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GPR attribute analysis: There is more than amplitudes

机译：GPR属性分析：振幅不止

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摘要

Ground-penetrating radar (GPR) is a well-established geophysical method to address near-surface problems in various disciplines; for example, archaeology, geology, hydrology, and engineering. As noted by B?niger (2011), the ongoing success of 2D and 3D GPR surveying can be attributed to numerous developments including recent advances in kinematic surveying, electro-magnetic wavefield modelling, and the design and application of novel processing algorithms. Standard processing of 2D and 3D GPR reflection data focuses on enhancing and correctly imaging structural features such as geological layer boundaries or man-made buried objects, although it has been recognized that GPR data may comprise further information, for example, regarding subsurface material properties. Similar observations have been made regarding reservoir-related problems faced by the oil and gas industry using reflection seismic data (Chopra and Marfurt, 2007). Reservoir characterization and its assessment (e.g., the geometry of a salt dome, channel thickness, or material properties of relevant formations) have been significantly improved by the introduction of seismic attributes. In this context, the, term attribute refers to any measure calculated or extracted from the seismic data that helps to enhance or quantify features of interpretation interest (Chopra and Marfurt, 2007). Today's success of 2D and 3D seismic interpretation can clearly be associated with the advances in attribute-driven data analysis, often incorporating ideas and approaches originating from modern signal and image processing algorithms.
机译：探地雷达（GPR）是一种成熟的地球物理方法，可以解决各个学科中的近地表问题。例如考古学，地质学，水文学和工程学。如B？niger（2011）所述，2D和3D GPR测量的持续成功可以归因于众多发展，包括运动学测量，电磁波场建模以及新型处理算法的设计和应用方面的最新进展。 2D和3D GPR反射数据的标准处理着重于增强和正确成像诸如地质层边界或人造掩埋物体之类的结构特征，尽管已经认识到GPR数据可能包括其他信息，例如，有关地下材料的属性。使用反射地震数据对石油和天然气行业面临的与储层相关的问题也进行了类似的观察（Chopra和Marfurt，2007年）。通过引入地震属性，储层特征及其评估（例如盐丘的几何形状，河道厚度或相关地层的材料特性）已得到显着改善。在这种情况下，术语“属性”是指从地震数据中计算或提取的，有助于增强或量化解释兴趣特征的任何度量（Chopra和Marfurt，2007年）。当今2D和3D地震解释的成功显然可以与属性驱动的数据分析的进步相关，通常结合了源自现代信号和图像处理算法的思想和方法。

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