A generalization of the 2D Slepian functions

Author(s):  
Fethi Bouzeffour ◽  
Mubariz Garayev
Keyword(s):  
2021 ◽  
Author(s):  
Hannah Rogers ◽  
Ciaran Beggan ◽  
Kathryn Whaler

<p>Spherical Slepian functions (or ‘Slepian functions’) are mathematical functions which can be used to decompose potential fields, as represented by spherical harmonics, into smaller regions covering part of a spherical surface. This allows a spatio-spectral trade-off between aliasing of the signal at the boundary edges while constraining it within a region of interest. While Slepian functions have previously been applied to geodetic and crustal magnetic data, this work further applies Slepian functions to flows on the core-mantle boundary. There are two main reasons for restricting flow models to certain parts of the core surface. Firstly, we have reason to believe that different dynamics operate in different parts of the core (such as under LLSVPs) while, secondly, the modelled flow is ambiguous over certain parts of the surface (when applying flow assumptions). Spherical Slepian functions retain many of the advantages of our usual flow description, concerning for example the boundary conditions it must satisfy, and allowing easy calculation of the power spectrum, although greater initial computational effort is required.</p><p><br>In this work, we apply Slepian functions to core flow models by directly inverting from satellite virtual observatory magnetic data into regions of interest. We successfully demonstrate the technique and current short comings by showing whole core surface flow models, flow within a chosen region, and its corresponding complement. Unwanted spatial leakage is generated at the region edges in the separated flows but to less of an extent than when using spherical Slepian functions on existing flow models. The limited spectral content we can infer for core flows is responsible for most, if not all, of this leakage. Therefore, we present ongoing investigations into the cause of this leakage, and to highlight considerations when applying Slepian functions to core surface flow modelling.</p>


2009 ◽  
Vol 44 (4) ◽  
pp. 131-148 ◽  
Author(s):  
M. Eshagh

Spatially Restricted Integrals in Gradiometric Boundary Value ProblemsThe spherical Slepian functions can be used to localize the solutions of the gradiometric boundary value problems on a sphere. These functions involve spatially restricted integral products of scalar, vector and tensor spherical harmonics. This paper formulates these integrals in terms of combinations of the Gaunt coefficients and integrals of associated Legendre functions. The presented formulas for these integrals are useful in recovering the Earth's gravity field locally from the satellite gravity gradiometry data.


Author(s):  
Willi Freeden ◽  
M. Zuhair Nashed ◽  
Michael Schreiner
Keyword(s):  

2015 ◽  
Vol 56 (5) ◽  
pp. 907-915 ◽  
Author(s):  
Mohammad Ali Sharifi ◽  
Saeed Farzaneh

2020 ◽  
Vol 125 (11) ◽  
Author(s):  
Marzia Parisi ◽  
Eli Galanti ◽  
William M. Folkner ◽  
Yohai Kaspi ◽  
Dustin R. Buccino

2020 ◽  
Author(s):  
Naomi Schneider ◽  
Volker Michel

<p>A fundamental problem in the geosciences is the downward continuation of the gravitational potential. It enables us to learn more about the system Earth and, in particular, the climate change.</p><p>Mathematically, we can model a (downward continued) signal in a 'best basis' consisting of local and global trial functions from a dictionary. In practice, our dictionaries include spherical harmonics, Slepian functions and radial basis functions. The expansion in dictionary elements is obtained by one of the Inverse Problem Matching Pursuit (IPMP) algorithms.</p><p>However, it remains to discuss the choice of the dictionary. For this, we further developed the IPMP algorithms by introducing a learning technique. With this approach, they automatically select a finite number of optimized dictionary elements from infinitely many possible ones. We present the details of our method and give numerical examples.</p><p>See also: V. Michel and N. Schneider, <em>A first approach to learning a best basis for gravitational field modelling,</em> arxiv: 1901.04222v2</p>


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