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Authors: D'Auria, L.* 
Esposito, A. M.* 
Petrillo, Z.* 
Siniscalchi, A.* 
Title: Denoising Magnetotelluric Recordings Using Self-OrganizingMaps.
Issue Date: 2015
Publisher: Springer International Publishing
ISBN: 978-3-319-18163-9
Keywords: magnetotellurics
self-organizing maps
Subject Classification04. Solid Earth::04.02. Exploration geophysics::04.02.04. Magnetic and electrical methods 
04. Solid Earth::04.05. Geomagnetism::04.05.08. Instruments and techniques 
05. General::05.01. Computational geophysics::05.01.02. Cellular automata, fuzzy logic, genetic alghoritms, neural networks 
Abstract: We present a novel approach for the filtering of magnetotelluric data in urban areas. The magnetotelluric (MT) method is a valid technique for geophysical exploration of the Earth’s interiors. It provides information about the rocks’ resistivity and in particular, in volcanology, it allows to delineate the complex structure of volcanoes possibly detecting magmatic chambers and hydrothermal systems. Indeed, geological fluids (e.g. magma) are characterized by resistivity of many orders of magnitude lower than the surrounding rocks. However, the MT method requires the presence of natural electromagnetic fields. So in urban areas, the noise strongly influences the MT recordings, especially that produced by trains. Various denoising techniques have been proposed, but it is not always easy to identify the noise-free intervals. Thus, in this work we propose a neural method, the Self-Organizing Map (SOM), to perform the clustering of impedance tensors, computed on a Discrete Wavelet (DW) expansion of MT recordings. The use of the DW transform is motivated by the need of analyzing MT recordings both in time and frequency domain. The results of the SOM based clustering analysis applied to synthetic data have shown the capability of greatly reducing the effect of the noise on the retrieved apparent resistivity curves.
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