Scalar and vector Slepian functions, spherical signal estimation and spectral analysis

TL;DR

This paper reviews the theory and practical algorithms for Slepian functions, which are localized in both space and frequency, aiding the analysis of spatially and spectrally limited scientific data, especially on the sphere.

Contribution

It provides a comprehensive overview of Slepian functions for scalar and vector signals on the sphere, including new algorithms and methods for spectral analysis in geosciences.

Findings

Developed algorithms for spherical Slepian functions

Enhanced spectral analysis of spatially limited data

Applied methods to geoscientific signal modeling

Abstract

It is a well-known fact that mathematical functions that are timelimited (or spacelimited) cannot be simultaneously bandlimited (in frequency). Yet the finite precision of measurement and computation unavoidably bandlimits our observation and modeling scientific data, and we often only have access to, or are only interested in, a study area that is temporally or spatially bounded. In the geosciences we may be interested in spectrally modeling a time series defined only on a certain interval, or we may want to characterize a specific geographical area observed using an effectively bandlimited measurement device. It is clear that analyzing and representing scientific data of this kind will be facilitated if a basis of functions can be found that are "spatiospectrally" concentrated, i.e. "localized" in both domains at the same time. Here, we give a theoretical overview of one particular approach to this "concentration" problem, as originally proposed for time series by Slepian and coworkers, in the 1960s. We show how this framework leads to practical algorithms and statistically performant methods for the analysis of signals and their power spectra in one and two dimensions, and, particularly for applications in the geosciences, for scalar and vectorial signals defined on the surface of a unit sphere.