Estimation of Geodetic Virtual Velocity Based On Back Propagation Artificial Neural Networks (Case Study: NW Iran)

In order to study the crustal movements in Iran, establishment of several campaign GPS networks in 1998 seriously initiated geodynamical activities. After that in 2005, a network of ~120 permanent GPS stations named Iranian Permanent GPS Network (IPGN) has been installed to complete the campaign GPS networks already existing in Iran. Thanks to all campaign and continuous GPS sites, there are many geodetic velocity vectors indicating kinematic behavior of the crust at their positions. Now, the main question is about geodetic velocity for any other arbitrary station. Evidently, the best reliable solution is installing more GPS stations and recording satellite signals, which need considerable cost and time. Another solution, which could be an appropriate alternative, is applying some modern and smart estimation methods such as “Artificial Neural Networks (ANN)”. The main advantages of ANN method are capability learning of networks, parallel processing and computation flexibility. Based on 42 GPS velocity vectors existing in NW Iran, we estimated new velocity vectors for some arbitrary positions in study area by using two estimation “Back Propagation Artificial Neural Networks (BPANN)” and “Collocation”. This estimation was run in 2 models including 2 different reference stations but the same check points. The results from model 1 (with fewer reference points) showed BPANN’s RMSE in E and N components is ±2 mm and ±3.5 mm respectively, which is less than Collocation’s RMSE. The results from model 2 (with more reference points) showed BPANN’s RMSE in E and N components increased to ±1 mm and ±1.5 mm respectively. Therefore, it seems BPANN method could be considered as a good alternative to estimate geodetic velocity field relative to other classical estimation methods.