Abstract

We study time series of vertical ground displacements from continuous GNSS stations to investigate the spatial and temporal contribution of different geophysical processes to the time-varying displacements that are superimposed on vertical linear trends across the European Alps. We apply a multivariate statistics-based blind source separation algorithm to both GNSS displacement time series and to ground displacements associated with atmospheric and hydrological loading processes, as obtained from global reanalysis models. This allows us to associate each retrieved geodetic vertical deformation signal with a corresponding forcing process. Atmospheric loading is the most important one, reaching amplitudes larger than 2 cm. Besides atmospheric loading, seasonal displacements with amplitudes of about 1 cm are associated with temperature-related processes and with hydrological loading. We find that both temperature and hydrological loading cause peculiar spatial features of GNSS ground displacements. For example, temperature-related seasonal displacements show different behaviour at sites in the plains and in the mountains. Atmospheric and hydrological loading, besides the first-order spatially uniform feature, are associated also with NS and EW displacement gradients. We filter out signals associated with non-tectonic deformation from the raw time series to study their impact on both the estimated noise and linear rates in the vertical direction. While the impact on rates appears rather limited, given also the long-time span of the time-series considered in this work, the uncertainties estimated from filtered time-series assuming a power law + white noise model are significantly reduced, with an important increase in white noise contributions to the total noise budget. Finally, we present the filtered velocity field and show how vertical ground velocities are positively correlated with topographic features of the Alps.

Highlights

  • The increasing availability of GNSS observations, both from geophysical and non-geophysical networks, pushed forward the use of ground displacement measurements to study active geophysical processes on land, ice and in atmosphere, with applications in a broad range of Earth science disciplines (e.g., Blewitt et al, 2018)

  • We study time series of vertical ground displacements from continuous GNSS stations to investigate the spatial and temporal contribution of different geophysical processes to the time-varying displacements that are superimposed on vertical linear trends across the European Alps

  • Excluding tectonic and volcanological processes, and once removed the effect of tides associated with solid earth, pole and ocean, variations of atmospheric pressure loading and fluid redistribution in the Earth crust are the main cause of vertical 50 ground displacement recorded by GNSS stations worldwide (Liu et al 2015)

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Summary

Introduction

The increasing availability of GNSS observations, both from geophysical and non-geophysical networks, pushed forward the use of ground displacement measurements to study active geophysical processes on land, ice and in atmosphere, with applications in a broad range of Earth science disciplines (e.g., Blewitt et al, 2018). Serpelloni et al (2018) and Pintori et al (2021) use vbICA to characterize hydrological deformation signals associated with the hydrological cycle at a spatial scale not resolvable by GRACE observations, separating ground water storage signals from other surface mass loading signals; while Silverii et al (2021) perform a vbICA decomposition on GNSS time series in the Long Valley Caldera region (California, USA) to separate volcanic-related signals from other deformation processes, in particular the one 160 associated with hydrology This method is recently applied to inSAR data (Gualandi and Liu, 2021) to estimate the displacement caused by sediments’ compaction in San Joaquin Valley (California) and to separate a seasonal signal from the tectonic loading in the Central San Andreas Fault zone

GNSS dataset and time-series analysis
Meteo-climatic datasets
Results
GNSS vs environmental-related displacements
Vertical ground motion rates and noise analysis
Discussion
ICs interpretation
Vertical velocity gradients across the Alps
Conclusions
Full Text
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