5:15 PM - 6:45 PM
[MTT37-P07] Noise characteristics in GNSS time series based on robust spectral analysis: (1) Description of robust spectral estimation
Keywords:GNSS, GEONET, spectral analysis, observation noise
GNSS is an observation system to measure the locations of observation stations by receiving radio waves transmitted from the satellites. Due to this observation principle, raw GNSS data contain various information from satellites to receivers such as the ionosphere, atmosphere, surrounding environment close to the antennas, as well as the motion of the Earth. Thus, pre-processing of raw GNSS data is required to estimate the target products by modeling the other factors using analysis software.
The Geospatial Information Authority of Japan provides the daily coordinates at the GEONET stations as the F5 solution (Takamatsu et al. 2023). Because of its convenient use, the F5 solution is frequently used for a variety of geodetic, seismic, and volcanic analyses related to crustal deformation. To precisely extract the transient deformation due to tectonic or volcanic processes, it is important to understand the spatio-temporal noise characteristics and to accurately model them. Nonetheless, the comprehensive analysis of spatio-temporal noise characteristics in the F5 solution has been rarely published.
Takabatake and Yano (2023) recently proposed a method to robustly estimate the frequency-domain characteristics of observed time series. In this presentation, we introduce this robust spectral estimation; for the application of this method to the GEONET F5 solution, (see the accompanying presentation, Kano et al. in this session). The method utilizes the spectral Renyi divergences, which includes the Itakura-Saito divergence as a limit. The Itakura-Saito divergence (Itakura and Saito, 1968; Whittle, 1953) is widely used in the spectral analysis and provides the foundations of the maximum likelihood method or Burg’s maximum entropy method. However, the method based on the Itakura-Saito divergence is unstable in the presence of outliers in the frequency domain such as annual or semi-annual components. The spectral estimation using the spectral Renyi divergence is shown to become more stable against the presence of outliers in the frequency domain than that using the Itakura-Saito divergence, which reduces the effect due to pre-processing of observations.
The Geospatial Information Authority of Japan provides the daily coordinates at the GEONET stations as the F5 solution (Takamatsu et al. 2023). Because of its convenient use, the F5 solution is frequently used for a variety of geodetic, seismic, and volcanic analyses related to crustal deformation. To precisely extract the transient deformation due to tectonic or volcanic processes, it is important to understand the spatio-temporal noise characteristics and to accurately model them. Nonetheless, the comprehensive analysis of spatio-temporal noise characteristics in the F5 solution has been rarely published.
Takabatake and Yano (2023) recently proposed a method to robustly estimate the frequency-domain characteristics of observed time series. In this presentation, we introduce this robust spectral estimation; for the application of this method to the GEONET F5 solution, (see the accompanying presentation, Kano et al. in this session). The method utilizes the spectral Renyi divergences, which includes the Itakura-Saito divergence as a limit. The Itakura-Saito divergence (Itakura and Saito, 1968; Whittle, 1953) is widely used in the spectral analysis and provides the foundations of the maximum likelihood method or Burg’s maximum entropy method. However, the method based on the Itakura-Saito divergence is unstable in the presence of outliers in the frequency domain such as annual or semi-annual components. The spectral estimation using the spectral Renyi divergence is shown to become more stable against the presence of outliers in the frequency domain than that using the Itakura-Saito divergence, which reduces the effect due to pre-processing of observations.