PSO (Particle Swarm Optimization) for Interpretation of Magnetic Anomalies Caused by Simple Geometrical Structures
A new efficient approach to estimate parameters that controlled the source dimensions from magnetic anomaly profile data in light of PSO algorithm (particle swarm optimization) has been presented. The PSO algorithm has been connected in interpreting the magnetic anomaly profiles data onto a new formula for isolated sources embedded in the subsurface. The model parameters deciphered here are the depth of the body, the amplitude coefficient, the angle of effective magnetization, the shape factor and the horizontal coordinates of the source. The model parameters evaluated by the present technique, generally the depth of the covered structures were observed to be in astounding concurrence with the real parameters. The root mean square (RMS) error is considered as a criterion in estimating the misfit between the observed and computed anomalies. Inversion of noise-free synthetic data, noisy synthetic data which contains different levels of random noise (5, 10, 15 and 20%) as well as multiple structures and in additional two real-field data from USA and Egypt exhibits the viability of the approach. Thus, the final results of the different parameters are matched with those given in the published literature and from geologic results.
KeywordsMagnetic anomaly PSO algorithm the depth RMS
Authors would like to thank Prof. A. Rabinovich; C. Braitenberg; R. Dmowska Editors-in-Chief, Prof. Dr. Colin Farquharson, the Editor, Prof. Rodrigo Bijani, reviewer, and the other capable reviewer for their constructive comments for enhancing our original manuscript. Thanks are also due to Prof. Salah Mehanee, Geophysics Department, Faculty of Science, Cairo University, for his help and constant encouragement.
- Araffa, S. A. S., Helaly, A. S., Khozium, A., Lala, A. M. S., Soliman, S. A., & Hassan, N. M. (2015). Delineating groundwater and subsurface structures by using 2D resistivity, gravity and 3D magnetic data interpretation around Cairo-Belbies Desert road, Egypt. NRIAG Journal of Astronomy and Geophysics, 4, 134–146.CrossRefGoogle Scholar
- Chau, W. K. (2008). Application of a particle swarm optimization algorithm to hydrological problems. In L. N. Robinson (Ed.), Water Resources Research Progress (pp. 3–12). New York: Nova Science Publishers Inc.Google Scholar
- Colorni, A., Dorigo, M., & Maniezzo, V. (1991). Distributed optimization by ant colonies. In Proceedings of the 1st European conference on artificial life (pp. 134–142).Google Scholar
- Dong, P., Fan, J. L., Liu, C. H., Chen, G. W., Wang, L. S., Sun, B., et al. (2007). Magnetic anomaly characteristics out of reinforcement cage in cast-in situ pile. Progress in Geophysics, 22(5), 1660–1665. (in Chinese).Google Scholar
- Eberhart, R. C., & Shi, Y. (2001). Particle swarm optimization: Developments, applications and resources. In Proceedings of the congress on evolutionary computation, Seoul, Korea (pp. 81–86).Google Scholar
- He, J., & Guo, H. (2013). A modified particle swarm optimization algorithm. Telkomnika, 11(10), 6209–6215.Google Scholar
- Kennedy, J., & Eberhart, R. (1995). Particle Swarm Optimization. In IEEE international conference on neural networks (Perth, Australia): IEEE Service Center, PiscatawaY, NJ, lV (pp. 1942–1948).Google Scholar
- Salem, A., Elsirafi, A., & Ushijima, K. (1999). Design and application of high-resolution aeromagnetic survey over Gebel Duwi area and its offshore extension, Egypt. Memoirs of the Faculty of Engineering, Kyushu University, 59, 201–213.Google Scholar
- Shafiqullah, M., & Langlois, J. D. (1978). The Pima mining district Arizona—A geochronologic update. In New Mexico geological society guidebook 29th annual fall field conference guidebook (pp. 321–327).Google Scholar
- Srivastava, S., Datta, D., Agarwal, B. N. P., & Mehta, S. (2014). Applications of ant colony optimization in determination of source parameters from total gradient of potential fields. Near Surface Geophysics, 12, 373–389.Google Scholar
- Sweilam, N. H., El-Metwally, K., & Abdelazeem, M. (2007). Self potential signal inversion to simple polarized bodies using the particle swarm optimization method: A visibility study. Journal of Applied Geophysics, 6, 195–208.Google Scholar
- Zhdanov, M. S. (2002). Geophysical inversion theory and regularization problems (p. 633). Amsterdam: Elsevier.Google Scholar