ISSN 1000-3665 CN 11-2202/P

    地下水机器学习方法研究水位监测数据驱动的区域补排边界识别

    Application of machine learning to aquifer analyses:Locating hydrogeological boundaries with water table monitoring data

    • 摘要: 随着我国地下水监测工作的高速发展,高频率高密度水位监测数据的出现催生了对其进行深入信息挖掘的需求。在传统地下水模型研究中,地下水水位监测值常位于模型构建过程的下游,当水位监测的时空密度逐渐增大时,新增信息无法有效传导至模型的规划阶段并指导概念模型的修订。文章提出了一种地下水系统补排边界的识别方法,在不建立地下水数值模型的前提下,以监测井空间位置为节点,按照德劳内原则建立三角网格。在此网格系统中,首先定义一个水力梯度变换函数gradF,以求取网格中任意位置的水力梯度;借鉴机器学习领域的优化算法,使用水力梯度场驱动含水层中随机分布质点的运行轨迹,并以此推断和识别区域内地下水补给和排泄边界。在环境地学计算平台EnviFusion-CGS中实现,并构建了详细工作流程。以山东省青岛市大沽河中下游含水层为示范区,对含水系统的补给区和排泄区的空间分布及其动态变化进行了分析,取得了良好效果。本研究为构建和修订已有含水层概念模型提供了新思路。

       

      Abstract: Groundwater level fluctuations in China are being monitored with unprecedented frequency and density, which drives the need for mining such types of data. In a typical aquifer analysis project, groundwater level data is generally applied after the completion of the aquifer conceptual framework. When the temporal and spatial density of groundwater level data gradually increases, the information gain needs to be effectively transformed into conceptual knowledge of the model. In this study, we propose a method to identify hydrological boundaries based on the groundwater level monitoring data. In this method we discretize space into a triangular mesh using monitoring wells as the initial nodes, and a transformation function gradF is defined to calculate the hydraulic gradient at any given location on the mesh. The hydraulic gradient field is subsequently use to drive an array of randomly scattered particles to obtain the streamline representation of the flow field, which will in turn serve as the basis for deducing and refining the recharge and discharge boundaries of a hydrogeological domain. This method is implemented into the geo-environmental scientific computation platform (EnviFusion-CGS), and a detailed work flow is developed to facilitate the development of the aquifer conceptual model. This method is applied to the hydrogeological investigation of the Dagu aquifer located in Qingdao of Shandong Province, where the spatial distributions and dynamic fluctuations of the hydrogeological boundaries are identified.

       

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