Toward a data-driven framework for hydrogeological uncertainty characterization

建立水文地质不确定性表征的数据驱动框架

• 批准号: 392679921
• 负责人: Dr. Falk Hesse
• 金额: --
• 依托单位: Institut für Umweltwissenschaften und Geographie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/392679921?language=en
• 关键词: Toward; data; driven; framework; hydrogeological

The relevance of data to subsurface hydrology, or hydrogeology, is particularly high due to the combination of highly-heterogeneous subsurface properties and the general scarcity of data. This scarcity is caused by the high costs often associated with subsurface exploration. As a result, widely available access to data sets on subsurface conditions should be paramount, since it facilitates the application of data-driven methods like machine learning or Bayesian statistics.Yet collecting these data and making them available to practitioners remains difficult. Currently, the largest database on geostatistical parameters of the subsurface is the World-Wide HYdrogeological Parameters DAtabase (WWHYPDA). It contains approximately 20,000 measurements from 150 sites worldwide. This represents only a small fraction of the total amount of available data, which puts some limit on the characterization of the parametric uncertainty found in the subsurface. Moreover, the WWHYPDA does not contain any information on spatial correlation structures, like correlation lengths or empirical variograms, which means that no information on structural uncertainty can be gleaned from it.In this project, I want to address this problem by increasing the number of data assets available to the community of scientists and practitioners of (stochastic) subsurface hydrology. Mainly two different types of data are going to be used. The first data type is geo-referenced measurements of conductivity and transmissivity fields. They provide the most direct way to estimate spatial correlation structure and are consequently a natural choice. In addition, estimates on statistics of spatial structures exist in the literature and can be used if properly collected and integrated into the database. Finally, pumping tests are going to be used. Pumping tests are a well-established technique for the characterization of subsurface systems. Their application has, however, historically been focussed on the inference of one-point statistics, like the mean value, only. Yet, more recent developments have made it possible to infer two-point statistics, like the correlation length, from pumping tests, as well. After these additional data have been gathered, the final step is to amend existing tools and potentially providing new tools for analyzing, testing and processing these data.If successfully finished, the results from this project would provide a database as well as a number of algorithms, which will facilitate practitioners for the first time to employ data-driven methods to characterize structural uncertainty of the subsurface. If this project succeeds, the process of collecting and making these data available will be streamlined, updated and greatly expanded. In addition, a number of tools will be made available which can help to analyze, process and test these data.

数据与地下水文学或水文地质学的相关性特别高，这是由于高度异质的地下属性和普遍的数据稀缺所致。这种稀缺是由于地下勘探往往伴随着高昂的成本造成的。因此，广泛获得关于地下条件的数据集应该是至关重要的，因为它促进了机器学习或贝叶斯统计等数据驱动方法的应用。然而，收集这些数据并将其提供给从业人员仍然很困难。目前，关于地下地质统计参数的最大数据库是世界水文地质参数数据库(WWHYPDA)。它包含了来自全球150个地点的大约20,000个测量值。这只占可用数据总量的一小部分，这对地下参数不确定性的表征施加了一定的限制。此外，WWHYPDA不包含任何关于空间相关性结构的信息，如相关长度或经验变异函数，这意味着无法从它中收集关于结构不确定性的信息。在这个项目中，我想通过增加(随机)地下水文学科学家和实践者社区可用的数据资产来解决这个问题。主要将使用两种不同类型的数据。第一种数据类型是导电率和透射率场的地理参考测量。它们提供了估计空间相关性结构的最直接方法，因此是一个自然而然的选择。此外，对空间结构统计数据的估计存在于文献中，如果适当收集并合并到数据库中，就可以使用。最后，将使用抽水测试。抽水试验是表征地下系统的一种成熟的技术。然而，它们的应用历史上只集中在一点统计数据的推断上，如平均值。然而，最近的发展使人们有可能从抽水试验中推断出两点统计数据，比如关联长度。在收集了这些额外的数据后，最后一步是修改现有的工具，并可能提供新的工具来分析、测试和处理这些数据。如果成功完成，该项目的结果将提供一个数据库和一些算法，这将有助于从业者首次使用数据驱动的方法来表征地下结构的不确定性。如果该项目成功，收集和提供这些数据的过程将得到精简、更新和极大的扩展。此外，还将提供一些工具，帮助分析、处理和测试这些数据。