Precision of continuous GPS velocities from statistical analysis of synthetic time series

Masson, Christine; Mazzotti, Stephane; Vernant, Philippe

We use statistical analyses of synthetic position time series to estimate the potential precision of GPS (Global Positioning System) velocities. The synthetic series represent the standard range of noise, seasonal, and position offset characteristics, leaving aside extreme values. This analysis is combined with a new simple method for automatic offset detection that allows an automatic treatment of the massive dataset. Colored noise and the presence of offsets are the primary contributor to velocity variability. However, regression tree analyses show that the main factors controlling the velocity precision are first the duration of the series, second the presence of offsets, and third the noise level (dispersion and spectral index). Our analysis allows us to propose guidelines, which can be applied to actual GPS data, that constrain velocity precisions, characterized as a 95 % confidence limit of the velocity biases, based on simple parameters: (1) series durations over 8.0 years result in low-velocity biases in the horizontal (0.2 mm yr−1) and vertical (0.5 mm yr−1) components; (2) series durations of less than 4.5 years are not suitable for studies that require precisions lower than mm yr−1; (3) series of intermediate durations (4.5–8.0 years) are associated with an intermediate horizontal bias (0.6 mm yr−1) and a high vertical one (1.3 mm yr−1), unless they comprise no offset. Our results suggest that very long series durations (over 15–20 years) do not ensure a significantly lower bias compared to series of 8–10 years, due to the noise amplitude following a power-law dependency on the frequency. Thus, better characterizations of long-period GPS noise and pluri-annual environmental loads are critical to further improve GPS velocity precisions.

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Masson, Christine / Mazzotti, Stephane / Vernant, Philippe: Precision of continuous GPS velocities from statistical analysis of synthetic time series. 2019. Copernicus Publications.

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Rechteinhaber: Christine Masson et al.

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