A General Approach to Regularizing Inverse Problems with Regional Data using Slepian Wavelets

TL;DR

This paper introduces a method using Slepian wavelets to regularize inverse problems with regional data, enabling the application of standard techniques to localized datasets by constructing regional Slepian bases.

Contribution

It develops a general framework for creating Slepian functions tailored to regional inverse problems and integrates them with wavelet-based multiscale regularization techniques.

Findings

Slepian functions can be constructed for regional inverse problems.

The approach allows standard regularization methods to be used with regional data.

Numerical examples demonstrate the effectiveness of the method.

Abstract

Slepian functions are orthogonal function systems that live on subdomains (for example, geographical regions on the Earth's surface, or bandlimited portions of the entire spectrum). They have been firmly established as a useful tool for the synthesis and analysis of localized (concentrated or confined) signals, and for the modeling and inversion of noise-contaminated data that are only regionally available or only of regional interest. In this paper, we consider a general abstract setup for inverse problems represented by a linear and compact operator between Hilbert spaces with a known singular-value decomposition (svd). In practice, such an svd is often only given for the case of a global expansion of the data (e.g. on the whole sphere) but not for regional data distributions. We show that, in either case, Slepian functions (associated to an arbitrarily prescribed region and the given compact operator) can be determined and applied to construct a regularization for the ill-posed regional inverse problem. Moreover, we describe an algorithm for constructing the Slepian basis via an algebraic eigenvalue problem. The obtained Slepian functions can be used to derive an svd for the combination of the regionalizing projection and the compact operator. As a result, standard regularization techniques relying on a known svd become applicable also to those inverse problems where the data are regionally given only. In particular, wavelet-based multiscale techniques can be used. An example for the latter case is elaborated theoretically and tested on two synthetic numerical examples.