Research on the Temperature Effect and Postprocessing Compensation Methods of Zero-Length Spring Relative Gravimeter

In order to effectively solve the problems of ambient temperature variation significantly that affects the measurement accuracy of the gravimeter and the customary temperature control scheme tht severely restricts the rapid start-up capability of gravity measurement, a two-layer temperature compensation algorithm (TLTCA) for a zero-length spring relative gravimeter is proposed. The upper layer temperature compensation model is constructed based on influence mechanisms and correlation analysis, and the population initialization and position update formulas of the sparrow search algorithm (SSA) used to optimize model coefficients are improved to form an improved SSA algorithm (ISSA), which significantly improves the search capability of the global optimal solution. A large-scale measured dataset was created to train the lower layer temperature compensation model using a BP neural network (BPNN). The TLTCA integrates the advantages of the upper and lower models, improving the accuracy and stability of the gravimeter output. The temperature compensation effects of SSA, ISSA, BPNN, and TLTCA are examined under four experimental environments, namely, constant external temperature, natural temperature variation, $1~^{\circ }$ C/min temperature rise and fall, and rapid temperature variation. The results are compared to the least-squares method (LSM), commonly used in engineering practice. The experimental results demonstrate that the TLTCA has the best effect, with the standard deviation (STD) after compensation up to 0.89, 2.76, 8.66, and $12.02~\mu $ Gal, which is comparable to that of the high-precision temperature control scheme and has important reference significance for enhancing gravimeter rapid start-up and environmental adaptability.