Resumen
The marine atomic interferometric gravimeter is a vital precision instrument for measuring marine geophysical information, which is widely used in mineral resources exploration, military applications, and missile launches. In practical measurements, vibration disturbance is an important factor that affects measurement accuracy. This paper proposes the combination of improved complete ensemble empirical mode decomposition with adaptive noise and locally weighted regression for vibration characterization of gravimeter vibration data. Firstly, the original signal is added into a pair of white noise for adaptive noise-complete ensemble empirical mode decomposition to obtain multiple intrinsic mode functions. The efficient IMF components and noise components are filtered out under the dual indicators of correlation coefficient and variance contribution ratio, and then the LOESS filtering method is used for noise reduction to obtain useful signal detail information; finally, the noise-containing components are reconstructed with the effective components after the noise-reduction process. The experimental results of both simulated and measured vibration signals show that the proposed method can effectively decompose the different high- and low-frequency bands contained in the vibration signal and remove the noise of the original signal.