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

Abstract

Nowadays, a huge amount of GNSS continuously operating reference stations (CORS) have been established around the world, which have already been and will continue providing massive troposphere zenith total delay (ZTD) data. This paper 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 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 minutes are required for the DNN training using a NVIDIA GeForce RTX 3090Ti 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 mean value (-0.0184 vs -0.0187 mm) and standard deviation (5.43 vs 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 hours 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 CORS-based ZTD dataset, which could be extended to applications including troposphere temporal-spatial pattern exploration and ZTD augmentation on high-precision GNSS positioning.