On the application of the Stockwell transform to GPR data analysis

Riba, L.; Piro, Salvatore; Battisti, Ubertino; Sambuelli, Luigi

doi:10.1109/iwagpr.2015.7292683

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…Szymczyk et al [5] proposed a three-dimensional ST for identifying sinkholes in geological structures. Riba et al [6] applied the ST to enhance the signalto-noise ratio in 3D GPR data for archaeological exploration. Zhang et al [7] utilized generalized ST slices to identify thin layers in lakebed sediment.…”

Section: Time-frequency (T-f) Transforms Decompose Signals Into the T...mentioning

confidence: 99%

An Improved Synchrosqueezing S-Transform and Its Application in a GPR Detection Task

Xiong,

An,

Sun

et al. 2024

Sensors

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The S-transform is a fundamental time–frequency (T-F) domain analysis method in ground penetrating radar (GPR) data processing and can be used for identifying targets, denoising, extracting thin layers, and high-resolution imaging. However, the S-transform spectrum experiences energy leakage near the instantaneous frequency. This phenomenon causes frequency components to erroneously spread over a wider range, impacting the accuracy and precision of GPR data processing. Synchrosqueezing is an effective method to prevent spectrum leakage. In this work, we introduce the synchrosqueezing generalized phase-shifting S-transform (SS-GPST). Initially, it resolves the compatibility issue between the S-transform and the synchrosqueezing strategy through phase-shifting. Subsequently, the SS-GPST accomplishes spectral energy focusing and resolution enhancement via a generalized parameter and synchrosqueezing. A synthetic signal test shows that the SS-GPST excels over other methods at focusing degree, spectral resolution, and signal reconstruction accuracy and speed. In actual GPR tunnel detection data processing, we assess the adaptability of the SS-GPST from three aspects: spectral energy distribution, thin layer identification, and data denoising. The results indicate: (1) compared to other methods, the SS-GPST accurately expresses spectral components with a strong focusing degree and fewer interference components; (2) high-frequency slices of the SS-GPST accurately detect the top and bottom interfaces of a 3.0–3.5 cm reinforcement protection layer; and (3) due to fewer interference components in the SS-GPST spectrum, reconstructing GPR profiles through the SS-GPST inverse transform is an efficient denoising technique. The SS-GPST demonstrates adaptability to different data processing purposes, offers high-resolution T-F spectra, and shows potential to supersede the S-transform.

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Section: Time-frequency (T-f) Transforms Decompose Signals Into the T...mentioning

confidence: 99%

An Improved Synchrosqueezing S-Transform and Its Application in a GPR Detection Task

Xiong,

An,

Sun

et al. 2024

Sensors

View full text Add to dashboard Cite

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A Multistream Attention Network for Airport Runway Subsurface Target Segmentation

Wang

Zhao

et al. 2023

IEEE Geosci. Remote Sensing Lett.

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Combining ground-penetrating radar sections with different antenna frequencies including time-frequency domain noise suppression filters

2022

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Ground-penetrating radar (GPR) is one of the most useful geophysical methods that rapidly and efficiently provide high-resolution subsurface images depending on antenna frequencies and target depth. Recently, to enrich the interpretation of GPR sections, multifrequency sections have been combined at deeper and shallower levels in a profile. In this context, the techniques of combination and image quality improving processes of GPR data also have gained importance. For this, simple summation (SS) and time-shifted balanced summation (TSBS) techniques, frequency-domain local mean notch filter (FDLMNF), frequency-space ( f- x) filter, and automatic gain control (AGC) processes are sequentially applied within a workflow. Although the FDLMNF is used to effectively remove single-frequency spike-like noises without distortion around the notch frequency, the f- x filter is applied to suppress the random noise and improve the lateral traceability of the section, and AGC provides a statistically based gain recovery. In the proposed workflow, after generating synthetic and field GPR data with 100 and 250 MHz frequencies, data were combined using SS and TSBS techniques. Finally, FDLMNF, f- x, and AGC were applied to increase the image quality of this combined section. The result sections were evaluated and compared using the Fourier average amplitude and f- k spectra. According to the spectra, the TSBS technique provides good spectral balancing and offers wider spectral content. At the same time, it was observed that the image quality of the combined GPR sections increased significantly after applying FDLMNF, f- x, and AGC processes. As a result, when the sections resulting from SS and TSBS techniques are examined, the image enhancement processes that protect and strengthen the spectral bandwidth of the data are successful. Thus, the temporal resolution and spatial continuity of GPR sections also will be increased, and a better quality interpretation will be provided.

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On the application of the Stockwell transform to GPR data analysis

Cited by 4 publications

References 7 publications

An Improved Synchrosqueezing S-Transform and Its Application in a GPR Detection Task

An Improved Synchrosqueezing S-Transform and Its Application in a GPR Detection Task

A Multistream Attention Network for Airport Runway Subsurface Target Segmentation

Combining ground-penetrating radar sections with different antenna frequencies including time-frequency domain noise suppression filters

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