DocumentCode :
3565321
Title :
A complete quantitative analysis of self-potential anomaly using singular value decomposition algorithm
Author :
Candra, Arya Dwi ; Srigutomo, Wahyu ; Sungkono ; Santosa, Bagus Jaya
Author_Institution :
Dept. of Phys., Inst. Teknol. Sepuluh Nopember, Surabaya, Indonesia
fYear :
2014
Firstpage :
1
Lastpage :
4
Abstract :
A new quantitative interpretation method of self potential anomaly related to geometric-shaped models such as horizontal cylinder, vertical cylinder, and sphere object has been proposed in this paper. This method is based on the concept of solving least-squares algorithm with singular value decomposition approach which is designed and implemented to calculate the depth, the electric dipole moment, the polarization angle, and the geometric shape factor of self potential anomaly. This approach uses singular value decomposition algorithm to solve non-linear inversion of self potential anomaly. The singular value decomposition algorithm was randomly tested on theoretical synthetic data which was generated by a chosen statistical distribution from a known model with different random noise level. The result shows there is a close agreement between the assumed and calculated parameters. Finally the method validity is tested on the real self potential data anomaly which is obtained from a cylindrical object that was buried at certain depth.
Keywords :
electric moments; geophysical techniques; least squares approximations; random noise; singular value decomposition; statistical distributions; terrestrial electricity; electric dipole moment; geometric shape factor; geometric-shaped models; horizontal cylinder; least-squares algorithm; nonlinear inversion; polarization angle; quantitative analysis; quantitative interpretation method; random noise level; real self potential data anomaly; singular value decomposition approach; sphere object; statistical distribution; synthetic data; vertical cylinder; Geophysical measurements; Noise; Self-potential anomaly; non-intrusive measurement; non-linear inversion; singular value decomposition algorithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Smart Instrumentation, Measurement and Applications (ICSIMA), 2014 IEEE International Conference on
Print_ISBN :
978-1-4799-8039-0
Type :
conf
DOI :
10.1109/ICSIMA.2014.7047419
Filename :
7047419
Link To Document :
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