Near-surface magnetic resonance imaging

Nuclear magnetic resonance (NMR) spectroscopy and magnetic resonance imaging (MRI) are well established techniques. Both require samples to be placed inside spatially-homogeneous static magnetic and radio frequency magnetic fields. This research focuses on applying the principles of NMR spectroscopy and imaging to design a system that can produce images of the NMR signal from a sample placed adjacent to it. The long term goal is to image at distances of meters from the device. The specific application of interest is locating buried utilities in fully-hydrated soils by identifying non-signal bearing features in an NMR signal rich background. This thesis asserts that a minimum of three questions need to be answered to determine if near surface MRI is feasible. 1.) Can the Earth\u27s magnetic field (BEarth) be used as the applied static magnetic field? 2.) Is the NMR signal from the water found in fully-hydrated soils sufficient for imaging? 3.) Can a near-surface imaging technique be developed? This thesis research attempts to answer each of these questions in three stand alone investigations: 1.) In situ measurements of BEarth, 2.) laboratory-based relaxation studies of synthetic sands, and 3.) the development of a proof-of-concept near-surface magnetic resonance imaging device. It is the assertion of this thesis that the results of these three studies are needed to begin to properly understand the signal space that a viable surface instrument would have to operate within for practical use. The BEarth results showed that better selection performance would be achieved using a larger B0 field. The synthetic sands study gave results suggest that relaxation behavior of water was highly deterministic based on particle diameter. The imaging performance of the unilateral MRI device was measured and the results demonstrate that imaging at depths on the order of one centimeter is possible. Overall, the results from these three studies have advanced the understanding of near-surface inhomogeneous magnetic resonance, while giving strong indications that near-field MRI in fully-hydrated soil is promising

Theoretical Developments in Electromagnetic Induction Geophysics with Selected Applications in the Near Surface

Near-surface applied electromagnetic geophysics is experiencing an explosive period of growth with many innovative techniques and applications presently emergent and others certain to be forthcoming. An attempt is made here to bring together and describe some of the most notable advances. This is a difficult task since papers describing electromagnetic induction methods are widely dispersed throughout the scientific literature. The traditional topics discussed herein include modeling, inversion, heterogeneity, anisotropy, target recognition, logging, and airborne electromagnetics (EM). Several new or emerging techniques are introduced including landmine detection, biogeophysics, interferometry, shallow-water electromagnetics, radiomagnetotellurics, and airborne unexploded ordnance (UXO) discrimination. Representative case histories that illustrate the range of exciting new geoscience that has been enabled by the developing techniques are presented from important application areas such as hydrogeology, contamination, UXO and landmines, soils and agriculture, archeology, and hazards and climat

Detecting graves in GPR data: assessing the viability of machine learning for the interpretation of graves in B-scan data using medieval Irish case studies.

As commercial archaeogeophysical survey progressively shifts towards large landscape-scale surveys, small features like graves become more difficult to identify and interpret. In order to increase the rate and confidence of grave identification before excavation using geophysical methods, the accuracy and speed of survey outputs and reporting must be improved. The approach taken in this research was first to consider the survey parameters that govern the effectiveness of the four conventional techniques used in commercial archaeogeophysical evaluations (magnetometry, earth resistance, electromagnetic induction and ground-penetrating radar). Subsequently, in respect of ground-penetrating radar (GPR), this research developed machine learning applications to improve the speed and confidence of detecting inhumation graves. The survey parameters research combined established survey guidelines for the UK, Ireland, and Europe to account for local geology, soils and land cover to provide survey guidance for individual sites via a decision-based application linked to GIS database. To develop two machine learning tools for localising and probability scoring grave-like responses in GPR data, convolutional neural networks and transfer learning were used to analyse radargrams of medieval graves and timeslices of modern proxy clandestine graves. Models were c. 93% accurate at labelling images as containing a grave or no grave and c. 96% accurate in labelling and locating potential graves in radargram images. For timeslices, machine learning models achieved 94% classification accuracy. The >90% accuracy of the machine learning models demonstrates the viability of machine-assisted detection of inhumation graves within GPR data. While the expansion of the training dataset would further improve the accuracy of the proposed methods, the current machine-led interpretation methods provide valuable assistance for human-led interpretation until more data becomes available. The survey guidance tool and the two machine learning applications have been packaged into the Reilig web application toolset, which is freely available

Classification and repeatability studies of transient electromagnetic measurements with respect to the development of CO2-monitoring techniques

The mitigation of greenhouse gases, like CO2 is a challenging aspect for our society. A strategy to hamper the constant emission of CO2 is utilizing carbon capture and storage technologies. CO2 is sequestrated in subsurface reservoirs. However, these reservoirs harbor the risk of leakage and appropriate geophysical monitoring methods are needed. A crucial aspect of monitoring is the assignment of measured data to certain events occurring. Especially if changes in the measured data are small, suitable statistical methods are needed. In this thesis, a new statistical workflow based on cluster analysis is proposed to detect similar transient electromagnetic signals. The similarity criteria dynamic time warping, the autoregressive distance, and the normalized root-mean-square distance are investigated and evaluated with respect to the classic Euclidean norm. The optimal number of clusters is determined using the gap statistic and visualized with multidimensional scaling. To validate the clustering results, silhouette values are used. The statistical workflow is applied to a synthetic data set, a long-term monitoring data set and a repeat measurement at a pilot CO2-sequestration site in Brooks, Alberta

Rotating Magnetometry For Terrestrial And Extraterrestrial Subsurface Explorations

Signaling and sensing with rotating magnet sources have both Terrestrial and Extraterrestrial applications. The dual spinning magnet unit presented in this paper is a simple, lightweight solution to help understand soil densities and locate water and ice pockets, for example, on Mars. Traditional magnetic telemetry systems that use energy-inefficient large induction coils and antennas as sources and receivers are not practical for extraterrestrial and remote field sensing applications. The recent proliferation of strong rare-earth permanent magnets and high-sensitivity magnetometers enables alternative magnetic telemetry system concepts with significantly more compact formats and lower energy consumption. There are also terrestrial applications, for example, subterranean objects such as underground infrastructure and unexploded ordnances (UXO) that are often unmapped and difficult to find on Earth. Current ground penetrating radar units are expensive, large, and heavy. The research presented explores the viability and possibility to develop a unit that will induce an oscillating magnetic field with controllable shape to reliably locate buried ferromagnetic and non-ferromagnetic objects while remaining lightweight and cost effective. A Dual Rotating Magnet (DRM) design is presented. Experiments and numerical simulations assess the system for terrestrial and extraterrestrial detection of: 1) differences in soil densities, 2) water and ice pockets at shallow depths in the subsurface, and 3) subterranean ferromagnetic and non-ferromagnetic objects of interest

Design of a training tool for improving the use of hand-held detectors in humanitarian demining

Purpose - The purpose of this paper is to introduce the design of a training tool intended to improve deminers' technique during close-in detection tasks. Design/methodology/approach - Following an introduction that highlights the impact of mines and improvised explosive devices (IEDs), and the importance of training for enhancing the safety and the efficiency of the deminers, this paper considers the utilization of a sensory tracking system to study the skill of the hand-held detector expert operators. With the compiled information, some critical performance variables can be extracted, assessed, and quantified, so that they can be used afterwards as reference values for the training task. In a second stage, the sensory tracking system is used for analysing the trainee skills. The experimentation phase aims to test the effectiveness of the elements that compose the sensory system to track the hand-held detector during the training sessions. Findings - The proposed training tool will be able to evaluate the deminers' efficiency during the scanning tasks and will provide important information for improving their competences. Originality/value - This paper highlights the need of introducing emerging technologies for enhancing the current training techniques for deminers and proposes a sensory tracking system that can be successfully utilised for evaluating trainees' performance with hand-held detectors. © Emerald Group Publishing Limited.The authors acknowledge funding from the European Community's Seventh Framework Programme (FP7/2007‐2013 TIRAMISU) under Grant Agreement No. 284747 and partial funding under Robocity2030 S‐0505/DPI‐0176 and FORTUNA A1/039883/11 (Agencia Española de Cooperación Internacional para el Desarrollo – AECID). Dr Roemi Fernández acknowledges support from CSIC under grant JAE‐DOC. Dr Héctor Montes acknowledges support from Universidad Tecnológica de Panamá and from CSIC under grant JAE‐DOC.Peer Reviewe

Design of a training tool for improving the use of hand-held detectors in humanitarian demining

Purpose – The purpose of this paper is to introduce the design of a training tool intended to improve deminers' technique during close-in detection tasks. Design/methodology/approach – Following an introduction that highlights the impact of mines and improvised explosive devices (IEDs), and the importance of training for enhancing the safety and the efficiency of the deminers, this paper considers the utilization of a sensory tracking system to study the skill of the hand-held detector expert operators. With the compiled information, some critical performance variables can be extracted, assessed, and quantified, so that they can be used afterwards as reference values for the training task. In a second stage, the sensory tracking system is used for analysing the trainee skills. The experimentation phase aims to test the effectiveness of the elements that compose the sensory system to track the hand-held detector during the training sessions. Findings – The proposed training tool will be able to evaluate the deminers' efficiency during the scanning tasks and will provide important information for improving their competences. Originality/value – This paper highlights the need of introducing emerging technologies for enhancing the current training techniques for deminers and proposes a sensory tracking system that can be successfully utilised for evaluating trainees' performance with hand-held detectors

Advanced Sensors for Real-Time Monitoring Applications

It is impossible to imagine the modern world without sensors, or without real-time information about almost everything—from local temperature to material composition and health parameters. We sense, measure, and process data and act accordingly all the time. In fact, real-time monitoring and information is key to a successful business, an assistant in life-saving decisions that healthcare professionals make, and a tool in research that could revolutionize the future. To ensure that sensors address the rapidly developing needs of various areas of our lives and activities, scientists, researchers, manufacturers, and end-users have established an efficient dialogue so that the newest technological achievements in all aspects of real-time sensing can be implemented for the benefit of the wider community. This book documents some of the results of such a dialogue and reports on advances in sensors and sensor systems for existing and emerging real-time monitoring applications