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Least squares spectral analysis and its application to superconducting gravimeter data analysis

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Geo-spatial Information Science

Abstract

Detection of a periodic signal hidden in noise is the goal of Superconducting Gravimeter (SG) data analysis. Due to spikes, gaps, datum shrifts (offsets) and other disturbances, the traditional FFT method shows inherent limitations. Instead, the least squares spectral analysis (LSSA) has showed itself more suitable than Fourier analysis of gappy, unequally spaced and unequally weighted data series in a variety of applications in geodesy and geophysics. This paper reviews the principle of LSSA and gives a possible strategy for the analysis of time series obtained from the Canadian Superconducting Gravimeter Installation (CGSD), with gaps, offsets, unequal sampling decimation of the data and unequally weighted data points.

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Authors and Affiliations

  1. School of Geodesy and Geomatics, Wuhan University, 129 Luoya Road, 430079, Wuhan, China

    Yin Hui Ph.D (professor)

Authors
  1. Yin Hui Ph.D
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  2. Spiros D. Pagiatakis
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Additional information

Funded by the Canadian National Center of Excellence GEOIDE.

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Hui, Y., Pagiatakis, S.D. Least squares spectral analysis and its application to superconducting gravimeter data analysis. Geo-spat. Inf. Sci. 7, 279–283 (2004). https://doi.org/10.1007/BF02828552

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  • DOI: https://doi.org/10.1007/BF02828552

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