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Undersea Buried Pipeline Reconstruction Based on the Level Set and Inverse Multiquadric Regularization Method

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Abstract

The electric inversion technique reconstructs the subsurface medium distribution from acquired data. On the basis of electric inversion, objects buried under the earth or seabed, such as pipelines and unexploded ordnance, are detected and located in a contactless manner. However, the process of accurately reconstructing the shape of the target object is challenging because electric inversion is a nonlinear and ill-posed problem. In this work, we present an inverse multiquadric (IMQ) regularization method based on the level set function for reconstructing buried pipelines. In the case of locating underwater objects, the unknown inversion area is split into two parts, the background and the pipeline with known conductivity. The geometry of the pipeline is represented based on the level set function for achieving a noiseless inversion image. To obtain a binary image, the IMQ is used as the regularization term, which ‘pushes’ the level set function away from 0. We also provide an appropriate method to select the bandwidth and regularization parameters for the IMQ regularization term, resulting in reconstructed images with sharp edges. The simulation results and analysis show that the proposed method performs better than classical inversion methods.

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References

  • Adler, A., and Lionheart, W. R., 2006. Uses and abuses of eidors: An extensible software base for EIT. Physiological Measurement, 27: S25.

    Article  Google Scholar 

  • Aghasi, A., Kilmer, M., and Miller, E. L., 2011. Parametric level set methods for inverse problems. SIAM Journal on Imaging Sciences, 4: 618–650.

    Article  Google Scholar 

  • Ammari, H., Garnier, J., and Sølna, K., 2013. Resolution and stability analysis in full-aperture, linearized conductivity and wave imaging. Proceedings of the American Mathematical Society, 141: 3431–3446.

    Article  Google Scholar 

  • Batenburg, K. J., and Sijbers, J., 2011. Dart: A practical reconstruction algorithm for discrete tomography. IEEE Transactions on Image Processing, 20: 2542–2553.

    Article  Google Scholar 

  • Bazeille, S., Lebastard, V., Lanneau, S., and Boyer, F., 2017. Model based object localization and shape estimation using electric sense on underwater robots. IFAC—PapersOnLine, 50(1): 5047–5054.

    Article  Google Scholar 

  • Borsic, A., and Adler, A., 2012. A primal-dual interior-point framework for using the L1 or L2 norm on the data and regularization terms of inverse problems. Inverse Problems, 28: 095011.

    Article  Google Scholar 

  • Borsic, A., Lionheart, W. R., and McLeod, C. N., 2002. Generation of anisotropic-smoothness regularization filters for EIT. IEEE Transactions on Medical Imaging, 21: 579–587.

    Article  Google Scholar 

  • Brown, B. H., 2003. Electrical impedance tomography (EIT): A review. Journal of Medical Engineering and Technology, 27: 97–108.

    Article  Google Scholar 

  • Chambolle, A., and Pock, T., 2011. A first-order primal-dual algorithm for convex problems with applications to imaging. Journal of Mathematical Imaging and Vision, 40: 120–145.

    Article  Google Scholar 

  • Chen, S., Wu, J., Huang, X., and Li, J., 2019. An accurate localization method for subsea pipelines by using external magnetic fields. Measurement, 147: 106803.

    Article  Google Scholar 

  • Constable, S. C., Parker, R. L., and Constable, C. G., 1987. Occam’s inversion: A practical algorithm for generating smooth models from electromagnetic sounding data. Geophysics, 52: 289–300.

    Article  Google Scholar 

  • Fournier, D., and Oldenburg, D. W., 2019. Inversion using spatially variable mixed ℓp norms. Geophysical Journal International, 218(1): 268–282.

    Article  Google Scholar 

  • Halter, R. J., Schned, A., Heaney, J., Hartov, A., Schutz, S., and Paulsen, K. D., 2008. Electrical impedance spectroscopy of benign and malignant prostatic tissues. The Journal of Urology, 179: 1580–1586.

    Article  Google Scholar 

  • Hu, M., Yu, P., Rao, C., Zhao, C., and Zhang, L., 2019. 3D sharp-boundary inversion of potential-field data with an adjustable exponential stabilizing functional. Geophysics, 84: J1–J15.

    Article  Google Scholar 

  • Hua, P., Woo, E. J., Webster, J. G., and Tompkins, W. J., 1991. Iterative reconstruction methods using regularization and optimal current patterns in electrical impedance tomography. IEEE Transactions on Medical Imaging, 10: 621–628.

    Article  Google Scholar 

  • Huang, X., Chen, G., Zhang, Y., Li, J., Xu, T., and Chen, S., 2018. Inversion of magnetic fields inside pipelines: Modeling, validations, and applications. Structural Health Monitoring, 17: 80–90.

    Article  Google Scholar 

  • Huang, X., Chen, S., Guo, S., Zhao, W., and Jin, S., 2013. Magnetic charge and magnetic field distributions in ferromagnetic pipe. Applied Computational Electromagnetics Society Journal, 28: 737–746.

    Google Scholar 

  • Jafarpour, S., Xu, W., Hassibi, B., and Calderbank, R., 2009. Efficient and robust compressed sensing using optimized expander graphs. IEEE Transactions on Information Theory, 55: 4299–4308.

    Article  Google Scholar 

  • Jin, H., Guo, J., Wang, H., Zhuang, Z., Qin, J., and Wang, T., 2020. Magnetic anomaly detection and localization using orthogonal basis of magnetic tensor contraction. IEEE Transactions on Geoscience and Remote Sensing, 58: 5944–5954.

    Article  Google Scholar 

  • Jung, Y. M., and Yun, S., 2014. Impedance imaging with firstorder TV regularization. IEEE Transactions on Medical Imaging, 34: 193–202.

    Article  Google Scholar 

  • Kadu, A., van Leeuwen, T., and Batenburg, K. J., 2017. A parametric level-set method for partially discrete tomography. In: Discrete Geometry for Computer Imagery. DGCI 2017. Kropatsch, W., et al., eds., Springer, Cham, 122–134.

    Chapter  Google Scholar 

  • Kadu, A., van Leeuwen, T., and Mulder, W. A., 2016. Salt reconstruction in full-waveform inversion with a parametric levelset method. IEEE Transactions on Computational Imaging, 3: 305–315.

    Article  Google Scholar 

  • Liu, D., Khambampati, A. K., and Du, J., 2017. A parametric level set method for electrical impedance tomography. IEEE Transactions on Medical Imaging, 37: 451–460.

    Article  Google Scholar 

  • Liu, D., Khambampati, A. K., Kim, S., and Kim, K. Y, 2015. Multi-phase flow monitoring with electrical impedance tomography using level set based method. Nuclear Engineering and Design, 289: 108–116.

    Article  Google Scholar 

  • Martins, J., Moura, C., and Vargas, R., 2018. Image reconstruction using simulated annealing in electrical impedance tomography: A new approach. Inverse Problems in Science and Engineering, 26: 834–854.

    Article  Google Scholar 

  • Miller, L. M., Silverman, Y., MacIver, M. A., and Murphey, T. D., 2015. Ergodic exploration of distributed information. IEEE Transactions on Robotics, 32: 36–52.

    Article  Google Scholar 

  • Pidlisecky, A., Haber, E., and Knight, R., 2007. Resinvm3D: A 3D resistivity inversion package. Geophysics, 72: H1–H10.

    Article  Google Scholar 

  • Portniaguine, O., and Zhdanov, M. S., 1999. Focusing geophysical inversion images. Geophysics, 64: 874–887.

    Article  Google Scholar 

  • Ranjan, S., Kambhammettu, B., Peddinti, S. R., and Adinarayana, J., 2018. A compressed sensing based 3D resistivity inversion algorithm for hydrogeological applications. Journal of Applied Geophysics, 151: 318–327.

    Article  Google Scholar 

  • Renaut, R. A., Vatankhah, S., and Ardestani, V. E., 2017. Hybrid and iteratively reweighted regularization by unbiased predictive risk and weighted GCV for projected systems. SIAM Journal on Scientific Computing, 39: B221–B243.

    Article  Google Scholar 

  • Sadleir, R. J., and Fox, R. A., 2001. Detection and quantification of intraperitoneal fluid using electrical impedance tomography. IEEE Transactions on Biomedical Engineering, 48: 484–491.

    Article  Google Scholar 

  • Shang, W., Xue, W., Li, Y., and Xu, Y., 2020. Improved primaldual interior-point method using the lawson-norm for inverse problems. IEEE Access, 8: 41053–41061.

    Article  Google Scholar 

  • Shang, W., Xue, W., Xu, Y., and Geng, W., 2019. Undersea target reconstruction based on coupled laplacian-of-gaussian and minimum gradient support regularizations. IEEE Access, 7: 171633–171647.

    Article  Google Scholar 

  • Shi, W., Li, Y., and Wang, Y., 2019. Noise-free maximum correntropy criterion algorithm in non-gaussian environment. IEEE Transactions on Circuits and Systems II: Express Briefs, 67: 2224–2228.

    Google Scholar 

  • Simyrdanis, K., Moffat, I., Papadopoulos, N., Kowlessar, J., and Bailey, M., 2018. 3D mapping of the submerged Crowie barge using electrical resistivity tomography. International Journal of Geophysics, 2018: 6480565.

    Article  Google Scholar 

  • Soleimani, M., Lionheart, W., and Dorn, O., 2006. Level set reconstruction of conductivity and permittivity from boundary electrical measurements using experimental data. Inverse Problems in Science and Engineering, 14: 193–210.

    Article  Google Scholar 

  • Sun, B., Yue, S., Cui, Z., and Wang, H., 2015. A new linear back projection algorithm to electrical tomography based on measuring data decomposition. Measurement Science and Technology, 26: 125402.

    Article  Google Scholar 

  • Sun, B., Yue, S., Hao, Z., Cui, Z., and Wang, H., 2019. An improved tikhonov regularization method for lung cancer monitoring using electrical impedance tomography. IEEE Sensors Journal, 19: 3049–3057.

    Article  Google Scholar 

  • Tehrani, J. N., McEwan, A., Jin, C., and Van Schaik, A., 2012. L1 regularization method in electrical impedance tomography by using the L1-curve (Pareto frontier curve). Applied Mathematical Modelling, 36: 1095–1105.

    Article  Google Scholar 

  • Tian, W., 2008. Integrated method for the detection and location of underwater pipelines. Applied Acoustics, 69: 387–398.

    Article  Google Scholar 

  • Utsugi, M., 2019. 3-D inversion of magnetic data based on the L1–L2 norm regularization. Earth, Planets and Space, 71: 73.

    Article  Google Scholar 

  • Vo, C. K., Staples, S., Cowell, D. M., Varcoe, B., and Freear, S., 2020. Determining the depth and location of buried pipeline by magnetometer survey. Journal of Pipeline Systems Engineering and Practice, 11: 04020001.

    Article  Google Scholar 

  • Wolf-Homeyer, S., 2019. Object localization in fluids based on a bioinspired electroreceptor system. PhD thesis. Bielefeld University Germany.

  • Wu, P., and Guo, Z., 2020. High-precision inversion of buried depth inurban underground iron pipelines based on AM-PSO algorithmfor magnetic anomaly. Progress in Electromagnetics Research, 100: 17–30.

    Article  Google Scholar 

  • Xiang, Y., Yu, P., Zhang, L., Feng, S., and Utada, H., 2017. Regularized magnetotelluric inversion based on a minimum support gradient stabilizing functional. Earth, Planets and Space, 69: 158.

    Article  Google Scholar 

  • Zhdanov, M. S., and Portniaguine, O., 1999. Focusing geophysical inversion images. Geophysics, 64: 874–887.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 52101383), the Fundamental Research Funds for the Central Universities (No. 307 2021CF0802), the Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology (No. AMCIT2101-02), the Sino-Russian Cooperation Fund of Harbin Engineering University (No. 2021HEUCRF006), the Ministry of Science and Higher Education of the Russian Federation (No. 075-15-2020-934), and the International Science & Technology Cooperation Program of China (No. 2014DF R10240).

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Correspondence to Yidong Xu.

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Shang, W., Xue, W., Xu, Y. et al. Undersea Buried Pipeline Reconstruction Based on the Level Set and Inverse Multiquadric Regularization Method. J. Ocean Univ. China 21, 101–112 (2022). https://doi.org/10.1007/s11802-022-4837-1

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  • DOI: https://doi.org/10.1007/s11802-022-4837-1

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