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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Inverse Profiling of Inhomogeneous Subsurface Targets With Arbitrary Cross Sections Using Covariance Matrix Adaptation Evolution Strategy
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Inverse Profiling of Inhomogeneous Subsurface Targets With Arbitrary Cross Sections Using Covariance Matrix Adaptation Evolution Strategy

机译:使用协方差矩阵自适应演化策略对具有任意横截面的非均匀地下目标进行反特征分析

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摘要

The problem of subsurface inverse profiling of a 2-D inhomogeneous buried dielectric target is addressed in this letter. An iterative optimization technique is proposed that utilizes Covariance Matrix Adaption Evolutionary Strategy (CMA-ES) as its inverse solver and Method of Moments, using Conjugate Gradient-fast Fourier transform, as the forward solver. The numerical results indicate that CMA-ES, as its first reported implementation in buried target reconstruction, can successfully be applied to this challenging reconstruction problem. Also, comparison with Evolutionary Programming and Particle Swarm Optimization indicates that CMA-ES can significantly outperform the other two-optimization techniques in the inhomogeneous subsurface imaging. In addition, examples of various scenarios involving noisy data, lossy targets and multiple targets further demonstrate that CMA-ES can be considered as a robust, simple, and efficient optimization tool in the reconstruction of complex buried targets.
机译:这封信解决了二维非均匀掩埋电介质靶的地下反轮廓问题。提出了一种迭代优化技术,该算法利用协方差矩阵适应进化策略(CMA-ES)作为其逆求解器,并利用共轭梯度快速傅里叶变换作为正向求解器,采用矩量法。数值结果表明,CMA-ES作为其在埋藏目标重建中的首次报道实施,可以成功地应用于这一具有挑战性的重建问题。此外,与进化规划和粒子群优化的比较表明,CMA-ES在非均匀地下成像中可以明显优于其他两种优化技术。此外,涉及嘈杂数据,有损目标和多个目标的各种场景的示例进一步证明,CMA-ES在重建复杂的掩埋目标时可以被视为一种强大,简单而有效的优化工具。

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