Abstract
The drift rate of relative gravimeters differs from time to time and from meter to meter. Furthermore, it is inefficient to estimate the drift rate by returning them frequently to the base station or stations with known gravity values during gravity survey campaigns for a large region. Unlike the conventional gravity adjustment procedure, which employs a linear drift model, we assumed that the variation of drift rate is a smooth function of lapsed time. Using this assumption, we proposed a new gravity data adjustment method by means of objective Bayesian statistical inference. Some hyper-parameters were used as trade-offs to balance the fitted residuals of gravity differences between station pairs and the smoothness of the temporal variation of the drift rate. We employed Akaike’s Bayesian information criterion (ABIC) to estimate these hyper-parameters. A comparison between results from applying the classical and the Bayesian adjustment methods to some simulated datasets showed that the new method is more robust and adaptive for solving problems caused by irregular nonlinear meter drift. The new adjustment method is capable of determining the time-varying drift rate function of any specific gravimeter and optimizing the weight constraints for every gravimeter used in a gravity survey. We also carried out an error analysis for the inverted gravity value at each station based on the marginal distribution. Finally, we used this approach to process actual gravity survey campaign data from an observation network in North China.
Highlights
Gravity survey campaign data generally refer to the gravity observed repeatedly at fixed stations with the same routes and similar time schedules
We proposed a new approach for adjusting gravity survey data using objective Bayesian analysis and minimizing Akaike’s Bayesian information criterion (ABIC) (Sakamoto et al 1988; Malinverno 2000; Mitsuhata 2004)
Relative gravimeters have been widely used in terrestrial gravity measurement
Summary
Gravity survey campaign data generally refer to the gravity observed repeatedly at fixed stations with the same routes and similar time schedules. We rewrote the network adjustment equations by introducing new trade-off parameters that balance the residual of gravity survey campaign data and the drift rate of the relative gravimeter This new method was tested with some synthetic datasets that were prepared with different drift models based on an actual gravity observation network. To reduce the effects of drift rate variation and offsets, the relative gravity difference (GD) between adjacent station pairs is used instead of the actual gravity measurements obtained at each station as the input information for the adjustment Another advantage of using GD is that, for station pairs with a relatively short elapsed time between observations, common-mode signals can be removed automatically (Kennedy et al 2016)
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