volume | PIER Journals

Abstract

Buried iron pipeline is an important part of urban infrastructure. In order to accurately obtain the location information of buried iron pipeline, here, we establish a forward model of magnetic anomaly in buried iron pipeline based on magnetic dipole reconstruction (MDR) method that determine four inversion parameters and two inversion objective functions. The vertical magnetic field data with different proportion noises are taken as observation values respectively to invert the parameters of underground pipeline and its location (buried depth) by using the adaptive mutation particle swarm optimization (AM-PSO) inversion algorithm. The errors of inversion and observation of vertical magnetic field are compared by substituting the inversion parameters into forward formulas. The results show that the AM-PSO inversion algorithm can accurately invert the pipeline depth, and the inversion error of the pipeline depth is less than 5%, which is acceptable in practical engineering. The inversion of the vertical magnetic field can basically coincide with the observed vertical magnetic field of the original model. At the same time, it is verified that the AM-PSO inversion algorithm is insensitive to magnetic anomaly noise data. In this study, the effectiveness of AM-PSO inversion algorithm method for pipeline depth inversion is analyzed, and an effective optimization inversion method is provided for underground iron pipeline depth inversion.

1. McConnell, T. J., B. Lo, A. Ryder-Turner, and J. A. Musser, "Enhanced 3D seismic surveys using a new airborne pipeline mapping system," SEG Technical Program Expanded Abstracts, 516-519, 1999. Google Scholar

2. Allred, B. J. and J. D. Redman, "Location of agricultural drainage pipes and assessment of agricultural drainage pipe conditions using ground penetrating radar," Journal of Environmental and Engineering Geophysics, Vol. 15, No. 3, 119-134, 2010.
doi:10.2113/JEEG15.3.119 Google Scholar

3. McGrath, P. H. and P. J. Hood, "An automatic least-squares multimodel method for magnetic interpretation," Geophysics, Vol. 38, No. 2, 349-358, 1973.
doi:10.1190/1.1440345 Google Scholar

4. Salem, A. and D. Ravat, "A combined analytic signal and Euler method (AN-EUL) for automatic interpretation of magnetic data," Geophysics, Vol. 68, No. 6, 1952-1961, 2003.
doi:10.1190/1.1635049 Google Scholar

5. Salem, A. and R. Smith, "Depth and structural index from normalized local wavenumber of 2D magnetic anomalies," Geophysical Prospecting, Vol. 53, No. 1, 83-89, 2005.
doi:10.1111/j.1365-2478.2005.00435.x Google Scholar

6. Abdelrahman, E. M. and K. S. Essa, "A new method for depth and shape determinations from magnetic data," Pure and Applied Geophysics, Vol. 172, No. 2, 439-460, 2015.
doi:10.1007/s00024-014-0885-9 Google Scholar

7. Tlas, M. and J. Asfahani, "The simplex algorithm for best-estimate of magnetic parameters related to simple geometric-shaped structures," Mathematical Geosciences, Vol. 47, No. 3, 301-316, 2015.
doi:10.1007/s11004-014-9549-7 Google Scholar

8. Gokturkler, G. and C. Balkaya, "Inversion of self-potential anomalies caused by simple-geometry bodies using global optimization algorithms," Journal of Geophysics and Engineering, Vol. 9, No. 5, 498-507, 2012.
doi:10.1088/1742-2132/9/5/498 Google Scholar

9. Biswas, A., "Interpretation of gravity and magnetic anomaly over thin sheet-type structure using very fast simulated annealing global optimization technique," Modeling Earth Systems and Environment, Vol. 2, No. 1, 30, 2016.
doi:10.1007/s40808-016-0082-1 Google Scholar

10. Ekinci, Y. L., "MATLAB-based algorithm to estimate depths of isolated thin dike-like sources using higher-order horizontal derivatives of magnetic anomalies," Springer Plus, Vol. 5, No. 1, 1384, 2016.
doi:10.1186/s40064-016-3030-7 Google Scholar

11. Guo, Z. Y., D. J. Liu, Q. Pan, Y. Y. Zhang, Y. Li, and Z. Wang, "Vertical magnetic field and its analytic signal applicability in oil field underground pipeline detection," Journal of Geophysics and Engineering, Vol. 12, No. 3, 340-350, 2015.
doi:10.1088/1742-2132/12/3/340 Google Scholar

12. Feng, S., D. Liu, X. Cheng, H. Fang, and C. Li, "A new segmentation strategy for processing magnetic anomaly detection data of shallow depth ferromagnetic pipeline," Journal of Applied Geophysics, Vol. 139, 65-72, 2017.
doi:10.1016/j.jappgeo.2017.02.009 Google Scholar

13. Zhu, H., D. Liu, S. Feng, Q. Pan, and J. Yan, "Magnetic dipole reconstruction geometry modeling for underground ferromagnetic pipe magnetic abnormal detection," Computer Measurement and Control, Vol. 25, No. 3, 201, 2017. Google Scholar

14. Biswas, A., M. P. Parija, and S. Kumar, "Global nonlinear optimization for the interpretation of source parameters from total gradient of gravity and magnetic anomalies caused by thin dyke," Annals of Geophysics, Vol. 60, No. 2, 0218, 2017.
doi:10.4401/ag-7129 Google Scholar

15. Guo, Z., D. Liu, Q. Pan, and Y. Y. Zhang, "Forward modeling of total magnetic anomaly over a pseudo-2D underground ferromagnetic pipeline," Journal of Applied Geophysics, Vol. 113, 14-30, 2015.
doi:10.1016/j.jappgeo.2014.12.011 Google Scholar

16. Wang, F., Y. Song, L. Dong, C. Tao, and X. Lin, "Magnetic anomalies of submarine pipeline based on theoretical calculation and actual measurement," IEEE Transactions on Magnetics, Vol. 55, No. 4, 1-10, 2019.
doi:10.1109/TMAG.2019.2898951 Google Scholar

17. Finlay, C. C., et al., "International geomagnetic reference field: The eleventh generation," Geophysical Journal International, Vol. 183, No. 3, 1216-1230, 2010.
doi:10.1111/j.1365-246X.2010.04804.x Google Scholar

18. Liu, D. G., et al., "Adaptive cancellation of geomagnetic background noise for magnetic anomaly detection using coherence," Measurement Science and Technology, Vol. 26, No. 1, 015008, 2014.
doi:10.1088/0957-0233/26/1/015008 Google Scholar

19. Sen, M. K. and P. L. Stoffa, Global Optimization Methods in Geophysical Inversion, Cambridge University Press, 2013.
doi:10.1017/CBO9780511997570

20. Singh, B., "Simultaneous computation of gravity and magnetic anomalies resulting from a 2-D object," Geophysics, Vol. 67, No. 3, 801-806, 2002.
doi:10.1190/1.1484524 Google Scholar

21. Eberhart, R. and J. Kennedy, "Particle swarm optimization," Proceedings of the IEEE International Conference on Neural Networks, Vol. 4, 1942-1948, 1995. Google Scholar

22. Kennedy, J., "Particle swarm optimization," Encyclopedia of Machine Learning, 760-766, 2010. Google Scholar

23. Merchaoui, M., A. Sakly, and M. F. Mimouni, "Particle swarm optimisation with adaptive mutation strategy for photovoltaic solar cell/module parameter extraction," Energy Conversion and Management, Vol. 175, 151-163, 2018.
doi:10.1016/j.enconman.2018.08.081 Google Scholar

24. Liang, H. and F. Kang, "Adaptive mutation particle swarm algorithm with dynamic nonlinear changed inertia weight," Optik, Vol. 127, No. 19, 8036-8042, 2016.
doi:10.1016/j.ijleo.2016.06.002 Google Scholar

25. Wang, H., W. Wang, and Z. Wu, "Particle swarm optimization with adaptive mutation for multimodal optimization," Applied Mathematics and Computation, Vol. 221, 296-305, 2013.
doi:10.1016/j.amc.2013.06.074 Google Scholar

26. Mohurd Technical Specification for Urban Underground Pipeline Detection and Survey, China Building Industry Press, 2017.

27. Aleardi, M., A. Tognarelli, and A. Mazzotti, "Characterisation of shallow marine sediments using high-resolution velocity analysis and genetic-algorithm-driven 1D elastic full-waveform inversion," Near Surface Geophysics, Vol. 14, No. 5, 449-460, 2016.
doi:10.3997/1873-0604.2016030 Google Scholar

Dr. Pan Wu

Dr. Zhiyong Guo