UXO target area identification with hidden Markov models

Abstract

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.