Investigating the Efficiency of Deep Learning Methods in Estimating GPS Geodetic Velocity

Geodetic velocity (GV) has many applications in tectonic motion determination and geodynamic studies. Due to the high cost of global navigation satellite system stations, deep learning methods have been investigated estimate GV. In this research, four convolutional neural networks (CNNs), Boltzmann machines, belief net recurrent applied. The GV 42 positioning stations is entered methods. outputs successfully passed normality test. results show that CNN method a lower goodness fit root mean square error (RMSE). can learn different dependencies extract features.