A realistic 2D multi-offset, multi-frequency synthetic GPR data set as a benchmark for testing new algorithms
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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-04300-1 |
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| Summary: | 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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| ISSN: | 2052-4463 |