The use of data-adaptive filtering for noise removal on magnetotelluric data

Physics

Scientific paper

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Scientific paper

A new method is described which can be used to reduce the bias error as well as the random error of the magnetotelluric impedance tensor. This reduction in noise can be done in a single stack, thus making averaging unnecessary or keeping it to a minimum. This can speed up a magnetotelluric sounding considerably. The method is based on a data-adaptive filtering technique. A unique feature of the method is that no a priori information of the signal or noise characteristic is necessary for its operation. Another advantage is that the method adapts itself to changing signal and/or noise characteristics. It is shown how random noise, spikes and steps can be removed by this method. A description of the noise problems in MT is given. The data-adaptive method is described and its properties are illustrated by applying it to severely distorted synthetic signals. Its effectiveness in reducing MT tensor bias and random noise is shown through the use of the unit impedance tensor.

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