Quantum Sensing - Ground, and Aquifer Monitoring for Environmental Sciences (QS-GAMES)

有保障和安全的饮用水供应对人民的健康和福祉至关重要，但由于对包括地下水变化在内的水文过程的了解有限，这一点受到阻碍。不仅更好地了解我们的供水来源是至关重要的，而且还必须确保宝贵的水不会在输送给用户的过程中丢失或浪费。然而，在世界范围内，泄漏率在20%-30%之间，浪费了宝贵的资源。最近的气候变化在温带地区导致了更多的干旱，同时增加了洪灾的风险，这使得对这些因素的了解变得更加重要。含水层等无形水储存是水文和气候模型未来预测能力中不确定性的主要来源。此外，了解泥炭地地区的水变化可以帮助我们恢复和维护这些宝贵的自然遗址，从而确保埋藏的碳仍然被困住，不会暴露在大气中。泥炭地的恢复可以显著提高我们未来储存碳的能力。QS-Games将开发一种变革性的方法，使用新型量子技术(QT)重力梯度仪传感器来检测土壤中的“看不见”的水，如监测地下水和含水层水位，以及检测地下水管中的泄漏。QS-GAMES将为冷原子重力梯度仪传感器在这些应用中的使用创造一个科学证据基础，与其他传感和数据处理技术一起进行试验，以创建更高空间分辨率的更准确的地下水地图，改变我们对水文过程的理解，并确保未来饮用水的强劲供应。为了实现这些目标，该项目利用了广泛而多样的研究人员小组，他们将合作开发Qt传感器、地球物理、地下公用事业、水文学、环境监测、数据处理和机器学习方面的专业知识。该项目有五个主要工作包：1.管理和传播：这将通过项目研究人员和工业顾问指导委员会，以及与行业利益攸关方、最终用户和更广泛的学术界接触，为项目提供指导。它将审查风险、传播活动和监测进展情况。传感器优化和验证试验：这将使用国家地下基础设施的受控规模实验来评估QT重力梯度和MEMS重力仪传感器的使用，并确定使用这些传感器测量地下含水层和泄漏管道中的水的最佳参数。应用试验：这将在特性良好的测试现场测试新型和现有传感器，并使用广泛的传感器阵列为QT传感器提供基准数据和方法。这将有助于识别与地面显著变化相关的其他调查挑战以及环境条件带来的其他噪声源，并评估Qt传感器如何在这种环境中以最佳性能运行。数据、水文和水流通过土壤建模：这将寻求通过融合多个数据集来更准确地推断地面条件、改进地下水建模和开发实时创建重力图来利用新的和现有的传感器数据的好处。额外的数据将大大增强我们的地下水模型，从而对水流的时间和空间变异性提供更多的信心。调查方法和准则：这将侧重于为不同的调查应用制定适合收集时差重力的方法。它将建立重力数据的质量控制框架，并为从业人员制定指导方针，以确保在所涉应用领域迅速采用这项技术。

A secure and safe supply of potable water is crucial to the health and well-being of the population, yet this is hampered by limited knowledge of the hydrological process including groundwater changes. Not only having a better understanding of the source of our water supply is crucial, but also ensuring no precious water is lost or wasted on route to consumers. Worldwide though, leakage rates are between 20-30% wasting a precious resource. Recent climate change has led to more droughts in temperate zones while at the same time increasing the risk of flooding making the understanding of these factors even more vital. Invisible water storage such as in aquifers is the main source of uncertainty in future prediction capabilities of hydrological and climate models. Also, understanding water changes in peatland areas can help us restore and maintain these precious natural sites thereby ensuring the embedded carbon remains trapped and is not exposed to the atmosphere. Peatland restoration can significantly contribute to our ability to store carbon in the future.QS-GAMES will develop a transformative approach using novel quantum technology (QT) gravity gradiometer sensors for the detection of "invisible" water in soils such as monitoring of groundwater and aquifer levels and leak detection in buried water pipes. QS-GAMES will create a scientific evidence base for the use of cold atom gravity gradiometer sensors for these applications, trialling them alongside other sensing and data processing techniques to create more accurate mapping of subsurface water with a higher spatial resolution, transforming our understanding of the hydrological process and ensuring a robust supply of potable water in the future.To achieve these goals, the project utilises a wide ranging and diverse group of researchers who will work collaboratively, with expertise in QT sensor development, geophysics, buried utilities, hydrology, environmental monitoring, data processing and machine learning. The project has five main work packages:1. Management and dissemination: This will provide steer to the project through the both the researchers on the project and a steering committee of industrial advisors, as well as engage with industrial stakeholders, end users and wider academic communities. It will review risks, dissemination activities and monitor progress.2. Sensor optimisation and validation trials: This will evaluate the use of QT gravity gradient and MEMS gravimeter sensors using controlled scaled experiments in the National Buried Infrastructure facility and determine the optimum parameters for measurement of water in the ground using these sensors for both aquifers and leaking pipes.3. Application Trials: This will test both the novel and existing sensors on well characterised test sites with extensive arrays of sensors providing benchmarking data and methodologies for the QT sensors. This will help identify additional survey challenges associated with significant variations in the ground as well as additional noise sources from environmental conditions and assess how the QT sensors can operate at optimised performance in this environment.4. Data, hydrological and water flow through soil modelling: This will look to exploit the benefits of novel and existing sensor data by fusing multiple datasets to infer ground conditions more accurately, improving groundwater modelling and developing real time creation of gravity maps. The additional data will significantly enhance our groundwater models thereby providing more confidence in the temporal and spatial variability of the water flow.5. Survey Methodologies and Guidelines: This will focus on developing methodologies suitable for the collection of time lapse gravity for the different survey applications. It will establish a quality control framework for gravity data and produce guidelines for practitioners to ensure rapid uptake of the technology in the application areas addressed.