We present a 2D multi-offset, multi-frequency synthetic GPR data set specifically designed to evaluate and test processing, analysis and inversion techniques. The data set replicates realistic subsurface conditions at four sections separated by 2 m. We modeled four multi-offset GPR profiles at 50, 100 and 200 MHz frequencies using realistic wavelets. The data set provides a robust framework for validating advanced GPR algorithms and techniques such as pre-stack depth migration, amplitude versus offset analysis and full waveform inversion. Extensive technical validation ensures data reproducibility and affordability. The standardized, realistic synthetic data set can be used as a reliable benchmark for developing and testing new algorithms and methods, thereby advancing the understanding of subsurface imaging and real-world data interpretation.

A realistic 2D multi-offset, multi-frequency synthetic GPR data set as a benchmark for testing new algorithms / Roncoroni, G., Koyan, P., Forte, E., Tronicke, J., Pipan, M.. - In: SCIENTIFIC DATA. - ISSN 2052-4463. - 12:1(2025), pp. 221.1-221.9. [10.1038/s41597-024-04300-1]

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

Roncoroni, G.
Primo
;
Forte, E.;Pipan, M.
Ultimo
2025-01-01

Abstract

We present a 2D multi-offset, multi-frequency synthetic GPR data set specifically designed to evaluate and test processing, analysis and inversion techniques. The data set replicates realistic subsurface conditions at four sections separated by 2 m. We modeled four multi-offset GPR profiles at 50, 100 and 200 MHz frequencies using realistic wavelets. The data set provides a robust framework for validating advanced GPR algorithms and techniques such as pre-stack depth migration, amplitude versus offset analysis and full waveform inversion. Extensive technical validation ensures data reproducibility and affordability. The standardized, realistic synthetic data set can be used as a reliable benchmark for developing and testing new algorithms and methods, thereby advancing the understanding of subsurface imaging and real-world data interpretation.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/3135780
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