Reverse Time Migration of Ground Penetrating Radar With Optimized Full Wavefield Separation Based on Poynting Vector Imaging Condition and TV-L1-Based Artifacts Suppression | IEEE Journals & Magazine | IEEE Xplore

Reverse Time Migration of Ground Penetrating Radar With Optimized Full Wavefield Separation Based on Poynting Vector Imaging Condition and TV-L1-Based Artifacts Suppression


Abstract:

Reverse time migration (RTM) has the advantage of high-precision imaging, and it can converge the radar wave back to its actual position, making it widely used in radar e...Show More

Abstract:

Reverse time migration (RTM) has the advantage of high-precision imaging, and it can converge the radar wave back to its actual position, making it widely used in radar exploration. However, there are artifacts, low-frequency noise, and fuzzy deep imaging in RTM results. Researchers have proposed full wavefield separation imaging condition and total variation (TV) technique, both of which could suppress noise and artifacts. However, the original wavefield separation method was considerably limited by its extensive calculation, and it cannot solve the problem of weak energy of imaging in the deep zone; the conventional TV technique was likely to be affected by artifacts due to the inevitable oversmoothing suppression of anomaly edges. To address these issues, this article improves the RTM methodology by combining an optimized full wavefield separation based on Poynting vector imaging condition and TV-L1-based artifacts suppressing technique. Specifically, the physical significance of the Poynting vector is introduced to separate the wavefield for reducing the calculation burden, the compensation function is integrated with the imaging condition to compensate for the deep energy, and the TV-L1-based artifacts suppressing method is used to resolve the imaging problem of loss of specific and edge details. Synthetic data and laboratory data experiments are carried out to verify the effectiveness and practicability of the proposed RTM methodology.
Article Sequence Number: 5919115
Date of Publication: 15 September 2023

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