MAGNETOTELLURIC DATA PROCESSING WITH A ROBUST STATISTICAL PROCEDURE HAVING A HIGH BREAKDOWN POINT

Abstract

A new robust magnetotelluric (MT) data processing algorithm is described, involving Siegel estimation on the basis of a repeated median (RM) algorithm for maximum protection against the influence of outliers and large errors. The spectral transformation is performed by means of a fast Fourier transformation followed by segment coherence sorting. To remove outliers and gaps in the time domain, an algorithm of forward autoregression prediction is applied. The processing technique is tested using two 7 day long synthetic MT time-series prepared within the framework of the COMDAT processing software comparison project. The first test contains pure MT signals, whereas in the second test the same signal is superimposed on different types of noise. To show the efficiency of the algorithm some examples of real MT data processing are also presented.