Deep learning of GPS geodetic velocity

Abstract

Installing permanent global positioning system (GPS) stations and receiving and monitoring long-term crustal deformation requires a high cost. Another solution, which could be an appropriate alternative, is applying some modern and smart estimation methods such as deep learning of artificial neural networks (DLANN). Based on the observations of the 42 GPS permanent stations in NW Iran, the velocity vectors of stations are estimated in unknown locations by four methods: back propagation of artificial neural networks (BPANN), least square collocation (LSC), Bat algorithm (BA) and random forest algorithm (RFA). BPANN has better performance and less variance than RFA, and the largest difference is in the gap areas, which estimates each vector method differently and it is preferred not to estimate these areas. The performances of LSC and BA algorithm is not better than BPANN. Therefore, it seems that the BPANN method can be considered as a suitable option for estimating the geodetic velocity field compared to other methods.

Keywords

• Geodetic Point Velocity
• Back Propagation
• Deep Learning
• Artificial Neural Networks