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

An evaluation of the performance of multi-static handheld ground penetrating radar using full wave inversion for landmine detection

This thesis presents an empirical study comparing the ability of multi-static and bi-static, handheld, ground penetrating radar (GPR) systems, using full wave inversion (FWI), to determine the properties of buried anti-personnel (AP) landmines. A major problem associated with humanitarian demining is the occurrence of many false positives during clearance operations. Therefore, a reduction of the false alarm rate (FAR) and/or increasing the probability of detection (POD) is a key research and technical objective. Sensor fusion has emerged as a technique that promises to significantly enhance landmine detection. This study considers a handheld, combined metal detector (MD) and GPR device, and quantifies the advantages of the use of antenna arrays. During demining operations with such systems, possible targets are detected using the MD and further categorised using the GPR, possibly excluding false positives. A system using FWI imaging techniques to estimate the subsurface parameters is considered in this work.A previous study of multi-static GPR FWI used simplistic, 2D far-field propagation models, despite the targets being 3D and within the near field. This novel study uses full 3D electromagnetic (EM) wave simulation of the antenna arrays and propagation through the air and ground. Full EM simulation allows the sensitivity of radio measurements to landmine characteristics to be determined. The number and configuration of antenna elements are very important and must be optimised, contrary to the 2D sensitivity studies in (Watson, Lionheart 2014, Watson 2016) which conclude that the degree (number of elements) of the multi-static system is not critical. A novel sensitivity analysis for tilted handheld GPR antennas is used to demonstrate the positive impact of tilted antenna orientation on detection performance. A time domain GPR and A-scan data, consistent with a commercial handheld system, the MINEHOUND, is used throughout the simulated experiments which are based on synthetic GPR measurements.Finally, this thesis introduces a novel method of optimising the FWI solution through feature extraction or estimation of the internal air void typically present in pressure activated mines, to distinguish mines from non-mine targets and reduce the incidence of false positives

Ground‐Penetrating Radar for Close‐in Mine Detection

In this chapter, two of the major challenges in the application of ground‐penetrating radar in humanitarian demining operations are addressed: (i) development and testing of affordable and practical ground penetrating radar (GPR)‐based systems, which can be used off‐ground and (ii) development of robust signal processing techniques for landmines detection and identification. Different approaches developed at the Royal Military Academy in order to demonstrate the possibility of enhancing close‐range landmine detection and identification using ground‐penetrating radar under laboratory and outdoor conditions are summarized here. Data acquired using different affordable and practical GPR‐based systems are used to validate a number of promising developments in signal processing techniques for target detection and identification. The proposed approaches have been validated with success in laboratory and outdoor conditions and for different scenarios, including antipersonnel, low‐metal content landmines, improvised explosive devices and real mine‐affected soils

Electromagnetic imaging and deep learning for transition to renewable energies: a technology review

Electromagnetic imaging is a technique that has been employed and perfected to investigate the Earth subsurface over the past three decades. Besides the traditional geophysical surveys (e.g., hydrocarbon exploration, geological mapping), several new applications have appeared (e.g., characterization of geothermal energy reservoirs, capture and storage of carbon dioxide, water prospecting, and monitoring of hazardous-waste deposits). The development of new numerical schemes, algorithms, and easy access to supercomputers have supported innovation throughout the geo-electromagnetic community. In particular, deep learning solutions have taken electromagnetic imaging technology to a different level. These emerging deep learning tools have significantly contributed to data processing for enhanced electromagnetic imaging of the Earth. Herein, we review innovative electromagnetic imaging technologies and deep learning solutions and their role in better understanding useful resources for the energy transition path. To better understand this landscape, we describe the physics behind electromagnetic imaging, current trends in its numerical modeling, development of computational tools (traditional approaches and emerging deep learning schemes), and discuss some key applications for the energy transition. We focus on the need to explore all the alternatives of technologies and expertise transfer to propel the energy landscape forward. We hope this review may be useful for the entire geo-electromagnetic community and inspire and drive the further development of innovative electromagnetic imaging technologies to power a safer future based on energy sources.This work was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreements No. 955606 (DEEP-SEA) and No. 777778 (MATHROCKS). Furthermore, the research leading of this study has received funding from the Ministerio de Educación y Ciencia (Spain) under Project TED2021-131882B-C42.Peer ReviewedPostprint (published version

Statistical and adaptive signal processing for UXO discrimination for next-generation sensor data

Abstract-Until recently, detection algorithms could not reliably distinguish between buried UXO and clutter, leading to many false alarms. Over the last several years modern geophysical techniques have been developed that merge more sophisticated sensors, underlying physical models, statistical signal processing algorithms, and adaptive training techniques. These new approaches have dramatically reduced false alarm rates, although for the most part they have been applied to data collected at sites with relatively benign topology and anomaly densities. On more challenging sites, performance of even these more modern discrimination approaches is still quite poor. As a result, efforts are underway to develop a new generation of UXO sensors that will produce data streams of multi-axis vector or gradiometric measurements, for which optimal processing has not yet been carefully considered or developed. We describe a research program to address this processing gap, employing a synergistic use of advanced phenomenologicalmodeling and signal-processing algorithms. The key foci of the program are (1) development of new physics-based signal processing approaches applicable to the problem in which vector data is available from such sensors; and (2) development of the theory of optimal experiments to guide the optimal design and deployment of the new sensor modalities. Here, we present initial results using simulated data obtained with our phenomenological models that indicate that optimal processing of features extracted from multi-axis EMI data can provide substantial improvements in discrimination performance over processing of features extracted from single-axis data

Detecting and Classifying UXO

This article presents state-of-the-art unexploded ordnance detection and classification, including examples from recent field-demonstration studies. After reviewing sensor technologies, with a focus on magnetic and electromagnetic systems, the authors discuss advanced processing techniques that allow for reliable discrimination between hazardous ordnance and harmless metallic clutter. Finally, the article shows results from a large-scale field demonstration conducted in 2011. In this case study, electromagnetic data acquired with an advanced sensor is used to identify ordnance at the site, reducing the number of excavations required with conventional metal detectors by 85%

Catholic Relief Services Develops MRE Materials

This article presents state-of-the-art unexploded ordnance detection and classification, including examples from recent field-demonstration studies. After reviewing sensor technologies, with a focus on magnetic and electromagnetic systems, the authors discuss advanced processing techniques that allow for reliable discrimination between hazardous ordnance and harmless metallic clutter. Finally, the article shows results from a large-scale field demonstration conducted in 2011. In this case study, electromagnetic data acquired with an advanced sensor is used to identify ordnance at the site, reducing the number of excavations required with conventional metal detectors by 85%

Application Of Antenna Synthesis And Digital Signal Processing Techniques For Active Millimeter-wave Imaging Systems

Millimeter-wave imaging has gathered attention in recent years for its ability to penetrate clothing, thin layers of soils, and certain construction materials. However, image quality remains a challenge that needs to be addressed. One way of improving image quality is by increasing the dimensions of the collecting aperture. A sparse array can be used to synthesize a larger aperture with a limited set of relatively small detectors. In this research we design, build, and test a test-bed having an active source at 94 GHz and an array of coherent detectors, mounted on arms that extend radially on a rotary table. Using this test bed a circular area with a maximum diameter of 900 mm can be scanned. The signal is down-converted using heterodyne receivers with digital in-phase and quadrature detection. Signal correlation is performed using the digitized data, which is stored for post-processing, electronic focusing, and image reconstruction. Near-field imaging using interferometric reconstructions is achieved using electronic focusing. Imaging tests show the ability of the system to generate imagery of concealed and unconcealed objects at distances between 400 and 700 mm. A study of the effects of redundant and nonredundant configurations on image quality for 4 common detector configurations is presented. In this document we show that an active sparse-aperture imaging system using digital correlators is a viable way to generate millimeter-wave images

Application of Polarimetric GPR to detection of subsurface objects

Tohoku University佐藤源之課