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Full-Waveform Inversion for Subsurface Penetrating Radar based on Adaptive Bilateral Total Variation Regularization
Author(s) -
Laibao Cao,
Tao Liu,
Shen Mengmeng,
Yujun Wang,
Chunlin Huang
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2187/1/012043
Subject(s) - smoothing , ground penetrating radar , inversion (geology) , regularization (linguistics) , total variation denoising , algorithm , radar , synthetic data , mathematics , geology , computer science , computer vision , artificial intelligence , seismology , noise reduction , telecommunications , tectonics
Full-waveform inversion (FWI) of subsurface penetrating radar (SPR) is one of the most promising techniques for quantitative reconstruction of high-resolution dielectric properties distribution. However, due to the incomplete observation data of surface detection, there is significant ill-posedness in inversion and the inversion accuracy is lower than expected. In this paper, an FWI method for SPR data with adaptive bilateral total variation (ABTV) regularization is proposed. The method utilizes ABTV regularization to supplement the data constraints and retains the anomaly body boundary information. In addition, the regularization function of adaptive weight factor matrix is designed for the limitation of the standard bilateral total variation (BTV) regularization, which considers both the smoothing effect and the edge preservation effect. Synthetic experiment shows that the proposed method can reconstruct the permittivity distribution more accurately from 2-D SPR data.

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