Collaborative Research: Data Fusion for Characterizing and Understanding Water Flow Systems in Karst Aquifers

合作研究：用于表征和理解岩溶含水层水流系统的数据融合

• 批准号: 1933779
• 负责人: Junfeng Zhu
• 金额: $ 42.14万
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1933779&HistoricalAwards=false
• 关键词: Collaborative; Research; Data; Fusion; Characterizing

Aquifers are geologic materials that store and transmit groundwater and supply drinking water for 51% of the total U.S. population. Groundwater is also an indispensable resource for energy production, agricultural and industrial uses. A karst aquifer is a special type of aquifer that is formed in regions underlain by soluble rocks, typically carbonate rocks. Karst aquifers hold 40% of U.S. groundwater. The dissolution of soluble rocks through time creates a complex groundwater flow system in karst aquifers, which is typically characterized by a network of fractures and conduits that connect to the surface water through sinkholes, sinking streams, and springs. Those characteristics make karst aquifers potentially vulnerable to both climate change and contamination. The proposed research seeks to enhance understanding of the complex network of fractures and conduits in karst aquifers in order to advance the prediction of water flow, surface water and groundwater interaction, contaminant transport, nutrient cycle, and the carbon cycle in many karst aquifers under increased stresses from human activities. This project will also greatly benefit the economically distressed karst Appalachian region where students are underrepresented in STEM (Science, Technology, Engineering, and Mathematics). This project is jointly funded by the Hydrologic Sciences (HS) Program and the Established Program to Stimulate Competitive Research (EPSCoR). This research will be centered on conducting collaborative research using a complementary data fusion approach. The approach fuses data collected from hydraulic tomography, river stage tomography, electrical resistivity tomography, and tracer tests to produce a more reliable map of fractures and conduits in karst aquifers in a cost-effective manner. In turn, the results will lead to improved understanding and prediction of flow and solute transport in the karst aquifers. The overarching goal of this project is to develop, test, and validate an innovative approach for characterizing karst aquifers in detail based on the data fusion concept. To achieve this goal, this research will design two new field surveys: surface and subsurface river stage tomography and electrical resistivity tomography with moving current sources. This research will also explore the feasibility of natural lightning tomography for large-scale resistivity surveys. The data collected from these surveys will be integrated into a geostatistical-based inversion framework to characterize the distribution and morphology of fractures and conduits with great detail. The fusion approach will be tested and validated at the Cane Run Royal Spring Basin in central Kentucky. The validity of the characterized karst aquifers will be evaluated using a separate model with the field-collected water level, water chemistry, tracer, and stable isotope data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

蓄水层是储存和输送地下水的地质物质，为美国51%的人口提供饮用水。地下水也是能源生产、农业和工业使用不可缺少的资源。喀斯特含水层是一种特殊类型的含水层，形成于可溶岩石（通常是碳酸盐岩）下面的区域。喀斯特含水层拥有美国40%的地下水。随着时间的推移，可溶性岩石的溶解在喀斯特含水层中形成了一个复杂的地下水流动系统，其典型特征是由裂缝和管道组成的网络，这些裂缝和管道通过天坑、下沉的溪流和泉水与地表水相连。这些特征使得喀斯特含水层可能容易受到气候变化和污染的影响。本研究旨在加强对喀斯特含水层裂隙和管道复杂网络的认识，以推进人类活动压力增加下许多喀斯特含水层的水流、地表水和地下水相互作用、污染物运移、养分循环和碳循环的预测。该项目还将极大地惠及经济困难的喀斯特阿巴拉契亚地区，该地区的学生在STEM（科学、技术、工程和数学）领域的代表性不足。该项目由水文科学（HS）计划和促进竞争性研究的既定计划（EPSCoR）共同资助。本研究将以利用互补的数据融合方法进行合作研究为中心。该方法融合了从水力层析成像、河床层析成像、电阻率层析成像和示踪测试中收集的数据，以经济有效的方式生成了更可靠的岩溶含水层裂缝和管道图。反过来，这些结果将有助于提高对喀斯特含水层流动和溶质运移的认识和预测。该项目的总体目标是开发、测试和验证一种基于数据融合概念详细描述喀斯特含水层的创新方法。为了实现这一目标，本研究将设计两种新的实地调查：地表和地下河流层析成像和移动电流源的电阻率层析成像。本研究还将探讨自然闪电层析成像用于大规模电阻率测量的可行性。从这些调查中收集的数据将被整合到一个基于地质统计学的反演框架中，以非常详细地表征裂缝和管道的分布和形态。这种融合方法将在肯塔基州中部的Cane Run Royal Spring盆地进行测试和验证。将使用一个单独的模型，结合现场收集的水位、水化学、示踪剂和稳定同位素数据，对特征喀斯特含水层的有效性进行评估。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Automatic Segmentation of Sinkholes Using a Convolutional Neural Network

• DOI: 10.1029/2021ea002195
• 发表时间: 2021-12
• 期刊: Earth and Space Science
• 影响因子: 3.1
• 作者: M. U. Rafique;Junfeng Zhu;Nathan Jacobs

Numerical model development for investigating hydrologic pathways in shallow fluviokarst

研究浅层河流喀斯特水文路径的数值模型开发

• DOI: 10.1016/j.jhydrol.2020.125844
• 发表时间: 2021
• 期刊: Journal of Hydrology
• 影响因子: 6.4
• 作者: Al Aamery, Nabil;Adams, Ethan;Fox, James;Husic, Admin;Zhu, Junfeng;Gerlitz, Morgan;Agouridis, Carmen;Bettel, Leonie
• 通讯作者: Bettel, Leonie

Sediment transport investigation in a karst aquifer hypothesizes controls on internal versus external sediment origin and saturation impact on hysteresis

岩溶含水层中的沉积物输运研究假设控制内部与外部沉积物来源以及饱和度对滞后的影响

• DOI: 10.1016/j.jhydrol.2022.128391
• 发表时间: 2022
• 期刊: Journal of Hydrology
• 影响因子: 6.4
• 作者: Bettel, Leonie;Fox, Jimmy;Husic, Admin;Zhu, Junfeng;Al Aamery, Nabil;Mahoney, Tyler;Gold-McCoy, Ariel
• 通讯作者: Gold-McCoy, Ariel

SEASONALITY OF FLOW PATHS AND STORM RESPONSES IN A FLUVIO-KARST SYSTEM

河流喀斯特系统中水流路径的季节性和风暴响应

• 发表时间: 2023
• 期刊: Proceedings of the 17th Sinkhole Conference
• 作者: Tobin, Benjamin W.;Zhu, Junfeng;Webb, Steven;Fox, James F.
• 通讯作者: Fox, James F.