Potential-field estimation from satellite data using scalar and vector Slepian functions

TL;DR

This paper introduces two novel methods using scalar and vector spherical Slepian functions to estimate Earth's potential fields from satellite gradient data, improving local data analysis and model recovery.

Contribution

It presents new approaches employing scalar and vector spherical Slepian functions for potential field estimation from satellite data, tailored for local and regional analysis.

Findings

Effective estimation of potential fields from local satellite data.

Introduction of a new vectorial spherical Slepian functions.

Enhanced accuracy in potential field modeling for specific regions.

Abstract

In the last few decades a series of increasingly sophisticated satellite missions has brought us gravity and magnetometry data of ever improving quality. To make optimal use of this rich source of information on the structure of Earth and other celestial bodies, our computational algorithms should be well matched to the specific properties of the data. In particular, inversion methods require specialized adaptation if the data are only locally available, their quality varies spatially, or if we are interested in model recovery only for a specific spatial region. Here, we present two approaches to estimate potential fields on a spherical Earth, from gradient data collected at satellite altitude. Our context is that of the estimation of the gravitational or magnetic potential from vector-valued measurements. Both of our approaches utilize spherical Slepian functions to produce an approximation of local data at satellite altitude, which is subsequently transformed to the Earth's spherical reference surface. The first approach is designed for radial-component data only, and uses scalar Slepian functions. The second approach uses all three components of the gradient data and incorporates a new type of vectorial spherical Slepian functions which we introduce in this chapter.