Quantitative analysis and cancellation of rotating modulation noise in gravity gradient measurement

Abstract

Rotating accelerometer gravity gradiometer (RAGG) is an important instrument for auxiliary navigation and mineral exploration. The turntable in RAGG modulates the gravity gradient signal to a high frequency to avoid the influence of low-frequency noise. In order to quantitatively analyze and cancel the rotating modulation noise in gravity gradient measurement, the theoretical RAGG output error model with rotating modulation noise is derived and experimentally verified on a high-precision air-bearing turntable. The experimental results indicate that the dominant rotating modulation noise is turntable vibration noise, which results from the supply pressure fluctuations revealed by further analysis. Based on the rotating modulation noise characteristics, a deep neural network (DNN) is trained to cancel the rotating modulation noise from the RAGG output. Positively, the root-mean-square error (RMSE) of the trained DNN output is 1.7 E in sample data. Furthermore, a gravity gradient calibration platform is established to validate the effectiveness of this method by generating a gravity gradient signal with the same frequency as the rotating modulation noise and amplitude of 100 E. After cancelling the noise by the trained DNN, the RMSE of RAGG output reduced from 94 E to 7.7 E, while the RMSE of the result processed by wavelet analysis remains at 87 E. These results demonstrate that the trained DNN successfully cancels 92% of the rotating modulation noise in actual RAGG measurement without affecting the gravity gradient signal.