旋转式航空重力梯度仪动态测量误差传递模型与事后误差补偿

航空重力梯度测量技术可快速、高效地完成面积性重力梯度数据采集工作,在矿产资源勘查、军事目标探测等诸多科学领域具有广泛的应用.而航空重力梯度动态测量误差补偿方法是重力梯度动态测量数据处理中的一项重要工作.本文首先对旋转式重力梯度仪误差传递机理进行了定量分析,在综合考虑重力梯度仪系统非理想因素相互作用的情况下,建立了多种非理想因素与外部动态运动参数相耦合的误差传递模型;其次,提出了基于数据驱动的深度学习方法对航空动态测量误差进行补偿,并基于误差传递传递模型建立仿真数据样本集验证了方法的有效性;最后,通过航空重力梯度仪实测数据的处理和应用,验证了本文建立事后误差补偿方法的泛化性,进一步验证了本文建立方法在航空动态测量噪声抑制中的实用性,为航空重力梯度动态测量数据的处理提供技术储备.

Error transfer model and error compensation for dynamic measurement of rotating accelerometer gravity gradiometer

Airborne gravity gradient measurement technology can quickly and efficiently collect the airborne gravity gradient data,which has been widely used in many scientific fields,such as mineral resources exploration and military target detection.The error compensation method of airborne gravity gradient dynamic measurement is an important work in data processing of gravity gradient dynamic measurement.In this paper,the error transfer mechanism of the rotating gravity gradiometer is analyzed quantitatively.Considering the interaction of non-ideal factors in the gravity gradiometer system,the error transfer model coupled with various non-ideal factors and external dynamic motion parameters is established.Secondly,a data-driven deep learning method was proposed to compensate the errors of airborne dynamic measurement,and the effectiveness of the method was verified by establishing a simulation data set based on the error transfer model.Finally,through the processing and application of the airborne gravity gradiometer measured data,the generalization of the post-mission compensation method established in this paper is verified,and the practicability of the method established in this paper in the noise suppression of the airborne dynamic measurement is further verified.This technology provides technical reserves for the processing of airborne gravity gradient dynamic measurement data.