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Hybrid PSO-GA Algorithm for Estimation of Magnetic Anomaly Parameters Due to Simple Geometric Structures

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Abstract

The optimization algorithm is a very fast method for modeling magnetic anomalies from an ideal geological model. In addition, these models can be used to explore and estimate mineral deposits. However, in exploratory geophysics, it is common to use modeling methods with regular geometric shapes to estimate anomalous parameters such as shape, material, depth, and elongation angles. In this study, the parameters of the magnetic model are estimated using a hybrid PSO-GA method, which is an evolutionary algorithm based on a combination of particle swarm optimization (PSO) and genetic algorithms (GA). In this method, the PSO algorithm improves the magnetic data, while the GA modifies the decision to estimate the model parameters. Moreover, the balance between exploration and exploitation capabilities improves the performance of this method by combining genetic operators in the hybrid PSO-GA algorithm. Here, Gaussian white noise has been added to the artificial data with different percentages in the range of 0–25% to analyze the error of the obtained model. The results show that the proposed method can provide valuable outcomes in estimating model parameters up to 25% noise. Moreover, the accurate data from an aeromagnetic survey in the Basiran region in South Khorasan province are used to validate the estimated model parameters. The results show that the estimation of the parameters obtained from the current method is consistent with the parameters given from the commercial software and the published geological results.

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References

  • Abdelazeem, M., Fathy, M., & Gobashy, M. (2021). Magnetometric identification of sub-basins for hydrocarbon potentialities in Qattara Ridge, North Western Desert, Egypt. Pure and Applied Geophysics, 178(3), 995–1020.

    Article  Google Scholar 

  • Abdelrahman, E.-S.M., & Essa, K. S. (2005). Magnetic interpretation using a least-squares, depth-shape curves method. Geophysics, 70(3), L23–L30.

    Article  Google Scholar 

  • Abdelrahman, E., & Essa, K. (2015). A new method for depth and shape determinations from magnetic data. Pure and Applied Geophysics, 172(2), 439–460.

    Article  Google Scholar 

  • Abdelrahman, E., El-Arby, H. M., El-Arby, T. M., & Essa, K. S. (2003). A least-squares minimization approach to depth determination from magnetic data. Pure and Applied Geophysics, 160(7), 1259–1271.

    Article  Google Scholar 

  • Abdelrahman, E. S. M., Abo-Ezz, E. R., Essa, K. S., El-Araby, T., & Soliman, K. S. (2007). A new least-squares minimization approach to depth and shape determination from magnetic data. Geophysical Prospecting, 55(3), 433–446.

    Article  Google Scholar 

  • Abdelrahman, E.-S.M., Abo-Ezz, E. R., & Essa, K. S. (2012). Parametric inversion of residual magnetic anomalies due to simple geometric bodies. Exploration Geophysics, 43(3), 178–189.

    Article  Google Scholar 

  • Abdelrahman, E.-S.M., Essa, K. S., El-Araby, T. M., & Abo-Ezz, E. R. (2016). Depth and shape solutions from second moving average residual magnetic anomalies. Exploration Geophysics, 47(1), 58–66.

    Article  Google Scholar 

  • Abo-Ezz, E., & Essa, K. (2016). A least-squares minimization approach for model parameters estimate by using a new magnetic anomaly formula. Pure and Applied Geophysics, 173(4), 1265–1278.

    Article  Google Scholar 

  • Abubakar, R., Muxworthy, A., Sephton, M., Southern, P., Watson, J., Fraser, A., & Almeida, T. (2015). Formation of magnetic minerals at hydrocarbon-generation conditions. Marine and Petroleum Geology, 68, 509–519.

    Article  Google Scholar 

  • Al-Garni, M. A. (2011). Magnetic and DC resistivity investigation for groundwater in a complex subsurface terrain. Arabian Journal of Geosciences, 4(3), 385–400.

    Article  Google Scholar 

  • Araffa, S. A. S., Helaly, A. S., Khozium, A., Lala, A. M., 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(1), 134–146.

    Article  Google Scholar 

  • Balkaya, Ç., Ekinci, Y. L., Göktürkler, G., & Turan, S. (2017). 3D non-linear inversion of magnetic anomalies caused by prismatic bodies using differential evolution algorithm. Journal of Applied Geophysics, 136, 372–386.

    Article  Google Scholar 

  • Bektaş, Ö., Ravat, D., Büyüksaraç, A., Bilim, F., & Ateş, A. (2007). Regional geothermal characterisation of East Anatolia from aeromagnetic, heat flow and gravity data. Pure and Applied Geophysics, 164(5), 975–998.

    Article  Google Scholar 

  • Boschetti, F., Dentith, M., & List, R. (1997). Inversion of potential field data by genetic algorithms. Geophysical Prospecting, 45(3), 461–478.

    Article  Google Scholar 

  • Bresco, M., Raiconi, G., Barone, F., De Rosa, R., & Milano, L. (2005). Genetic approach helps to speed classical Price algorithm for global optimization. Soft Computing, 9(7), 525–535.

    Article  Google Scholar 

  • Clerc, M. (2010). Particle swarm optimization, vol 93. Wiley.

    Google Scholar 

  • Colorni, A., Dorigo, M., & Maniezzo, V. (1991). Distributed optimization by ant colonies. In Proceedings of the first European conference on artificial life.

  • Deb, K. (2000). An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186(2–4), 311–338.

    Article  Google Scholar 

  • Di Maio, R., Rani, P., Piegari, E., & Milano, L. (2016). Self-potential data inversion through a Genetic-Price algorithm. Computers and Geosciences, 94, 86–95.

    Article  Google Scholar 

  • Ekinci, Y. L., Balkaya, Ç., Şeren, A., Kaya, M. A., & Lightfoot, C. S. (2014). Geomagnetic and geoelectrical prospection for buried archaeological remains on the Upper City of Amorium, a Byzantine city in midwestern Turkey. Journal of Geophysics and Engineering, 11(1), 015012.

    Article  Google Scholar 

  • Ekinci, Y. L., Balkaya, Ç., Göktürkler, G., & Turan, S. (2016). Model parameter estimations from residual gravity anomalies due to simple-shaped sources using differential evolution algorithm. Journal of Applied Geophysics, 129, 133–147.

    Article  Google Scholar 

  • Essa, K. S., & Elhussein, M. (2017). A new approach for the interpretation of magnetic data by a 2-D dipping dike. Journal of Applied Geophysics, 136, 431–443.

    Article  Google Scholar 

  • Essa, K. S., & El-Hussein, M. (2017). 2D dipping dike magnetic data interpretation using a robust particle swarm optimization. Geoscientific Instrumentation, Methods and Data Systems Discussions, 20, 1–20.

    Google Scholar 

  • Essa, K. S., & Elhussein, M. (2018). PSO (particle swarm optimization) for interpretation of magnetic anomalies caused by simple geometrical structures. Pure and Applied Geophysics, 175(10), 3539–3553.

    Article  Google Scholar 

  • Farquharson, C. G., & Craven, J. A. (2009). Three-dimensional inversion of magnetotelluric data for mineral exploration: An example from the McArthur River uranium deposit, Saskatchewan, Canada. Journal of Applied Geophysics, 68(4), 450–458.

    Article  Google Scholar 

  • Garg, H. (2016). A hybrid PSO-GA algorithm for constrained optimization problems. Applied Mathematics and Computation, 274, 292–305.

    Article  Google Scholar 

  • Garg, H. (2015). A hybrid GA-GSA algorithm for optimizing the performance of an industrial system by utilizing uncertain data. In Handbook of research on artificial intelligence techniques and algorithms (pp. 620–654). IGI Global.

  • Gay, S. P., Jr. (1963). Standard curves for interpretation of magnetic anomalies over long tabular bodies. Geophysics, 28(2), 161–200.

    Article  Google Scholar 

  • Gobashy, M., & Abdelazeem, M. (2021). Metaheuristics inversion of self-potential anomalies. Self-Potential Method: Theoretical Modeling and Applications in Geosciences (pp. 35–103). Springer.

    Chapter  Google Scholar 

  • Gobashy, M., Abdelazeem, M., & Abdrabou, M. (2020). Minerals and ore deposits exploration using meta-heuristic based optimization on magnetic data. Contributions to Geophysics and Geodesy, 50(2), 161–199.

    Article  Google Scholar 

  • Gobashy, M. M., Eldougdoug, A., Abdelazeem, M., & Abdelhalim, A. (2021). Future development of gold mineralization utilizing integrated geology and aeromagnetic techniques: A case study in the Barramiya Mining District, Central Eastern Desert of Egypt. Natural Resources Research, 30(3), 2007–2028.

    Article  Google Scholar 

  • Gobashy, M., Metwally, A., Abdelazeem, M., Soliman, K., & Abdelhalim, A. (2021). Geophysical exploration of shallow groundwater aquifers in arid regions: A case study of Siwa oasis, Egypt. Natural Resources Research, 30(5), 3355–3384.

    Article  Google Scholar 

  • Grandis, H., & Maulana, Y. (2017). Particle swarm optimization (PSO) for magnetotelluric (MT) 1D inversion modeling. IOP conference series: Earth and Environmental Science.

  • Hamby, D. M. (1994). A review of techniques for parameter sensitivity analysis of environmental models. Environmental Monitoring and Assessment, 32(2), 135–154.

    Article  Google Scholar 

  • Isiet, M., & Gadala, M. (2020). Sensitivity analysis of control parameters in particle swarm optimization. Journal of Computational Science, 41, 101086.

    Article  Google Scholar 

  • Ivakhnenko, O. P., Abirov, R., & Logvinenko, A. (2015). New method for characterisation of petroleum reservoir fluidmineral deposits using magnetic analysis. Energy Procedia, 76, 454–462.

    Article  Google Scholar 

  • Kaftan, İ. (2017). Interpretation of magnetic anomalies using a genetic algorithm. Acta Geophysica, 65(4), 627–634.

    Article  Google Scholar 

  • Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks.

  • Kennedy, J., Eberhart, R., & Shi, Y. (2001). The Morgan Kaufmann series in evolutionary computation.

  • Ku, C. C., & Sharp, J. A. (1983). Werner deconvolution for automated magnetic interpretation and its refinement using Marquardt’s inverse modeling. Geophysics, 48(6), 754–774.

    Article  Google Scholar 

  • Li, Y., & Oldenburg, D. W. (1996). 3-D inversion of magnetic data. Geophysics, 61(2), 394–408.

    Article  Google Scholar 

  • Lines, L., & Treitel, S. (1984). A review of least-squares inversion and its application to geophysical problems. Geophysical Prospecting, 32(2), 159–186.

    Article  Google Scholar 

  • Martínez, J. L. F., Gonzalo, E. G., Álvarez, J. P. F., Kuzma, H. A., & Pérez, C. O. M. (2010). PSO: A powerful algorithm to solve geophysical inverse problems: Application to a 1D-DC resistivity case. Journal of Applied Geophysics, 71(1), 13–25.

    Article  Google Scholar 

  • Mehanee, S. A., & Essa, K. S. (2015). 2.5 D regularized inversion for the interpretation of residual gravity data by a dipping thin sheet: numerical examples and case studies with an insight on sensitivity and non-uniqueness. Earth, Planets and Space, 67(1), 1–26.

    Article  Google Scholar 

  • Moghaddam, M. M., Mirzaei, S., Nouraliee, J., & Porkhial, S. (2016). Integrated magnetic and gravity surveys for geothermal exploration in Central Iran. Arabian Journal of Geosciences, 9(7), 1–12.

    Google Scholar 

  • Moghaddam, M. M., Fanaei, G. K., Mirzaei, S., & Abedi, M. (2019). Interpretation of aerial magnetic data to estimate the depth of magnetic rock foundations and hidden faults in Basiran region, South Khorasan. Geology of Iran, 13, 111–128.

    Google Scholar 

  • Nabighian, M. N., Grauch, V., Hansen, R., LaFehr, T., Li, Y., Peirce, J. W., Phillips, J. D., & Ruder, M. (2005). The historical development of the magnetic method in exploration. Geophysics, 70(6), 33–61.

    Article  Google Scholar 

  • Nyabeze, P., & Gwavava, O. (2016). Investigating heat and magnetic source depths in the Soutpansberg Basin, South Africa: Exploring the Soutpansberg Basin Geothermal Field. Geothermal Energy, 4(1), 1–20.

    Article  Google Scholar 

  • O’Neill, R., Gardner, R., & Mankin, J. (1980). Analysis of parameter error in a nonlinear model. Ecological Modelling, 8, 297–311.

    Article  Google Scholar 

  • Pilkington, M. (2006). Joint inversion of gravity and magnetic data for two-layer models. Geophysics, 71(3), L35–L42.

    Article  Google Scholar 

  • Rao, T. P., & Subrahmanyam, M. (1988). Characteristic curves for the inversion of magnetic anomalies of spherical ore bodies. Pure and Applied Geophysics, 126(1), 69–83.

    Article  Google Scholar 

  • Rao, B., Murthy, I. R., & Rao, C. V. (1973). A computer program for interpreting vertical magnetic anomalies of spheres and horizontal cylinders. Pure and Applied Geophysics, 110(1), 2056–2065.

    Article  Google Scholar 

  • Reza, M. T., Narges, N., Seyed-Mostafa, K., & Alireza, S.-B. (2013). Meta-heuristic algorithms theory and implementation in MATLAB. Islamic Azad University-South Tehran Branch.

    Google Scholar 

  • Sheikhalishahi, M., Ebrahimipour, V., Shiri, H., Zaman, H., & Jeihoonian, M. (2013). A hybrid GA–PSO approach for reliability optimization in redundancy allocation problem. The International Journal of Advanced Manufacturing Technology, 68(1–4), 317–338.

    Article  Google Scholar 

  • Srivastava, S., Datta, D., Agarwal, B., & Mehta, S. (2014). Applications of Ant Colony Optimization in determination of source parameters from total gradient of potential fields. Near Surface Geophysics, 12(3), 373–390.

    Article  Google Scholar 

  • Sweilam, N., Gobarsh, M., & Hashem, T. (2008). Vsing· particle swarm optimization with function stretching {SPSO} For IBver. ftag Gravity data:-a visibility study. Proceedings of the Mathematical & Physical Society of Egypt, 86(2), 259–281.

    Google Scholar 

  • Tarantola, A. (2005). Inverse problem theory and methods for model parameter estimation. SIAM.

  • Thompson, D. (1982). EULDPH: A new technique for making computer-assisted depth estimates from magnetic data. Geophysics, 47(1), 31–37.

    Article  Google Scholar 

  • Tlas, M., & Asfahani, J. (2011). Fair function minimization for interpretation of magnetic anomalies due to thin dikes, spheres and faults. Journal of Applied Geophysics, 75(2), 237–243.

    Article  Google Scholar 

  • Tlas, M., & Asfahani, J. (2015). The simplex algorithm for best-estimate of magnetic parameters related to simple geometric-shaped structures. Mathematical Geosciences, 47(3), 301–316.

    Article  Google Scholar 

  • Van den Bergh, F., & Engelbrecht, A. P. (2004). A cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 225–239.

    Article  Google Scholar 

  • Zhdanov, M. S. (2002). Geophysical inverse theory and regularization problems, vol 36. Elsevier.

    Google Scholar 

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Authors and Affiliations

  1. Petroleum, Mining and Material Engineering Department, Islamic Azad University, Central Tehran Branch, Tehran, Iran

    A. N. Sohouli

  2. Department of Physics, Karaj Branch, Islamic Azad University, Karaj, Iran

    H. Molhem

  3. Department of Physics, Central Tehran Branch, Islamic Azad University, Tehran, Iran

    N. Zare-Dehnavi

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  1. A. N. Sohouli
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  2. H. Molhem
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Sohouli, A.N., Molhem, H. & Zare-Dehnavi, N. Hybrid PSO-GA Algorithm for Estimation of Magnetic Anomaly Parameters Due to Simple Geometric Structures. Pure Appl. Geophys. 179, 2231–2254 (2022). https://doi.org/10.1007/s00024-022-03048-2

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