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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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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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