Article Details

GPR imaging criteria

Oleh   Tess X.H. Luo [-]
Kontributor / Dosen Pembimbing : Wallace W.L. Lai, Ray K.W. Chang, Dean Goodman
Jenis Koleksi : Jurnal elektronik
Penerbit : Lain-lain
Fakultas :
Subjek :
Kata Kunci : GPR 3D imaging Imaging parameters Standardized workflow
Sumber : Journal of Applied Geophysics 165 (2019) 37–48, https://doi.org/10.1016/j.jappgeo.2019.04.008
Staf Input/Edit : Devi Septia Nurul  
File : 1 file
Tanggal Input : 2019-05-15 13:02:43

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GPR has been widely acknowledged as an effective and efficient technique for imaging the subsurface. But the process of constructing 3D GPR images (C-scans) is still subjective and mainly relies upon the operator's knowledge and experience. This study reviews the parameters that affect GPR imaging quality: namely, profile spacing (PS), slice thickness (ST) and interpolations. Feature characteristics that have a crucial influence on imaging quality were also identified. Through conducting 25 carefully designed empirical experiments on concrete as well as subsurface structures, the relationship between 3D imaging parameters and feature characteristics were observed. A general workflow was derived for GPR C-scan generation, which is analogous to the typical signal processing steps used in 2D radargram signal processing (Jol, 2009). Empirical values in workflow were based on the retrieval of known ground-truth data and comparison with the processed images, i.e. the closest to reality. Unlike 2D processing, the workflow for 3D is feature-oriented and case-specific, and the proposed workflow gives guidelines on suitable ranges for 3 major parameters when used in a variety of applications. It was identified that feature shapes and the ratios of feature size to radar footprint are of vital importance. With the proposed flowchart, the often vague “survey experience” is parametrized and standardized, and the upper and lower limits governing the generation of objective and trustworthy 3D GPR images are defined. This workflow for GPR 3D slice imaging also paves the way for GPR feature extraction and change detection commonly adopted in remote sensing.