SLEPIAN Charlie

Spectral estimation problems using spherical harmonics and spherical Slepian functions

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Initial contribute: 2021-09-10

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Princeton University
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fjsimons@gmail.com
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Application-focused categoriesNatural-perspectiveLand regions
Application-focused categoriesIntegrated-perspectiveGlobal scale
Application-focused categoriesIntegrated-perspectiveRegional scale

Detailed Description

English {{currentDetailLanguage}} English

Quote from: https://academic.oup.com/gji/article/174/3/774/2003109

  We address the problem of estimating the spherical-harmonic power spectrum of a statistically isotropic scalar signal from noise-contaminated data on a region of the unit sphere. Three different methods of spectral estimation are considered: (i) the spherical analogue of the one-dimensional (1-D) periodogram, (ii) the maximum-likelihood method and (iii) a spherical analogue of the 1-D multitaper method. The periodogram exhibits strong spectral leakage, especially for small regions of area A ≪ 4π, and is generally unsuitable for spherical spectral analysis applications, just as it is in 1-D. The maximum-likelihood method is particularly useful in the case of nearly-whole-sphere coverage, A ≈ 4π, and has been widely used in cosmology to estimate the spectrum of the cosmic microwave background radiation from spacecraft observations. The spherical multitaper method affords easy control over the fundamental trade-off between spectral resolution and variance, and is easily implemented regardless of the region size, requiring neither non-linear iteration nor large-scale matrix inversion. As a result, the method is ideally suited for most applications in geophysics, geodesy or planetary science, where the objective is to obtain a spatially localized estimate of the spectrum of a signal from noisy data within a pre-selected and typically small region.

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Frederik Simons, Harig (2021). SLEPIAN Charlie, Model Item, OpenGMS, https://geomodeling.njnu.edu.cn/modelItem/b8b27f63-b026-408a-a915-b1732b611825
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Contributor(s)

Initial contribute : 2021-09-10

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Authorship

:  
Princeton University
:  
fjsimons@gmail.com
:  
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Last modifier
HaoCheng Wang
Last modify time
2021-09-18
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