A complete quantitative analysis of self-potential anomaly using singular value decomposition algorithm

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.