Optimal zero-crossing group selection method of the absolute gravimeter based on improved auto-regressive moving average model

Abstract

The absolute gravimeter is a precision instrument for measuring gravitational acceleration, which plays an important role in earthquake monitoring, crustal deformation, national defense construction, etc. The frequency of laser interference fringes of an absolute gravimeter gradually increases with the falling time. The data are sparse in the early stage and dense in the late stage. The fitting accuracy of gravity acceleration will be affected by least square fitting according to the fixed number of zero crossing groups. In response to this problem, a method based on Fourier series fitting is proposed in this paper to calculate the zero-crossing point. The whole falling process is divided into five frequency bands using Hilbert transformation. Then the multiplicative auto regression moving average (MARMA) model is trained according to the number of optimal zero crossing groups obtained by the honey badger algorithm. Through this model, the number of optimal zero crossing groups determined in each segment is predicted by least square fitting. Then the mean value of gravity acceleration in each segment is obtained. The method can improve the accuracy of gravity measurement by more than 25% compared to the fixed zero crossing groups method. It provides a new way to improve the measuring accuracy of an absolute gravimeter.