:: Volume 3, Issue 2 (9-2015) ::
jgit 2015, 3(2): 61-73 Back to browse issues page
Application of the Genetic Algorithm in RegionalGravity Field Modeling Using Spherical Radial Basis Functions
Abdoreza Safari , Hani Mahbuby * , Anahita Shahbazi
University of Tehran
Abstract:   (4624 Views)

Spherical radial basis functions (SRBFs) have been extensively used in regional gravity field modeling. Determining the optimal of the SRBF parameters including their shape and locations is one of the most challenging tasks in SRBF approximation of the Earth's gravity field. In this paper, an optimization strategy is suggested to solve the problem of gravity field modeling using SRBFs. For this purpose, the potential gravity anomaly is expanded into a linear combination of the SRBFs, and then, the system of observation equations is set based on gravity anomaly data. The unknown modeling parameters are consisted of two steps: 1- the 3D position of SRBFs, namely SRBF centers and SRBF depths are determined utilizing the genetic algorithm, and 2- the scaling coefficients in SRBF expansion of the gravity anomaly are determined using the Tikhonov regularization algorithm. In this approach, a chromosome population which includes the 3D position of the kernels is generated and those with more competence are chosen. Furthermore, new chromosomes are produced based on crossover, mutation and migration processes. Therefore, since the kernel positions are obtained via the genetic algorithm, the non-linear problem convert into a linear problem which the coefficients of the expansion for each chromosome can be solved using the Tikhonov regularization algorithm. The performance of the proposed optimization scheme is assessed based on synthetic gravity anomalies provided by EGM2008 up to degree and order of 2160. Finally, an accuracy of 1.08 mGal  in gravity anomalies and
0.78 m2/s2 in anomaly potentials is obtained. The numerical experiments reveal that the proposed optimization algorithm provides an appropriate SRBF distribution which improves the gravimetric models' accuracies.

Keywords: Hyperspectral image, Spectral-Spatial Classification, Dimensionality reduction, Genetic algorithm
Full-Text [PDF 767 kb]   (1553 Downloads)    
Type of Study: Research |
Received: 2016/03/11 | Accepted: 2016/03/11 | Published: 2016/03/11

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Volume 3, Issue 2 (9-2015) Back to browse issues page