T. van Dam

Hydrogeological investigations at the Membach station, Belgium, and application to correct long periodic gravity variations

[1] A comprehensive hydrogeological investigation regarding the influence of variations in local and regional water mass on superconducting gravity measurements is presented for observations taken near the geodynamic station of Membach, Belgium. Applying a regional water storage model, the gravity contribution due to the elastic deformation of the Earth was derived. In addition, the Newtonian gravity effect induced by the local water mass variations was calculated, using soil moisture observations taken at the ground surface (about 48 m above the gravimeters). The computation of the gravimetric effect is based on a digital elevation model with spatially discretized rectangular prisms. The obtained results are compared with the observations of a superconducting gravimeter (SG). We find that the seasonal variations can be reasonably well predicted with the regional water storage model and the local Newtonian effects. Shorter-period effects depend on the local changes in hydrology. This result shows the sensitivity of SG observations to very local water storage changes.

Evaluation of the precision of using absolute gravimeters to calibrate superconducting gravimeters

We present an experiment in which four different FG5 absolute gravimeters (AG) were operated simultaneously alongside a superconducting relative gravimeter (SG). We demonstrate that 0.1 % precision can be achieved on the calibration factor of the SG by comparison with AG measurements, independently of the FG5 instrument used for the calibration and of the offsets among the FG5 absolute values. This experiment demonstrates the robustness of using any FG5 absolute gravimeter to calibrate any SG. This result is of value to geoscientists analysing data from (a) globally distributed SGs which most probably have been calibrated using different instruments; or (b) any individual SG calibrated with different FG5 absolute gravimeters.

Results of the European Comparison of Absolute Gravimeters in Walferdange (Luxembourg) of November 2007

The second international comparison of absolute gravimeters was held in Walferdange, Grand Duchy of Luxembourg, in November 2007, in which twenty absolute gravimeters took part. A short description of the data processing and adjustments will be presented here and will be followed by the presentation of the results. Two different methods were applied to estimate the relative offsets between the gravimeters. We show that the results are equivalent as the uncertainties of both adjustments overlap. The absolute gravity meters agree with one another with a standard deviation of 2 μgal (1 gal = 1 cm/s2).

On Time-Variable Seasonal Signals: Comparison of SSA and Kalman Filtering Based Approach

Seasonal signals (annual and semi-annual) in GPS time series are of great importance for understanding the evolution of regional mass, e.g. ice and hydrology. Conventionally, these signals are derived by least-squares fitting of harmonic terms with a constant amplitude and phase. In reality, however, such seasonal signals are modulated, i.e., they have time-variable amplitudes and phases. Davis et al. (J Geophys Res 117(B1):B01,403, 2012) used a Kalman filtering (KF) based approach to investigate seasonal behavior of geodetic time series. Singular spectrum analysis (SSA) is a data-driven method that also allows to derive time-variable periodic signals from the GPS time series. In Chen et al. (J Geodyn 72:25–35, 2013), we compared time-varying seasonal signals obtained from SSA and KF for two GPS stations and received comparable results. In this paper, we apply SSA to a global set of 79 GPS stations and further confirm that SSA is a viable tool for deriving time variable periodic signals from the GPS time series. Moreover, we compare the SSA-derived periodic signals with the seasonal signals from KF with two different input process noise variances. Through the comparison, we find both SSA and KF obtain promising results from the stations with strong seasonal signals. While for the stations dominated by the long-term variations, SSA seems to be superior. We also find that KF with input process noise variance based on variance rates performs better than KF with the input process noise variance based on simulations.