Ground-penetrating radar (GPR) is a standard geophysical technique used to image near-surface structures in sedimentary environments. In such environments, GPR data acquisition and processing are increasingly following 3D strategies. However, the processed GPR data volumes are typically still interpreted using selected 2D slices and manual concepts such as GPR facies analyses. In seismic volume interpretation, the application of (semi-)automated and reproducible approaches such as 3D attribute analyses as well as the production of attribute-based facies models are common practices today. In contrast, the field of 3D GPR attribute analyses and corresponding facies models is largely untapped. We have developed and applied a workflow to produce 3D attribute-based GPR facies models comprising the dominant sedimentary reflection patterns in a GPR volume, which images complex sandy structures on the dune island of Spiekeroog (Northern Germany). After presenting our field site and details regarding our data acquisition and processing, we calculate and filter 3D texture attributes to generate a database comprising the dominant texture features of our GPR data. Then, we perform a dimensionality reduction of this database to obtain meta texture attributes, which we analyze and integrate using composite imaging and (also considering additional geometric information) fuzzy c-means cluster analysis resulting in a classified GPR facies model. Considering our facies model and a corresponding GPR facies chart, we interpret our GPR data set in terms of near-surface sedimentary units, the corresponding depositional environments, and the recent formation history at our field site. Thus, we demonstrate the potential of our workflow, which represents a novel and clear strategy to perform a more objective and consistent interpretation of 3D GPR data collected across different sedimentary environments.

中文翻译：

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生成分类相模型的 3D 探地雷达属性：来自沙丘岛的案例研究

探地雷达 (GPR) 是一种标准地球物理技术，用于对沉积环境中的近地表结构进行成像。在这样的环境中，探地雷达数据采集和处理越来越多地遵循 3D 策略。然而，处理后的 GPR 数据量通常仍使用选定的 2D 切片和手动概念（例如 GPR 相分析）进行解释。在地震体积解释中，应用（半）自动化和可重复的方法，例如 3D 属性分析以及基于属性的相模型的生成是当今的常见做法。相比之下，3D GPR 属性分析和相应相模型的领域在很大程度上尚未开发。我们开发并应用了一个工作流程来生成基于 3D 属性的 GPR 相模型，该模型包括 GPR 体积中的主要沉积反射模式，该模型对 Spiekeroog（德国北部）沙丘岛上的复杂沙质结构进行成像。在介绍了我们的现场以及有关我们的数据采集和处理的详细信息后，我们计算并过滤了 3D 纹理属性以生成一个包含 GPR 数据的主要纹理特征的数据库。然后，我们对该数据库进行降维以获得元纹理属性，我们使用复合成像和（也考虑额外的几何信息）模糊分析和整合这些属性。在介绍了我们的现场以及有关我们的数据采集和处理的详细信息后，我们计算并过滤了 3D 纹理属性以生成一个包含 GPR 数据的主要纹理特征的数据库。然后，我们对该数据库进行降维以获得元纹理属性，我们使用复合成像和（也考虑额外的几何信息）模糊分析和整合这些属性。在介绍了我们的现场以及有关我们的数据采集和处理的详细信息后，我们计算并过滤了 3D 纹理属性以生成一个包含 GPR 数据的主要纹理特征的数据库。然后，我们对该数据库进行降维以获得元纹理属性，我们使用复合成像和（也考虑额外的几何信息）模糊分析和整合这些属性。c - 均值聚类分析导致分类的 GPR 相模型。考虑到我们的相模型和相应的 GPR 相图，我们根据近地表沉积单元、相应的沉积环境和我们现场的最近形成历史来解释我们的 GPR 数据集。因此，我们展示了我们工作流程的潜力，它代表了一种新颖而清晰的策略，可以对在不同沉积环境中收集的 3D GPR 数据进行更客观和一致的解释。