RT - Journal Article
T1 - 3-D Crustal Deformation Analysis Using Isoparametric method and Multi-Layer Artificial Neural Networks (Case Study: Iran)
JF - kntu-jgit
YR - 2015
JO - kntu-jgit
VO - 2
IS - 4
UR - http://jgit.kntu.ac.ir/article-1-169-en.html
SP - 1
EP - 15
K1 - Isoparametric
K1 - Artificial neural network
K1 - Strain tensor
K1 - Crustal velocity
K1 - GPS
AB - One of the most important applications of geodesy in geodynamics is study of crustal deformations. In this paper, surface deformation analysis is investigated using 3-D model. 3-D isoparametric method and Iranian permanent GPS network (IPGN) were used to estimate strain tensor. In this method, strain parameters obtained from compares the relative distance between base point and its neighboring points. Since the strain tensor is calculated for each GPS station, considers the strain inhomogeouns and its compatibility with the reality. Due to the special characteristics of isoparametric method, using regularization techniques to solve this problem is inevitable. Tikhonov regularization is used for solving corresponding problem. Optimum value of regularization parameter is selected using minimum relative error in strain parameters, as well as, diagonal elements of resolution matrix is used for error analysis. For estimated velocity field and strain parameters in other geodetic points, in this research artificial neural network (ANN) with 3 layers is used. 4 GPS stations with convenient distribution were used for validating and testing. Minimum relative error obtained from this evaluation for velocity field in eastern component (VE) is 6.25% and northern component (VN) is 6.80%. Also root mean square error (RMSE) is computed ±1.85 (mm) and ±1.72 (mm) in VN and VE respectively. These results are agreement with focal mechanism of earthquakes in this region as well as Iran's geodynamic mechanisms.
LA eng
UL http://jgit.kntu.ac.ir/article-1-169-en.html
M3 10.29252/jgit.2.4.1
ER -