A Gross Error Detection Method of Satellite Gravity Data Based on Joint Variational Autoencoder

Objectives Under the traditional satellite observation data processing operation mode,outlier detection methods in gravity gradiometry have the problems of insufficient accuracy,low efficiency,and so on.

Methods This paper constructs an intelligent outlier detection method,which combines variational autoencoder and gated recurrent unit by the significant characteristics of variational autoencoders in multi-modal data integration analysis. First,on the basis of the origin and propertier of satellite gravity gradionmetry outliers,the gravity gradiometry dataset with outlier are simulated. Second,the network model captures the effective features of dataset by variational autoencoder,makes predictions on dataset by combining with gated recurrent unit,and automatically finds optimal convergence of the loss function by designing adaptive moment estimation as optimizer. Finally,the tested training model is applied to actual gravity observation data of civilian gravity satellites.

Results The accuracy of the proposed model in outlier detection reaches more than 98%,with a good detection effect on both discrete and regional gross errors.

Conclusions The trained network can quickly and accurately construct the sample features of the simulated gravity dataset,achieve fast and efficient gross error detection capabilities,and be effectively applied in data preprocessing of autonomous satellite gravity missions.