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

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-06 DOI:10.1038/s41597-024-04300-1
G Roncoroni, P Koyan, E Forte, J Tronicke, M Pipan
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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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一个真实的二维多偏移、多频率合成探地雷达数据集作为测试新算法的基准。
我们提出了一个2D多偏移、多频率合成GPR数据集,专门用于评估和测试处理、分析和反演技术。该数据集在相隔2米的四个剖面上复制了真实的地下条件。我们使用真实的小波模拟了50、100和200 MHz频率下的四个多偏移GPR剖面。该数据集为验证先进的GPR算法和技术(如叠前深度偏移、振幅与偏移量分析和全波形反演)提供了一个强大的框架。广泛的技术验证确保了数据的可重复性和可负担性。标准化的、真实的合成数据集可以作为开发和测试新算法和方法的可靠基准,从而提高对地下成像和真实数据解释的理解。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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