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Reverse time migration of ground‐penetrating radar with full wavefield decomposition based on the Hilbert transform
Author(s) -
Zhong Shichao,
Wang Yibo,
Zheng Yikang,
Chang Xu
Publication year - 2020
Publication title -
geophysical prospecting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.735
H-Index - 79
eISSN - 1365-2478
pISSN - 0016-8025
DOI - 10.1111/1365-2478.12905
Subject(s) - seismic migration , ground penetrating radar , hilbert transform , geology , radar , noise (video) , radar imaging , computer science , acoustics , algorithm , remote sensing , image (mathematics) , seismology , computer vision , physics , telecommunications , filter (signal processing)
The conventional reverse time migration of ground‐penetrating radar data is implemented with the two‐way wave equation. The cross‐correlation result contains low‐frequency noise and false images caused by improper wave paths. To eliminate low‐frequency noise and improve the quality of the migration image, we propose to separate the left‐up‐going, left‐down‐going, right‐up‐going and right‐down‐going wavefield components in the forward‐ and backward‐propagated wavefields based on the Hilbert transform. By applying the reverse time migration of ground‐penetrating radar data with full wavefield decomposition based on the Hilbert transform, we obtain the reverse time migration images of different wavefield components and combine correct imaging conditions to generate complete migration images. The proposed method is tested on the synthetic ground‐penetrating radar data of a tilt‐interface model and a complex model. The migration results show that the imaging condition of different wavefield components can highlight the desired structures. We further discuss the reasons for incomplete images by reverse time migration with partial wavefields. Compared with the conventional reverse time migration methods for ground‐penetrating radar data, low‐frequency noise can be eliminated in images generated by the reverse time migration method with full wavefield decomposition based on the Hilbert transform.