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Approach on Joint Inversion of Electromagnetic and Acoustic Data Based on Structural Constraints

机译:基于结构约束的电磁和声学数据联合反演的方法

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摘要

Because of strong nonlinearity, it is always a challenge to acquire fine resolution reconstruction results for electric strong scatterers with high contrasts using electromagnetic (EM) data. Many electric strong scatterers have weak nonlinearity in acoustic inversion. To make full use of the advantages of acoustic inversion, two joint inversion methods based on the structural constraints to reconstruct electric strong scatterers using EM and acoustic data are proposed in this article. These two methods utilize the framework of the subspace-based optimization method (SOM). In the inversion process, one of the methods utilizes a cross-gradient function to link the EM and the acoustic inversions, enforcing the structural similarity between the permittivity and the sound velocity. The second method is to utilize the reconstruction result of acoustic data as initial structural information for the EM inversion process. Compared with conventional separate SOM, these two methods achieve finer EM reconstruction for electric strong scatterers. Both structure and contrast values are well reconstructed. The efficiency and the robustness of the methods are validated through numerical experiments.
机译:由于强烈的非线性,从使用电磁(EM)数据具有高对比度的电力强散射体来始终是一个挑战。许多电力强散射体具有声反转的非线性弱。为了充分利用声反转的优点,在本文中提出了基于用于重建电动强散射体的结构约束的两个联合反演方法,并在本文中提出了使用EM和声学数据的影响。这两种方法利用了基于子空间的优化方法的框架(SOM)。在反转过程中,其中一个方法利用交叉梯度函数来链接EM和声反转,从而强制介电常数与声速之间的结构相似性。第二种方法是利用声学数据的重建结果作为EM反转过程的初始结构信息。与常规单独的SOM相比,这两种方法实现了电动强散射体的更精细的EM重建。结构和对比度值都很好地重建。通过数值实验验证了该方法的效率和稳健性。

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