Improved Responses with Multitaper Spectral Analysis for Magnetotelluric Time Series Data Processing

Abstract

In order to attain good quality transfer function estimates from magnetotelluric field data (i.e., smooth behavior and small uncertainties across all frequencies), we compare time series data processing with and without a multitaper approach for spectral estimation. There are several common ways to increase the reliability of the Fourier spectral estimation from experimental (noisy) data; for example to subdivide the experimental time series into segments, taper these segments (using single taper), perform the Fourier transform of the individual segments, and average the resulting spectra. To further reduce the bias of spectral estimation, a multitaper approach can be adopted. In this approach, a number of orthogonal taper functions are used to generate independent estimates, from the same time segment, which are subsequently averaged. We apply multitaper spectral analysis to magnetotelluric time series data and we show examples of responses from field data. The results clearly show that this approach improves the transfer function responses, often significantly, particularly at the long periods.