From 3D GPR facies and structures toward 3D petrophysical parameter models

从 3D GPR 相和结构到 3D 岩石物理参数模型

• 批准号: 374920008
• 负责人: Professor Dr. Jens Tronicke
• 金额: --
• 依托单位: Institut für Geowissenschaften
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/374920008?language=en
• 关键词: 3D; GPR; facies; structures; toward

Ground-penetrating radar (GPR) is an established near-surface geophysical tool, which is increasingly employed in variety of applications. Although we see steady methodological progress for improved GPR data acquisition and processing, the concepts and techniques typically employed for interpreting processed GPR reflection images have not significantly changed within the past decades. When exploring near-surface sedimentary environments, 2D and 3D GPR reflection surveying is routinely used to map and image subsurface architecture as needed, for example, to characterize depositional environments or to aid hydrogeological investigations. Even today, the interpretation of such GPR data sets largely relies on manual strategies such as reflector picking (to outline horizons) and delineating units of characteristic reflection patterns (to identify different GPR facies). To interpret GPR data in a more objective, efficient, and also more automated fashion, we have considered attribute-based interpretation strategies and developed, in the preceding project, a workflow for a largely automated delineation of GPR facies. This workflow relies on generating an attribute database consisting of various geometric and texture attributes. After attribute filtering and statistical analyses (e.g., to reduce redundancies information), we derive 3D facies images and classified facies models from our database. As demonstrated by synthetic and field data examples, these facies models outline characteristic structural units being helpful to develop a geological understanding of the buried subsurface. However, for many (e.g., hydrogeological or geotechnical) applications structural facies models are often not sufficient because a more quantitative understanding of subsurface characteristics and processes is needed. For example, in hydrogeology detailed models of the relevant subsurface structures and the governing material properties (i.e., porosity and hydraulic conductivity) are needed to develop a comprehensive understanding of groundwater flow and transport processes. Thus, we aim at extending our so far developed interpretation strategy by including additional point-based information and data as provided, for example, by independent direct-push soundings and borehole logs. We propose different ideas for incorporating such point data into the workflow including approaches based on advanced statistics and machine learning. These approaches will be compared to each other and evaluated using different synthetic and field data examples. Thus, the major objectives of this follow-up project are, firstly, to identify GPR attributes suitable for establishing links to different point-based petrophysical parameters and, secondly, to identify suitable strategies and methods to derive meaningful and reliable 3D models comprising structural and petrophysical characteristics of near-surface sedimentary environments.

探地雷达（GPR）是一种成熟的近地表地球物理工具，它越来越多地应用于各种应用。虽然我们看到稳定的改进探地雷达数据采集和处理的方法进步，通常用于解释处理探地雷达反射图像的概念和技术在过去几十年中没有显着改变。在勘探近地表沉积环境时，2D和3D GPR反射测量通常用于根据需要绘制地下结构图和成像，例如，表征沉积环境或辅助水文地质调查。即使在今天，这种探地雷达数据集的解释在很大程度上依赖于人工策略，如反射体拾取（以勾勒出层位）和描绘特征反射模式的单元（以识别不同的探地雷达相）。为了以更客观、更有效、更自动化的方式解释探地雷达数据，我们考虑了基于属性的解释策略，并在前一个项目中开发了一个工作流程，用于探地雷达相的自动化描绘。此工作流依赖于生成由各种几何和纹理属性组成的属性数据库。在属性过滤和统计分析（例如，为了减少冗余信息），我们从我们的数据库中获得3D相图像和分类相模型。如合成和现场数据实例所示，这些相模型概述了有助于对埋藏地下进行地质了解的特征结构单元。然而，对于许多人来说（例如，水文地质学或岩土工程学）应用结构相模型通常是不够的，因为需要对地下特征和过程进行更定量的理解。例如，在水文地质学中，相关地下结构的详细模型和控制材料特性（即，孔隙度和水力传导性），以全面了解地下水流动和运移过程。因此，我们的目标是通过包括额外的基于点的信息和数据来扩展我们迄今为止开发的解释策略，例如，由独立的直推测深和钻孔测井提供的信息和数据。我们提出了不同的想法，将这些点数据纳入工作流程，包括基于先进的统计和机器学习的方法。这些方法将相互比较，并使用不同的合成和现场数据的例子进行评估。因此，该后续项目的主要目标是，首先，确定适合建立不同的基于点的岩石物理参数的链接的GPR属性，其次，确定合适的策略和方法，以获得有意义的和可靠的三维模型，包括近地表沉积环境的结构和岩石物理特性。