Wee-g: a wideband Micro-Electro-Mechanical-Sensor for applications in Gravity Surveying and Gravitational Wave Astronomy

该项目将重点分析Wee-g MEMS重力仪阵列的数据。WEE-G非常新颖，也是世界上第一台MEMS重力仪(R.P.Middlemiss等人，《自然》，531,2016)，它能够显示出足够的灵敏度和稳定性来监测地球潮汐：由于月球和太阳的潮汐势而导致的地球弹性变形。从那时起，该团队一直致力于将该技术商业化，并开发油田原型，与石油和矿产勘探、环境监测以及国防和安全领域的行业合作。该设备是与格拉斯哥拆分的开尔文纳米技术公司(KNT)在詹姆斯·瓦特纳米制造中心内合作制造的，并利用现场可编程门阵列读数来监控设备和倾斜/温度环境通道。该系统将在2021年进行现场测试，目标是在2021/22年在埃特纳山一侧部署20个单元，用于世界上第一个火山重力成像阵列。从重力仪阵列中分析数据以前从未做过，所以我们的方法将是与我们的行业合作伙伴Bridgeporth(http://www.bridgeporth.com/)进行侧面现场测试，他们单个设备的数据可以与多个MEMS“像素”进行比较。我们将在一个区域内部署多个MEMS传感器，并优化阵列以进行空间和时延测量，成像潜在的重力异常，以更高的速率获取数据以更有效地提取地震背景噪声，并应用小波分析来剔除地震噪声。我们将利用引力研究所在贝叶斯推理和机器学习方面的专业知识来识别和学习；通过切换仪器位置、温度敏感度和对外部地震噪声的敏感度来实现仪器漂移，以优化返回的数据。另一个创新机会是在STFC Boulby设施和LIGO站点安装设备，以研究长期地震和重力变化

This project will focus on analysing the data from an array of Wee-g MEMS gravimeters. Wee-g is highly novel and the world's first MEMS gravimeter (R.P. Middlemiss et al., Nature 531, 2016) which was able to show sufficient sensitivity and stability to monitor the Earth tides: elastic deformations in the Earth due to the tidal potential of the Moon and the Sun. Since then, the team have been working on commercialising the technology and developing a field prototype, engaging with industries in the areas of oil & mineral prospecting, environmental monitoring and defence & security. The device is fabricated in partnership with Glasgow spin-out Kelvin Nanotechnology (KNT), within the James Watt Nanofabrication Centre, and utilises an FPGA readout to monitor both the device and tilt/temperature environmental channels. This system is undergoing field trials in 2021, with the aim to deploy 20 units onto the side of Mt Etna in 2021/22, for the world's first gravity imaging array on a volcano.Analysing data from arrays of gravimeters has never been done before, so our methodology will be to undertake side-side field trials with our industry partner Bridgeporth (http://www.bridgeporth.com/ ), where data from their single device can be compared to multiple MEMS "pixels". We will deploy multiple MEMS sensors over a region and optimise the array for spatial and time-lapse surveys, to image underlying gravity anomalies, take data at a higher rate to extract seismic background noise more efficiently, and applying wavelet analysis to notch out seismic noise. We will harness Institute for Gravitational research expertise in Bayesian inference & machine learning to identify and learn; instrumental drift via switching instrument location, temperature sensitivity, and sensitivity to external seismic noise, to optimise the data returned. A further innovative opportunity is installation of devices in the STFC Boulby facility and LIGO sites to study long term seismic and gravity changes