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A realistic 2D multi-offset, multi-frequency synthetic GPR data set as a benchmark for testing new algorithms.

G Roncoroni1, P Koyan2, E Forte1

  • 1University of Trieste, Department of Mathematics, Informatics and Geosciences, Trieste, Italy.

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Summary
This summary is machine-generated.

This study introduces a realistic synthetic Ground Penetrating Radar (GPR) dataset for testing advanced processing and inversion techniques. It offers a reproducible benchmark for subsurface imaging algorithm development.

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Area of Science:

  • Geophysics
  • Electrical Engineering

Background:

  • Ground Penetrating Radar (GPR) is crucial for subsurface imaging.
  • Developing and validating GPR processing and inversion techniques requires realistic datasets.

Purpose of the Study:

  • To present a novel 2D multi-offset, multi-frequency synthetic GPR dataset.
  • To provide a standardized benchmark for evaluating GPR data processing, analysis, and inversion algorithms.

Main Methods:

  • Modeled four multi-offset GPR profiles at 50, 100, and 200 MHz frequencies.
  • Replicated realistic subsurface conditions across four sections spaced 2m apart.
  • Utilized realistic wavelets for data generation.

Main Results:

  • The synthetic dataset enables testing of advanced GPR techniques like pre-stack depth migration and full waveform inversion.
  • Ensured data reproducibility and affordability through extensive technical validation.
  • The dataset serves as a reliable benchmark for developing new GPR algorithms.

Conclusions:

  • The presented synthetic GPR dataset is a valuable tool for advancing subsurface imaging and real-world data interpretation.
  • Facilitates the development and validation of sophisticated GPR methodologies.
  • Promotes standardization in GPR research and application.