Unexploded Ordnance Site Research Articles

GPR characterization of a lacustrine UXO site

We used [Formula: see text] ground-penetrating-radar (GPR) reflection profiles to determine if high-explosive (HE) depth bombs were possibly detonated within a small New England lake formerly used as a bombing range, as well as the depths and distribution of inert and possibly unexploded ordnance (UXO) within the subbottom sediments. Maximum water depth was [Formula: see text]. The primary munitions were unarmed practice bombs, but terrestrial fragments from HE depth bombs led us to seek stratigraphic obliteration and distortion as evidence of their use. The low water conductivity and quartz-rich sediments provided more than [Formula: see text] of subbottom penetration beneath slope and shelf sediments. The high permittivities of the water and subbottom sediments provided good antenna directivity, excellent 2D migrations, multicentimeter vertical resolution, and a unique pulse waveform signature for metal objects. Our line profiles revealed hundreds of subbottom diffractions, which implied an areal distribution of thou-sands of suspicious objects. Most lay beneath the central basin, but many resided beneath the slopes and shelves as well. Supplementary profiles of a nearby pristine lake and of controlled munitions, a side-scan sonar bottom survey, and time-domain reflectometry dielectric measurements of sediment samples corroborated our findings of natural stratigraphy, a unique metallic diffraction phase signature, the existence of many nonmetallic objects including logs, and subbottom relative permittivities ranging from 28 to 76. The intact natural lacustrine strata, including deltaic foresets and bottom sets, faults, and conformable and unconformable horizons, suggest that live bombs likely did not detonate in the lake. The only stratigraphic disturbances we found were apparent perforations beneath about [Formula: see text] of water, within which diffractions of metallic origin originated. The dielectric results and the prominent slope strata over the deepest horizons led us to conclude that the exceptional subbottom penetration existed under predominantly sandy sediments.

Detection of Unexploded Ordnance via Efficient Semisupervised and Active Learning

Semi supervised learning and active learning are considered for unexploded ordnance (UXO) detection. Semi supervised learning algorithms are designed using both labeled and unlabeled data, where here labeled data correspond to sensor signatures for which the identity of the buried item (UXO/non-UXO) is known; for unlabeled data, one only has access to the corresponding sensor data. Active learning is used to define which unlabeled signatures would be most informative to improve the classifier design if the associated label could be acquired (where for UXO sensing, the label is acquired by excavation). A graph-based semi supervised algorithm is applied, which employs the idea of a random Markov walk on a graph, thereby exploiting knowledge of the data manifold (where the manifold is defined by both the labeled and unlabeled data). The algorithm is used to infer labels for the unlabeled data, providing a probability that a given unlabeled signature corresponds to a buried UXO. An efficient active-learning procedure is developed for this algorithm, based on a mutual information measure. In this manner, one initially performs excavation with the purpose of acquiring labels to improve the classifier, and once this active-learning phase is completed, the resulting semi supervised classifier is then applied to the remaining unlabeled signatures to quantify the probability that each such item is a UXO. Example classification results are presented for an actual UXO site, based on electromagnetic induction and magnetometer data. Performance is assessed in comparison to other semi supervised approaches, as well as to supervised algorithms.

Multiscale structure of UXO site characterization: spatial estimation and uncertainty quantification

Unexploded ordnance (UXO) site characterization must consider both how the contamination is generated and how we observe that contamination. Within the generation and observation processes, dependence structures can be exploited at multiple scales. We describe a conceptual site characterization process, the dependence structures available at several scales, and consider their statistical estimation aspects. It is evident that most of the statistical methods that are needed to address the estimation problems are known but their application-specific implementation may not be available. We demonstrate estimation at one scale and propose a representation for site contamination intensity that takes full account of uncertainty, is flexible enough to answer regulatory requirements, and is a practical tool for managing detailed spatial site characterization and remediation. The representation is based on point process spatial estimation methods that require modern computational resources for practical application. These methods have provisions for including prior and covariate information.

UXO target area identification with hidden Markov models

Site characterization activities at potential unexploded ordnance (UXO) sites rely on sparse sampling collected as geophysical surveys along strip transects. From these samples, the locations of target areas, those regions on the site where the geophysical anomaly density is significantly above the background density, must be identified. A target area detection approach using a hidden Markov model (HMM) is developed here. HMM’s use stationary transition probabilities from one state to another for steps between adjacent locations as well as the probability of any particular observation occurring given each possible underlying state. The approach developed here identifies the transition probabilities directly from the conceptual site model (CSM) created as part of the UXO site characterization process. A series of simulations examine the ability of the HMM approach to simultaneously determine the target area locations within each transect and to estimate the unknown anomaly intensity within the identified target area. The HMM results are compared to those obtained using a simpler target detection approach that considers the background anomaly density to be defined by a Poisson distribution and each location to be independent of any adjacent location. Results show that the HMM approach is capable of accurately identifying the target locations with limited false positive identifications when both the background and target are intensities are known. The HMM approach is relatively robust to changes in the initial estimate of the target anomaly intensity and is capable of identifying target locations and the corresponding target anomaly intensity when this intensity is approximately 60% higher than the background intensity at intensities that are representative of actual field sites. Application to data collected from a wide area assessment field site show that the HMM approach identifies the area of the site with elevated anomaly intensity with few false positives. This field site application also shows that the HMM results are relatively robust to changes in the transect width.

A Bivariate Gaussian Model for Unexploded Ordnance Classification with EMI Data

A bivariate Gaussian model is proposed for modeling spatially varying electromagnetic-induction (EMI) response of unexploded ordnance (UXO). This model is proposed for EMI sensors that do not exploit enough physics to warrant using the popular magnetic-dipole model currently commonly used. These two competing models are applied to measured EM61 sensor data at a real UXO site. UXO classification performance using the proposed bivariate Gaussian model is shown to be superior to an approach employing the magnetic-dipole model. Moreover, the bivariate Gaussian model requires no labeled training data, obviates classifier construction, and has fewer model parameters to learn.

Optimizing decision threshold and weighting parameter for UXO discrimination

Establishing a decision threshold to separate unexploded ordnance (UXO) from clutter is a critical task in UXO discrimination procedures. It is, however, a difficult task because the decision threshold depends on many parameters such as local geology, distribution of UXO and clutter, and cultural and environmental noise at a given UXO cleanup site. Some discrimination algorithms also use site-dependent weighting factors. In practice, one determines these parameters empirically from calibration experiments. To facilitate this task, we introduce a simple function called the degree of discrimination, constructed from calibration plots at a UXO cleanup site. First, we compute the degree of discrimination for a representative set of thresholds and weighting factors using ground truth data from the calibration site. Then, we find the optimum decision threshold and weighting factors associated with the best degree of discrimination. Finally, we use these optimal parameters for the UXO site cleanup. We have conducted two experiments using the broadband electromagnetic (EM) sensor with a concentric coil configuration. Our experiments show that 92% of UXO is declared and 74% of clutter is rejected using our optimized threshold and weights. A blind test shows that optimizing the threshold and weights increases the degree of discrimination from 0 to 0.15. Our tests also show that the detection scheme with a collective threshold for all UXO types is easier to use and yields better discrimination than the scheme based on individual thresholds for each UXO type.

Accounting for geophysical information in geostatistical characterization of unexploded ordnance (UXO) sites

Efficient and reliable unexploded ordnance (UXO) site characterization is needed for decisions regarding future land use. There are several types of data available at UXO sites and geophysical signal maps are one of the most valuable sources of information. Incorporation of such information into site characterization requires a flexible and reliable methodology. Geostatistics allows one to account for exhaustive secondary information (i.e.,, known at every location within the field) in many different ways. Kriging and logistic regression were combined to map the probability of occurrence of at least one geophysical anomaly of interest, such as UXO, from a limited number of indicator data. Logistic regression is used to derive the trend from a geophysical signal map, and kriged residuals are added to the trend to estimate the probabilities of the presence of UXO at unsampled locations (simple kriging with varying local means or SKlm). Each location is identified for further remedial action if the estimated probability is greater than a given threshold. The technique is illustrated using a hypothetical UXO site generated by a UXO simulator, and a corresponding geophysical signal map. Indicator data are collected along two transects located within the site. Classification performances are then assessed by computing proportions of correct classification, false positive, false negative, and Kappa statistics. Two common approaches, one of which does not take any secondary information into account (ordinary indicator kriging) and a variant of common cokriging (collocated cokriging), were used for comparison purposes. Results indicate that accounting for exhaustive secondary information improves the overall characterization of UXO sites if an appropriate methodology, SKlm in this case, is used.

Comparison of Magnetic and Electromagnetic Anomalies Caused by Underground Structures

Magnetic and electromagnetic (EM) methods are perhaps the most convenient and popular geophysical survey methods for detecting buried manmade objects, which is due to their non‐intrusiveness, light field logistics, high survey speed, and the quality of information. One should always consider the two methods as the precursor to any geophysical survey. Often, the data resulting from the two methods are sufficient for characterizing buried objects.In this paper, we present magnetic and EM data collected at four sites: (1) Cloud Chamber at the Nevada Test Site, (2) Anacostia Metro Tunnels in Washington, D.C., (3) Cold Test Pit at Idaho National Engineering Laboratory, and (4) Unexploded ordnance site at Jefferson Proving Ground, Indiana. The first two sites may be considered typical underground facilities. The last two sites, however, contain small buried objects (storage tanks, ordnance, etc.) specifically prepared to test various geophysical methods for detection and, possibly, discrimination.We find throug...

Resolving Power of Some Geophysical Methods as Applied to Detecting Buried Unexploded Ordnance

Various geophysical sensors are employed for the purpose of detecting and clearing unexploded ordnance (UXO) items buried in the battlefield including inland, beachhead, surf‐zone, and the ocean. Similar sensors are also used for environmental investigation of ordnance and explosive waste (OEW) sites including bombing ranges, target practice areas, and explosive manufacturing facilities, as well as nuclear, biological, and chemical (NBC) waste sites. One of the most important issues is the concept of “standoff” detection so that the survey crew and their equipment are reasonably safe from accidentally setting off buried explosives or contacting hazardous materials. Despite the long history of geophysical sensors used at UXO sites, there exists a knowledge gap between geophysicists and the end‐users in understanding the applicability and limitations of the geophysical tools. I attempt in this article a heuristic review of classical geophysical methods from a perspective of their usefulness for standoff, non‐intrusive target detection. The most routinely used UXO sensors are magnetometers and metal detectors. A magnetometer is a passive sensor that measures the geomagnetic distortion caused by a nearby ferrous target. A metal detector actively broadcasts an electromagnetic field and receives the secondary field induced by conductive targets. Barring unforeseen developments, I advocate in this article that magnetic and electromagnetic methods are, and will be, perhaps the only acceptable and effective tools in the near future for detecting buried UXOs. A discussion follows on the resolving power of the magnetic and electromagnetic methods in terms of UXO sizes and standoff distances. This quantitative discussion provides a theoretical basis for determining whether a given survey platform and a specified modus operandi would detect and resolve a specific, buried UXO at a stipulated standoff distance. The relationship between the standoff distance and the required resolution also governs whether survey platforms may be ground‐based or airborne.