An airborne gravity gradient compensation method based on residual backpropagation

Abstract Airborne gravity gradient dynamic measurement error compensation is a crucial aspect of data processing in gravity gradient dynamic measurements. This study introduces a deep learning approach based on a residual backpropagation (Res-BP) neural network for post-error compensation in airborne gravity gradient dynamic measurement. The network employs residual connections to facilitate identity mapping, thereby enabling gradient propagation across layers. This strategy preserves the original information while acquiring additional information through nonlinear operations, effectively mitigating the gradient vanishing issue and enhancing the neural network’s fitting capability. The method proposed in this paper is applied to both simulation data from a gravity gradiometer and high-altitude dynamic measured data of an airborne gravity gradient. Compared to traditional neural network multilayer perceptron, the Res-BP method significantly improves compensation accuracy through its application in high flight experiment of the southern section of Zhangguangcai Ridge on the western side of Mudanjiang City, Heilongjiang Province.