A Nineteen Day Earth Tide Measurement with a MEMS Gravimeter

The measurement of tiny variations in local gravity enables the observation of subterranean features. Gravimeters have historically been extremely expensive instruments, but usable gravity measurements have recently been conducted using MEMS (microelectromechanical systems) sensors. Such sensors are cheap to produce, since they rely on the same fabrication techniques used to produce mobile phone accelerometers. A significant challenge in the development of MEMS gravimeters is maintaining stability over long time periods, which is essential for long term monitoring applications. A standard way to demonstrate gravimeter stability and sensitivity is to measure the periodic elastic distortion of the Earth due to tidal forces - the Earth tides. Here we present a nineteen day measurement of the Earth tides, with a correlation coefficient to the theoretical signal of 0.979. The estimated bias instability of the proposed gravimeter is 8.18 microGal for an averaging time of ~400 s when considering the raw, uncompensated data. The bias instability extracted from the sensor electronic noise sits just under 2 mircoGal for an averaging time of ~200 s. After removing the long-term temperature and control electronics effects from the raw data, a linear drift of 268 microGal/day is observed in the data, which is among one of the best reported for a MEMS device. These results demonstrate that this MEMS gravimeter is capable of conducting long-therm time-lapse gravimetry, a functionality essential for applications such as volcanology

Developing and field testing the Wee-g

Abstract not currently available

Development and Assembly of a MEMS Based High-Sensitivity Relative Gravimeter for Multi-Pixel Imaging Applications

The manufacture and production of a high-sensitivity cost-effective gravimeter has the potential to change the methodology and efficiency of gravity measurements. Currently, the most common method to conduct a survey is by using a single gravimeter, usually costing tens of thousands of Dollars, with measurements taken at multiple locations to obtain the required data. The availability of a cost-effective gravimeter however would allow the user to install multiple gravimeters, at the same cost of a single gravimeter, to increase the efficiency of surveys and long-term monitoring. Since the previous reporting on a low-drift relative MEMS gravimeter for multi-pixel imaging applications (Prasad, A. et al, EGU2020-18528), significant progress has been made in the development and assembly of the previously reported system. Field prototypes have been manufactured and undergone significant testing to investigate the stability and robustness of the system in preparation for the deployment of multiple devices as part of the gravity imager on Mount Etna. The device, known as Wee-g, has several key features which makes it an attractive prospect in the field of gravimetry. Examples of these features are that the Wee-g is small and portable with the ability to connect to the device remotely, can be powered through a mains connected power supply, or through portable batteries, weighs under 4kg, has a low power consumption during normal use of 5W, correct for tilt through manual adjustments or remotely through integrated stepper motors with a total tilt correction range of 5 degrees, the ability to read out tilt of the device through an inclinometer for either alignment or long term monitoring and numerous temperature sensors and heater servos to control the temperature of the MEMS to <1mK. This presentation aims to report on the progress that has been achieved in the development and manufacturing of the prototype devices, various testing of the devices under various laboratory conditions (such as the measurements of the Earth tides, and a relative measurement of gravity at various floor levels), as well as additional applications that are to be explored in 2021

An Update to the Development of the Wee-g: A High-Sensitivity MEMS-Based Relative Gravimeter for Multi-Pixel Application

The measurement of tiny variations of gravity over long time-scales or across the landscape has been of interest for geophysicists and various industries since the development of the first modern gravimeter. The manufacturing cost and overall survey time required with commercial gravimeters, however, limit their potential application. The MEMS gravimeter developed at the University of Glasgow, Wee-g, is a small form-factor, high-sensitivity relative gravimeter under development, with its low cost enabling the potential to be used in a multi-pixel setting, such as the network planned to be installed around Mount Etna under the NEWTON-g project. Since the previous reporting of the development and assembly of a MEMS based high-sensitivity relative gravimeter for multi-pixel imaging applications (Toland, K et al, EGU2021-13167), significant progress has been achieved towards the goal of achieving multi-pixel imaging. Wee-g field prototypes have been delivered to end users for various projects, including one currently deployed on Mount Etna since summer 2021. The field prototype running on Mount Etna is running in parallel with an iGrav commercial gravimeter to help understand the characteristics of the Wee-g and allow for comparisons with a commercial device. Currently, multiple final design Wee-g devices are being manufactured for delivery, such as for the multi-pixel array as part of NEWTON-g and for various outdoor field trials. This presentation will report on the analysis of the field prototype Wee-g device that is currently running on Mount Etna, as well as the progress that has been made in manufacturing multiple Wee-g devices, and the outlook for activities that will be running throughout 2022, paving the way to a more effective and detailed method of gravity surveying

A Simulation Study of the Temperature Sensitivity and Impact of Fabrication Tolerances on the Performance of a Geometric Anti-Spring Based MEMS Gravimeter

In this work, the effect of temperature change and fabrication tolerances observed from fabricated devices for a geometric anti-spring (GAS) based Microelectromechanical Systems (MEMS) gravimeter is modelled using Finite Element Analysis (FEA). The temperature-induced effects are analysed in terms of the temperature coefficient of deflection (TCD) for GAS flexures of varying cross-section profiles. The simulated models suggest that the maximum TCD is observed at the minimum stiffness operating points of the flexures. The models also suggest that the cross-sectional shape changes due to fabrication tolerances significantly impact the stiffness, and, hence, the resonant frequency of the devices. Interestingly, it is observed that the temperature sensitivities of the simplified models are found to be mainly dependent on the device material (Si), irrespective of the cross-sectional profiles

A 19 day earth tide measurement with a MEMS gravimeter

The measurement of tiny variations in local gravity enables the observation of subterranean features. Gravimeters have historically been extremely expensive instruments, but usable gravity measurements have recently been conducted using MEMS (microelectromechanical systems) sensors. Such sensors are cheap to produce, since they rely on the same fabrication techniques used to produce mobile phone accelerometers. A significant challenge in the development of MEMS gravimeters is maintaining stability over long time periods, which is essential for long term monitoring applications. A standard way to demonstrate gravimeter stability and sensitivity is to measure the periodic elastic distortion of the Earth due to tidal forces—the Earth tides. Here, a 19 day measurement of the Earth tides, with a correlation coefficient to the theoretical signal of 0.975, has been presented. This result demonstrates that this MEMS gravimeter is capable of conducting long-term time-lapse gravimetry, a functionality essential for applications such as volcanology

D5.1 – MOBILITY-AWARE DECENTRALIZED ANALYTICS v1

<p>The "Mobility-aware Decentralized Analytics v1" of the MobiSpaces Project, hereafter referred as the Deliverable D5.1, reports the work performed under WP5 during its first reporting period (M4 – M10) regarding (i) mobility aware learning at the edge, (ii) FL of spatiotemporal data, (iii) XAI techniques for trustworthiness, fairness and explainability of created models, (iv) visual analytics, and (v) authorization and access control framework. </p><p>In particular, during the aforementioned period, the work of the participating partners, organised under five (5) tasks – from Task 5.1 to Task 5.5 – resulted in methods, tools, and services, related to mobility data, and aiming at their efficient large-scale processing and analytics. Some of the developments are considered preparatory infrastructure actions, to serve (coordinate, integrate, validate, etc.) results that will follow in the next reporting periods, while others constitute our first self-standing research results in mobility data analytics. In particular, </p><p>− the group of preparatory infrastructure actions includes a mobility-aware benchmarking pipeline tool (under Task 5.1), a stream processors and worker containers tool for Federated Learning (FL) client model management (under Task 5.2), a privacy-aware Visual Analytics (VA) backend/frontend tool (under Task 5.4), and a coordinated authorization and access control mechanism (under Task 5.5), whereas </p><p>− a simulated FL environment and a cross-silo Federated forecasting method, called FedVRF (both under Task 5.2), and an eXplainable AI (XAI) library for timeseries (under Task 5.3) are considered self-standing research results. </p><p>Regarding the relation to the Use Cases, specs and data from all four (4) use cases where exploited, including time series and movement data (UC1/2) along with air quality measurements and traffic emissions (UC2) and vessel tracking AIS data (UC3/4). </p><p>The majority of the above results is oriented to feed the Edge Analytics Suite under development in MobiSpaces, hence they are demonstrated in deliverable D2.10; the only exceptions are the benchmarking pipeline tool and the coordinated authorization and access control mechanism, which are considered as parts of the Green and Environmental Dimensioning Workbench and the Data Governance Platform, respectively (thus, demonstrated in deliverables D2.16 and D2.4, respectively).</p&gt

D4.1 – AI-BASED DATA OPERATIONS V1

<p>This is the first of the series of deliverables related to the activities of WP4 ("AI-based Data Management for Green Data Operations"). Following the MobiSpaces Reference Architecture defined under the scope of T2.1 ("Design of Reference Architecture") and its current release reported in D2.1 ("Conceptual Model & Reference Architecture v1"), this document gives more details about one of the major architectural pillars, the AI-based Data Operations Toolbox. </p><p>The overall activities conducted under the scope of WP4 ("AI-based Data Management for Green Data Operations") that are being reported in this document, mostly focus on this particular pillar, with the exception of the T4.4 ("Privacy-driven Data Aggregation"). The latter is considered as part of the Trustworthy Data Governance Services, however, the progress of this task is reported in these series of deliverables that summarize the activities of the whole WP4. In this document, we present the individual software components that are part of the AI-based Data Operations Toolbox, we give details of their interactions, the background technologies that these components are currently being built upon, along with more detailed description of their internal building blocks. </p><p>WP4 focuses on both the data management aspects of MobiSpaces and the data operations of the platform in terms of automating the definition of AI workflows in a declarative manner and their corresponding runtime deployment and orchestration of their entire data lifecycle. The first category of components consists of the Data Management Toolset of the integrated solution that offers a variety of different but complementary data management systems to be exploited by the data users and application developers. For the second category of components, we provide the tools and algorithms for automating the definition and execution of complex AI workflows, consuming data from the aforementioned Data Management Toolset in a transparent manner. The target objective is to execute these workflows in an energy efficient manner, using our novel resource allocator to reduce the carbon emission. </p><p>The duration of WP4 spans from M04 to M34. This deliverable reports the work that has been conducted until M10, which accomplishes the milestone MS04 ("Software prototypes - Iteration I"). At this phase of the project, we have identified the internal building blocks of the AI-based Data Operations Toolbox, the details of their interactions and we have delivered the first release of the corresponding prototypes. In this report our primary focus is on the individual evaluation of the components, while D2.7 ("AI-based Data Operations Toolbox v1") will later focus on the integrated solution based on our prototypes described here, to be evaluated by the project's use cases. Given the different maturity levels of the different components in WP4 at this moment, in this document we either provide some initial evaluation results or a concrete plan for evaluation that will be followed during the next period. Two additional versions are planned to be submitted in M22 and M34, where the second and third release of the prototypes will be available, giving more details of the implementation and final evaluation, implementing all target objectives of the WP4. </p&gt