Denoising magnetotelluric recordings using self-organizing maps

A novel approach for processing magnetotelluric data in urban areas is presented. 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 a neural method, the Self-Organizing Map (SOM), is proposed 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 SOM is principally tested on synthetic dataset. Then, as a further validation of the method, it is applied on real data recorded at volcano Etna, Sicily. In both cases, the obtained results have shown the SOM capability of greatly reducing the effect of the noise on the retrieved apparent resistivity curves.