyzed period, a selection criterion is provided and tested on synthetic and field MT data.

Improving magnetotelluric impedance tensor estimates by self-organizing maps

CARBONARI R.;DI MAIO R.;
2018

Abstract

In the last decades, the magnetotelluric (MT) method has been proved to be a useful geophysical tool in different contexts, from geothermal reservoir characterization to crustal structures studies. Nevertheless, the MT method is very sensitive to the presence of noise. Indeed, as the method is based on the measurement of both the electric and magnetic components of the natural electromagnetic (EM) field, it fails when these components are affected by noises of different nature. In particular, in industrialized and urbanized context, the MT time series could be strongly affected by man-made noise and, as a consequence, the impedance tensor estimates, given by the ratio between the electric and magnetic components of the MT field, could be unreliable. To improve the reliability of these estimates, most of the proposed approaches rely on the robust evaluation of the impedance tensor as well as on the use of remote reference MT stations or on the combination of both approaches. However, these methods are not always effective. The robust methods fail when most of the data are affected by noise, giving as result a biased impedance tensor, while the remote reference approach is ineffective when the noise is correlated between reference and local MT station. In recent years, alternative procedures have been proposed to obtain reliable estimates of the MT impedance tensor. In the present work, a different approach based on the use of Discrete Wavelet Transform (DWT) and Self-Organizing Map (SOM) neural network analysis is proposed for improving the magnetotelluric impedance tensor estimates. The approach has been tested by changing type, level and window length of the noise affecting MT time series. Furthermore, in order to identify the most reliable apparent resistivity and phase values among the different impedance tensors clusters provided by the SOM analysis for each analyzed period, a selection criterion is provided and tested on synthetic and field MT data.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11588/739921
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