Bayesian approach for network adjustment for gravity survey campaign: methodology and model test

Variations in the strength of gravity over the Earth’s surface reflect underlying geophysical changes such as groundwater change, surface vertical deformation, tectonic events, earthquakes and other processes. To study such processes, so-called gravity survey campaigns collect observations made repeatedly at fixed stations. The accuracy of this data improves the more quickly the measurements can be made, in part due to the ongoing evolution of the gravitational field, but also because of limitations in measuring apparatus. Many surveys today must rely on relatively inexpensive and convenient portable gravimeters equipped with spring sensors, the accuracy of which is limited by drift in operation over time, caused by factors such as the spring age, temperature inside the instrument, and transportation. These factors cause gradual drift in the measured values, even in unchanging gravity conditions.

To correct for errors, this drift can be modelled with a simple linear function, but this technique becomes increasingly inaccurate for campaigns lasting more than 24 hours. In a new paper, LML External Fellow Jiancang Zhuang and colleagues try to overcome this problem by proposing a new approach for adjusting gravity survey data using objective Bayesian analysis. They revised the network adjustment equations by introducing parameters to balance the residual of gravity survey campaign data and the drift rate of the gravimeter. They then tested this new method on synthetic data sets prepared with different drift models and based on an actual gravity observation network. Zhuang and colleagues evaluated this method by applying it to two observation data sets and compared the adjustment results to the absolute gravity observation.

Their results demonstrate that the new method is more robust and adaptive for solving problems caused by irregular non-linear meter drift. It 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. The authors also carried out an error analysis for the inverted gravity value at each station based on the marginal distribution, and used this approach to process actual gravity survey campaign data from an observation network in North China.

The paper is available at http://bemlar.ism.ac.jp/zhuang/pubs/chen2019jg.pdf

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