Unexploded Ordnance

Magnetic soils can seriously hamper the performance of electromagnetic sensors for the detection of buried land mines and unexploded ordnance (UXO). Soils formed on basaltic substrates commonly have large concentrations of ferrimagnetic iron oxide minerals, which are the main cause of soil magnetic behavior. Previous work has shown that viscous remanent magnetism (VRM) in particular, which is caused by the presence of ferrimagnetic minerals of different sizes and shapes, poses a large problem for electromagnetic surveys. The causes of the variability in magnetic soil properties in general and VRM in particular are not well understood. In this paper we present the results of laboratory studies of soil magnetic properties on three Hawaiian Islands: O"ahu, Kaho"olawe, and Hawaii. The data show a strong negative correlation between mean annual precipitation and induced magnetization, and a positive correlation between mean annual precipitation and the frequency dependent magnetic behavior. Soil erosion, which reduces the thickness of the soil cover, also influences the magnetic properties.

In a study at a military range with the objective to discriminate potentially hazardous [Formula: see text] mortars from nonhazardous shrapnel, range, and cultural debris, six different discrimination techniques were tested using data from an array of magnetometers, a time-domain electromagnetic induction (EMI) cart, an array of time-domain sensors, and a time-domain EMI cart with a wider measurement bandwidth. Discrimination was achieved using rule-based or statistical classification of feature vectors extracted from dipole or polarization tensor models fit to detected anomalies. For magnetics, the ranking by moment yielded better discrimination results than that of apparent remanence from relatively large remanent magnetizations of several of the seeded items. The magnetometer results produced very accurate depths and fewer failed fits attributable to noisy data or model insuffi-ciency. The EMI-based methods were more effective than the magnetometer for intrinsic discrimination ab...

The repurposing of military lands is common in many parts of the world
and presents a variety of conservation opportunities. This study examines
land cover at Big Oaks National Wildlife Refuge, Indiana (U.S.A.) as it
transitioned from military proving ground to wildlife refuge from 1985 to
2013. We use remote sensing, semi-structured interviews, and a review of
planning and management documents to examine this transition.
Limited change in land cover composition and distribution are detected,
despite changes in use and management. This landscape similarity
relates to similarities in land management practices, and the impact of
landscape history on current management practices. The findings suggest
that military use and conservation objectives at this site yield similar
land covers and are not necessarily in contrast to each other. As military
base closures continue, the potential to maintain and expand conservation
opportunities on these lands will likely grow in importance.

Undersea deposition of unexploded ordnance (UXO) constitutes a potential source of contamination of marine environments by hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) and octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX). Using sediment from a coastal UXO field, Oahu Island, Hawaii, we isolated four novel aerobic RDX-degrading fungi HAW-OCF1, HAW-OCF2, HAW-OCF3 and HAW-OCF5, tentatively identified as members of Rhodotorula, Bullera, Acremonium and Penicillium, respectively. The four isolates mineralized 15–34% of RDX in 58 days as determined by liberated 14CO2. Subsequently we selected Acremonium to determine biotransformation pathway(s) of RDX in more details. When RDX (100 μM) was incubated with resting cells of Acremonium we detected methylenedinitramine (MEDINA), N2O and HCHO. Also we detected hexahydro-1-nitroso-3,5-dinitro-1,3,5-triazine (MNX) together with trace amounts of hexahydro-1,3-dinitroso-5-nitro-1,3,5-triazine (DNX) and hexahydro-1,3,5-trinitroso-1,3,5-triazine (TNX). Under the same conditions MNX produced N2O and HCHO together with trace amounts of DNX and TNX, but we were unable to detect MEDINA. TNX did not degrade with Acremonium. These experimental findings suggested that RDX degraded via at least two major initial routes; one route involved direct ring cleavage to MEDINA and another involved reduction to MNX prior to ring cleavage. Nitrite was only detected in trace amounts suggesting that degradation via initial denitration did take place but not significantly. Aerobic incubation of Acremonium in sediment contaminated with RDX led to enhanced removal of the nitramine.

This paper addresses a particular detection problem related to the largest freshwater lake of Central and Western Europe, namely the Lake Balaton. The bed-silt of this shallow water lake (its average depth is 3.3 meters) contains several type of objects: industrial debris, historical vestiges, and in particular, an unknown quantity of unexploded ordnance from the second World War. It is important to localize and classify these objects for precise risk assessment and eventual later removal (not addressed in the paper). The bed-silt of the lake can be characterized as a mud having a light and constantly changing structure. It follows that the shallow water is almost always mixed with the mud. This admixture is constantly maintained by winds and navigating boats, hence the underwater visibility is close to zero during the most part of the year, especially in the depth range close to the bottom. Since the climate makes it possible, the authors propose a special way to explore the bed-silt of the lake, namely the use an autonomous vehicle on the frozen lake. Note that the ice prevents the wind and navigating boats to generate water movement and thus the mud is sedimented. This autonomous vehicle is equipped with a GPS based onboard localization system, with multiple sensor and recording equipment, and with a radio link to its command post. More details about the nature of objects to be detected as well as about the architecture of the detection and localization system are presented in the paper.

Recent developments in airborne magnetic detection systems have made it possible to detect small ferro-metallic objects. However, airborne magnetic data can be really large and, therefore, there is an increasing need for a fully automatic interpretation technique that could be used to make decisions regarding the nature of the sources in the field in real time. The massively parallel processing advantage of artificial neural networks makes them suitable for hardware implementations; therefore, using these networks in conjunction with a magnetic system has the potential to greatly speed up the detection of ferro-metallic objects. In this paper, we have developed a new method for detection and characterization of unexploded ordnance (UXO) using a Hopfield neural network as applied to airborne magnetic data. The Hopfield network is used to optimize the magnetic moment of a dipole source representing the magnetic object at regular locations. For each location, the Hopfield network reaches its stable energy state. The location of the object corresponds to the location yielding the minimum Hopfield energy. Output results include position in two dimensions (horizontal location and depth), magnetic moment, and effective inclination. Theoretical and actual field examples show that the Hopfield network is accurate and objective tool for the detection of UXO. Moreover, because the Hopfield neural network is a natural analog-to-digital converter, it is ideally suited for incorporation into airborne magnetic instrumentation systems.

Recent developments in airborne magnetic detection systems have made it possible to detect small ferro-metallic objects. However, airborne magnetic data can be really large and, therefore, there is an increasing need for a fully automatic interpretation technique that could be used to make decisions regarding the nature of the sources in the field in real time. The massively parallel processing advantage of artificial neural networks makes them suitable for hardware implementations; therefore, using these networks in conjunction with a magnetic system has the potential to greatly speed up the detection of ferro-metallic objects. In this paper, we have developed a new method for detection and characterization of unexploded ordnance (UXO) using a Hopfield neural network as applied to airborne magnetic data. The Hopfield network is used to optimize the magnetic moment of a dipole source representing the magnetic object at regular locations. For each location, the Hopfield network reaches its stable energy state. The location of the object corresponds to the location yielding the minimum Hopfield energy. Output results include position in two dimensions (horizontal location and depth), magnetic moment, and effective inclination. Theoretical and actual field examples show that the Hopfield network is accurate and objective tool for the detection of UXO. Moreover, because the Hopfield neural network is a natural analog-to-digital converter, it is ideally suited for incorporation into airborne magnetic instrumentation systems.

Over the past decade, notable progress has been made in the performance of airborne geophysical systems for mapping and detection of unexploded ordnance in terrestrial and shallow marine environments. For magnetometer systems, the most significant improvements include development of denser magnetometer arrays and vertical gradiometer configurations. In prototype analyses and recent Environmental Security Technology Certification Program (ESTCP) assessments using new production systems the greatest sensitivity has been achieved with a vertical gradiometer configuration, despite model-based survey design results which suggest that dense total-field arrays would be superior. As effective as magnetometer systems have proven to be at many sites, they are inadequate at sites where basalts and other ferrous geologic formations or soils produce anomalies that approach or exceed those of target ordnance items. Additionally, magnetometer systems are ineffective where detection of non-ferrous ordnance items is of primary concern. Recent completion of the Battelle TEM-8 airborne time-domain electromagnetic system represents the culmination of nearly nine years of assessment and development of airborne electromagnetic systems for UXO mapping and detection. A recent ESTCP demonstration of this system in New Mexico showed that it was able to detect 99% of blind-seeded ordnance items, 81mm and larger, and that it could be used to map in detail a bombing target on a basalt flow where previous airborne magnetometer surveys had failed. The probability of detection for the TEM-8 in the blind-seeded study area was better than that reported for a dense-array total-field magnetometer demonstration of the same blind-seeded site, and the TEM-8 system successfully detected these items with less than half as many anomaly picks as the dense-array total-field magnetometer system.