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

Wee-g：用于重力测量和引力波天文学应用的宽带微机电传感器

• 批准号: 2604937
• 金额: --
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2604937
• 关键词: Wee; wideband; Micro; Electro; Mechanical

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

该项目将着重分析来自WEE-G MEMS重量表的数据。 Wee-G是高度新颖的，是世界上第一个MEMS重量表（R.P. Middlemiss等人，Nature 531，2016），它能够表现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，地球上的弹性变形。从那以后，该团队一直致力于商业化技术并开发现场原型，并与石油和矿产勘探，环境监测以及国防与安全领域的行业互动。该设备是与詹姆斯·瓦特纳米制造中心内格拉斯哥纺纱纳米技术（KNT）合作制造的，并利用FPGA读数来监视设备和倾斜/温度环境通道。该系统将在2021年进行现场试验，目的是将20个单位部署到2021/22 mt ETNA的一侧，以便全球在火山上的第一个重力成像阵列。从未做过的重量表中的Analyalysys数据以前从未进行过，因此我们的方法论可以与我们的行业合作伙伴进行互联网（我们的行业杂货）（ht），该数据范围（ht）（ht trapports），该数据范围（我们的行业bridgetp）（ht）（ht）（H）可以将其单个设备与多个内存“像素”进行比较。我们将在一个区域上部署多个MEMS传感器，并优化空间和延时调查的阵列，以图像潜在的重力异常，以较高的速率获取数据以更有效地提取地震背景噪声，并将小波分析应用于Notch notch seistic sisisic噪声。我们将利用贝叶斯推理和机器学习的引力研究专业知识来识别和学习；通过切换仪器位置，温度敏感性以及对外部地震噪声的敏感性的仪器漂移，以优化返回的数据。另一个创新的机会是在STFC Boulby设施和Ligo站点安装设备，以研究长期地震和重力变化