Integrating short-time linear canonical transform and joint space-time-frequency analysis for advanced representation of subsurface information in ground penetrating radar
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View abstract on PubMed
Summary
This summary is machine-generated.The Short-Time Linear Canonical Transform (STLCT) enhances Ground Penetrating Radar (GPR) data analysis. STLCT offers superior adaptability and resolution for subsurface exploration compared to traditional methods.
Area Of Science
- Geophysics
- Signal Processing
- Non-destructive Testing
Background
- Ground Penetrating Radar (GPR) is crucial for subsurface exploration.
- Classical time-frequency methods like STFT face limitations due to the time-bandwidth product theorem.
- Analyzing non-smooth, time-varying GPR signals requires advanced techniques.
Purpose Of The Study
- Introduce the Short-Time Linear Canonical Transform (STLCT) for GPR signal processing.
- Address limitations of classical methods in analyzing complex GPR signals.
- Enhance time-frequency localization and resolution for subsurface anomaly detection.
Main Methods
- Developed the theoretical foundation of STLCT, including its properties and a convolution theorem.
- Simulated subsurface scenarios using GPRMAX 2.0 for validation.
- Evaluated STLCT performance on synthetic and real-world GPR data.
Main Results
- STLCT demonstrates superior adaptability, clarity, and robustness over classical methods.
- Improved signal interpretation in complex subsurface environments.
- STLCT offers enhanced time-frequency resolution for GPR data.
Conclusions
- STLCT is a powerful and flexible alternative for GPR signal processing.
- The method provides significant improvements for subsurface anomaly detection.
- STLCT represents a practical enhancement for advanced GPR analysis pipelines.
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