An Efficient Deep Learning-Based Troposphere ZTD Dataset Generation Method for Massive GNSS CORS Stations

Nowadays, a large number of Global Navigation Satellite System (GNSS) continuously operating reference stations (CORS) have been established around the world, which have already been and will continue to provide massive troposphere zenith total delay (ZTD) data. This article proposes an efficient deep learning-based troposphere ZTD dataset generation method, including ZTD series segmentation, addictive and innovational outlier detection, and missing data imputation. The overall standard deviation of the first-order ZTD difference series between adjacent epochs of the selected CORS station in January 2018 is reduced from 0.84 to 0.26 mm with the 3-sigma rule and the wavelet decomposition strategy for outlier elimination. A dense neural network (DNN) is subsequently designed to impute missing ZTD data. Only 3.4 min are required for the DNN training using an NVIDIA GeForce RTX 3090Ti graphics processing unit (GPU). The complete ZTD series with missing ZTD data imputed by the well-trained DNN model has a good agreement with the ZTD series before the data imputation in terms of the mean value (-0.0184 versus -0.0187 mm) and standard deviation (5.43 versus 5.26 mm). Complete ZTD series of 120 CORS stations are generated to further evaluate the computational efficiency of the proposed method. An average of 2.56/5.38/5.77/4.33 h are necessary to generate the ZTD dataset for January/April/July/October in 2018, respectively. Our study confirms that the proposed method can efficiently generate a CORS-based ZTD dataset, which could be extended to applications, including troposphere temporal-spatial pattern exploration and ZTD augmentation on high-precision GNSS positioning.