Analysis of EMI and Magnetic Data; Aberdeen Proving Ground

For classification problems where the test data are labeled sequentially, the point at which all true positives are first identified is often of critical importance. This article develops hypothesis tests to assess whether all true positives have been labeled in the test data. The tests use a partial receiver operating characteristic (ROC) that is generated from a labeled subset of the test data. These methods are developed in the context of unexploded ordnance (UXO) classification, but are applicable to any binary classification problem. First, the likelihood of the observed ROC given binormal model parameters is derived using order statistics, leading to a nonlinear parameter estimation problem. I then derive the approximate distribution of the point on the ROC at which all true instances are found. Using estimated binormal parameters, this distribution can be integrated up to a desired confidence level to define a critical false alarm rate (FAR). If the selected operating point is before this critical point, then additional labels out to the critical point are required. A second test uses the uncertainty in binormal parameters to determine the critical FAR. These tests are demonstrated with UXO classification examples and both approaches are recommended for testing operating points.

Sub-Audio Magnetics (SAM) is a method by which a total-field magnetic sensor may be used to simultaneously acquire both total magnetic intensity (TMI) and total-field electromagnetic induction (TFEMI) responses from buried metal. In situations where public safety is involved, regulators, such as the U.S. Environmental Protection Agency, can mandate the use of both magnetic and electromagnetic sensors for the detection of potentially hazardous items such as unexploded ordnance (UXO) and metal drums. SAM has now completed a program of evaluation and certification as a state-of-the-art technology, acceptable for application in the litigation-sensitive environmental hazard remediation market.During its certification program, SAM was applied to controlled U.S. test sites at the Aberdeen Proving Ground, Maryland, McKinley Range, Alabama, Waikoloa, Hawaii, and at the Newholme facility in Australia. It was also applied to situations in Montana where live UXO was present. In these demonstrations, a direct comparison was made between SAM results and common, state-of-the-art alternative geophysical detecting technologies. Based on demonstrations at these sites, it has been possible to quantify the key performance parameters: level of effort, detection performance as a function of depth and target size, false-alarm rejection achieved through dual-mode sensing, and the cost-benefit ratio of SAM compared with contemporary sensor technologies such as the Geonics EM61 and EM63 electromagnetic detectors, and G-tek’s TM-5EMU electromagnetic detector and TM-4 total field magnetometer.SAM has proven to be very successful in detecting a wide range of metallic UXO items to depths exceeding achievable detection by all contemporary sensors. In the Montana live sites, where the smallest item of UXO was a 76 mm projectile, false-alarm rates were reduced from 7 per UXO, using TMI only, to 0.7 per UXO using combined SAM TMI and TFEMI data, without diminishing the probability of UXO detection. Survey cost per hectare using SAM was determined to be less than one quarter that of conducting TMI plus EMI surveys separately.The application of SAM to extremely magnetic lava flows at Waikoloa in Hawaii showed that SAM cannot yet compete with the ground-balancing capability of the TM-5EMU in this environment. Advanced signal-processing development may reduce this present limitation.

The detection and classification of unexploded ordnance (UXO) is considered a multidimensional pattern recognition problem. Standard techniques in solving multidimensional detection and classification problems involve using large sets of templates or libraries. This paper shows that by using wavelet transformation a single library will allow a particular class of ordnance to be classified over a range of depths.

The fundamental problem in applying geophysical mapping to locate unexploded ordnance (UXO) is distinguishing true UXO from non-UXO. Enhancing the accuracy of UXO detection has multiple benefits, especially in the areas of cost savings and safety. We investigated discrimination approaches using both magnetic field data and numerically modeled data. Libraries of total field magnetic (TFM) responses were calculated using finite element modeling for three UXO types found at a Montana National Guard training site. UXO model parameters were varied over ranges of azimuth, declination, and depth resulting in approximately 600 models per UXO type. The modeled responses of finite-element model (FEM) and actual TFM field data were then used as training data in discrimination and classification approaches comparing neural networks (NN), random forests (RF), and support vector machines (SVMs). The prediction targets in the training process comprised three classes: 1) binary [UXO or noninteresting object (NIO)]; 2) multiclass (UXO round type and NIO); and 3) classes derived from multiclass self-organizing feature map (SOFM) analysis. The multiclass SOFM targets generated from site-specific field data were found to be optimal for UXO discrimination. The best performing combination of class selection types using recentered data for UXO detection rates of 100% resulted in a false alarm rate (FAR) of 28%.

Magnetic mapping for the detection and characterization of UXO: Use of multi-sensor fluxgate 3-axis magnetometers and methods of interpretation

A few promising solutions for thermal imaging Unexploded Ordnance (UXO) detection were proposed after the start of the military conflict in Ukraine in 2014. At the same time, most of the landmine clearance protocols and practices are based on old, 20th-century technologies. More than 60 countries worldwide are still affected by explosive remnants of war, and new areas are contaminated almost every day. To date, no automated solutions exist for surface UXO detection by using thermal imaging. One of the reasons is also that there are no publicly available data. This research bridges both gaps by introducing an automated UXO detection method, and by publishing thermal imaging data. During a project in Bosnia and Herzegovina in 2019, an organisation, Norwegian People’s Aid, collected data about unexploded ordnances and made them available for this research. Thermal images with a size of 720 × 480 pixels were collected by using an Unmanned Aerial Vehicle at a height of 3 m, thus achieving a very small Ground Sampling Distance (GSD). One of the goals of our research was also to verify if the explosive war remnants’ detection accuracy could be improved further by using Convolutional Neural Networks (CNN). We have experimented with various existing modern CNN architectures for object identification, whereat the YOLOv5 model was selected as the most promising for retraining. An eleven-class object detection problem was solved primarily in this study. Our data were annotated semi-manually. Five versions of the YOLOv5 model, fine-tuned with a grid-search, were trained end-to-end on randomly selected 640 training and 80 validation images from our dataset. The trained models were verified on the remaining 88 images from our dataset. Objects from each of the eleven classes were identified with more than 90% probability, whereat the Mean Average Precision (mAP) at a 0.5 threshold was 99.5%, and the mAP at thresholds from 0.5 to 0.95 was 87.0% up to 90.5%, depending on the model’s complexity. Our results are comparable to the state-of-the-art, whereat these object detection methods have been tested on other similar small datasets with thermal images. Our study is one of the few in the field of Automated UXO detection by using thermal images, and the first that solves the problem of identifying more than one class of objects. On the other hand, publicly available thermal images with a relatively small GSD will enable and stimulate the development of new detection algorithms, where our method and results can serve as a baseline. Only really accurate automatic UXO detection solutions will help to solve one of the least explored worldwide life-threatening problems.

Buried unexploded ordnance (UXO) and land mines are grave threats to civilians who go back to contaminated areas in the postwar period. In this research, we propose a new technique to detect UXOs from ground penetrating radar (GPR) signals. The technique is based on an energy-based feature of the region of interest (ROI). A ROI selection technique in range domain is also proposed. The proposed method was tested with the UXO data collected by Battelle company and the Ohio State University. This data set was made available to public through the Unexploded Ordnance Center of Excellence (UXOCOE), Department of Defense, U.S.A. The results evaluated in terms of Receiver Operating Characteristic (ROC) curves suggest that our proposed technique performs very well. Comparisons to a traditional detection technique and our previously proposed technique are conducted. The improvement by the proposed technique does not only speed up the UXO/land mine clearance operation, but also save operating cost.

Unexploded ordnance (UXO) investigation is mainly carried out by the magnetic exploration. However, in this paper, the transient electromagnetic (TEM) method was used in order to detect UXO more accurately. Although the TEM method is usually used for deep sounding of mineral exploration, we used small loop TEM method to detect buried UXO. In order to understand the characteristics of the small loop TEM method, many experiments using small empty cans were carried out at Ito Campus in Kyushu University and a lot of features were discovered. For example, if the size of loop becomes smaller, the penetration depth becomes smaller. These results showed that the small loop TEM was applicable to UXO investigation. Based on these results, the test survey to detect the imitation UXOs using 180×180-cm-square loop was carried out. In this field experiment, there are various kinds of imitation UXOs, but all of the UXOs were detected. As a result, it was found that UXO detection by small loop TEM method is effective compared to magnetic method. Moreover, the small loop TEM method could narrow the range of UXO location to the size of 30×30cm-square.

This paper summarizes the development of a wireless magnetoresistive (MR) sensor array for unexploded ordnance (UXO) detection realized with wireless sensor network (WSN) technology. In recent years, the rapid development and research on WSN has enabled the application of such technology in a variety of areas¿DThis paper discusses the WSN implementation in a UXO and landmine detection system. The overall system design, hardware design, and network design for large area field survey for a passive UXO detection is discussed. The manufactured wireless MR sensor node prototype is extremely compact, lightweight and portable. It also demonstrated excellent overall system performance in house testing. A pilot production of the sensor nodes is proposed for large scale field testing to further verify the effectiveness of the wireless MR sensor array for UXO and landmine detection.

: This report the findings and recommendations of a study, conducted for the Environmental Security Technology Certification Program (ESTCP) to identify potential barriers to the transfer of technologies from the research and development phase to active field use for munitions detection. To conduct a practical, focused assessment of barriers to technology transfer, the study focused on two specific emerging technologies: Wide Area Assessment (WAA) and unexploded ordnance (UXO) discrimination. This report discusses WAA; a parallel report on UXO discrimination is also available. The issue of UXO detection and remediation technology transfer barriers has been discussed for the last decade, perhaps most notably in the Final Report of the Defense Science Board Task Force on Unexploded Ordnance1. That report identified two changes that would bring about significant cost savings: use WAA to reduce the footprint of potential Munition Response Sites (MRSs) and use discrimination technologies to reduce the number of holes dug looking for munitions. Over the lifecycle of an environmental action starting with site investigation (SI) and working through remedial investigation/feasibility study (RI/FS) and remedial action (RA) towards site closure, WAA has applicability on the front end and UXO discrimination has applicability during the RA phases (see Figure 1). The need for cost-effective WAA methods and technologies is driven by the magnitude of the effort the Department of Defense (DoD) must undertake to clean up UXO on former and active DoD lands. The DoD must examine millions of acres of land potentially contaminated with UXO to identify areas that actually contain UXO for cleanup. On much of the acreage perhaps 80% or more there are no UXO present; this area is presumptively clean and should be accepted as needing No Further Action (NFA) by all stakeholders.

Summary During an unexploded ordnance (UXO) clean-up project, most of the items excavated are not UXO, but are harmless scrap metal. If these items can be determined to be non-hazardous without digging, the cost of a site clean-up can be cut by a substantial amount. Reliable classification of suspected UXO targets using geophysical survey data is now possible. This paper provides a summary of new advanced electromagnetic sensors and related software technologies to consistently classify suspected UXO and buried munitions vs. non-hazardous buried metallic objects. The technology and the tools have matured to the point where classification can be (and are) routinely applied to UXO clean-up projects in a transparent and reproducible approach. This methodology is now being accepted and required by US government program managers and regulators in clean-up projects that span a range of site conditions.

Over the past five years, our group has focused on development of airborne magnetic and electromagnetic systems for mapping and detection of unexploded ordnance (UXO). Among these projects is one involving testing and development of a high temperature airborne Superconducting Quantum Interference Device (SQUID) tensor magnetometer. In association with the airborne SQUID development, we are conducting tests of the SQUID as a ground‐based instrument for UXO mapping and detection. The ground‐based SQUID is mounted on a man‐portable litter with ancillary instrumentation mounted on a cart and connected by an umbilical. High‐temperature SQUIDs use liquid nitrogen as the cryogen, which is more convenient than the helium‐cooled low‐temperature devices. Preliminary data from the ground‐SQUID prototype show good agreement with cesium vapor total magnetic field data along a test line. Noise frequencies and levels appear manageable and we anticipate that specialized processing should make it possible to extract more details about UXO items from their tensor magnetic properties.

: The research described in this report was conducted in fulfillment of Project MM-1572, A Complex Approach to UXO Discrimination: Combining Advanced EMI Forward and Statistical Signal Processing, submitted to the Strategic Environmental Research and Development Program (SERDP) in response to the Munitions Management Statement of Need MMSON-07-04, Advanced Technologies for Detection, Discrimination, and Remediation of Munitions and Explosives of Concern (MEC): UXO Technology. The well-known and prohibitive cost of carefully excavating all geophysical anomalies detected at lands contaminated with unexploded ordnance (UXO) is one of the greatest impediments to performing an efficient and thorough cleanup of former battlefields and of Department of Defense (DoD) and Department of Energy (DOE) sites. Innovative discrimination techniques are required that can quickly and reliably distinguish between hazardous UXO and non-hazardous metallic items. The key to success lies in the development of advanced processing techniques that can analyze and process sophisticated magnetic or electromagnetic induction data, with its novel waveforms, ever improving quality, and vector or tensor character, so as to maximize the probability of correct classification and minimize the falsealarm rate.

The Environmental Management Division of Aberdeen Proving Ground (APG), Maryland, is conducting a remedial investigation and feasibility study of the J-Field area at APG pursuant to the Comprehensive Environmental Response, Compensation, and Liability Act, as amended (CERCLA). J-Field is located within the Edgewood Area of APG in Harford County, Maryland. Since World War II, activities in the Edgewood Area have included the development, manufacture, testing, and destruction of chemical agents and munitions. These materials were destroyed at J-Field by open burning/open detonation. Portions of J-Field continue to be used for the detonation and disposal of unexploded ordnance (UXO) by open burning/open detonation under authority of the Resource Conservation and Recovery Act.

<p>The nature of the Estonian crust was studied using global topography, magnetic data, and gravity data to estimate its tectonic regime. The Estonian Precambrian crystalline basement, composed of Paleo- to MesoProterozoic metamorphic and igneous rocks, is covered by a Paleozoic sedimentary rock deposit 100–780 m thick. To visualize crustal sources of the Estonian basement, we employed spectrum analysis of magnetic and gravity data, as well as two-dimensional (2D) forward modeling of gravity data. The gravimetric data was also evaluated to identify the depth of the Moho and Conrad discontinuities in Estonia. The magnetic data has also been evaluated to calculate the Curie point depth, which was then utilized to predict heat flow values inside the research zone. The subsurface of Estonia is divided into six petrological-structural zones: Tallinn, Alutaguse, Johvi, West-Estonian, Tapa and South-Estonian. To assess the structural variations of the crust at these locations, profiles of topographic, gravity, magnetic and heat flow data were constructed in each of the petrological-structural zones. The spectrum analysis and 2D gravity forward models yielded residual and regional gravity anomaly maps that show a significant amplitude potential maximum across the precambrian Rapakivi granitoid plutons and the Paldiski-Pskov tectonic zone. The Curie point depth reveals values ranging from 7 to 26 km, whereas the Moho depth suggests values ranging from 48 to 72 km and the Conrad depth values ranging from 14 to 20 km.</p>