A GPR 2D Teager-Kaiser energy operator based on the multivariate variational mode decomposition. Issue 1 (2nd January 2023)

• Record Type: Journal Article
• Authors: Zhang, Xuebing; Qin, Yuxiang; Li, Yaoyao; Feng, Xuan; Li, Bonan; Hu, Xin; Song, Zhengchun
• Is Part Of: Remote sensing letters. Volume 14:Issue 1(2023)
• Page Start: 30
• Page End: 38
• Publication Date: 2023-01-02
• Subjects: Ground penetrating radar (GPR) -- multivariate variational mode decomposition (MVMD) -- 2D Teager-Kaiser energy operator (2D-TKEO) -- common-frequency slices; Remote sensing -- Periodicals; Remote sensing; Periodicals; 621.3678
• DOI: 10.1080/2150704X.2022.2154178
• Languages: English
• ISSNs: 2150-704X

Abstract

ABSTRACT: To delineate the anomalies related to subsurface target bodies and the geophysical events, a novel method based on the effective combination of the multivariate variational mode decomposition (MVMD) and 2D Teager-Kaiser operator (2D-TKEO) is proposed for ground-penetrating radar (GPR) data processing in this letter. The proposed method takes advantage of the multi-channel decomposition scheme of MVMD and the multi-directional localization of 2D-TKEO for tracking energy attributes. The time-frequency spectrum can be generated based on the IMF-slices decomposed by the MVMD, on which the 2D-TKEO is introduced to focus the spectrum energy, then the common frequency slices are extracted to reveal the energy components of different frequency ranges. The forward model and GPR field data are applied here to demonstrate that the time-frequency spectrum and the subsequent common-frequency slices based on the proposed MVMD-2DTKEO approach can depict the subsurface target bodies (e.g., voids and pipes) and subsurface layers with higher resolution and better stratigraphic continuity, compared with that of the S-transform and the VMD-1DTKEO.