Studying of Central Alborz's crustal velocity by using ANN method

The coordinates of the stations along with their velocity field and determination of the strain field are the most important parameters in determining the surface deformation of the shell. Preliminary estimation of the Earth's crust velocity field, especially in seismic areas and near faults, can provide valuable information on the geodynamic structure as well as how faults operate. Today, this is done by geodynamic network stations. Lack of sufficient number of stations around active faults and tectonic zones is one of the main problems in estimating velocity and strain in these sensitive areas. This factor can cause many problems in studying the mechanism of active and tectonic faults in the relevant areas.Different solutions can be offered to solve such a problem. Paying attention to the reliability of the solution, its accuracy and efficiency, how to do it and most importantly the discussion of time and cost can be important and fundamental factors in this work. Therefore, the main focus of this project is to provide a method with high reliability in results, low cost and high execution speed. Using different interpolation methods such as multilayer artificial neural network (MLP-ANN) or accurate statistical and mathematical methods such as kriging, collocation and polynomial methods can achieve velocity and strain field, especially in areas Be sensitive and responsive. The purpose of this paper is to use modern and accurate methods to estimate and determine the velocity field and displacement field as well as strain tensor parameters in 3D. Artificial neural network (ANN) method with particle mass optimization training (PSO) algorithm for spatial estimation of crustal velocity changes in Iran has been studied. GPS measurements of Central Alborz network stations have been used to evaluate the method.