Classification of UXO and non-UXO from magnetic anomaly data: a case study on inversion of drone magnetic data from Rømø, Denmark

Abstract

Summary A test site containing 24 targets of various disarmed unexploded ordnance (UXO) and non-UXO items were placed on a beach on the island of Rømø (Denmark) in a 600 m x 100 m area. Scalar magnetic anomalies were measured at 3-5 m altitude using an uncrewed aerial vehicle (UAV), towing a bird with a three-sensor triangular configuration to achieve a dense coverage with flight lines of 2 m spacing. The triple-sensor dataset is utilized in a probabilistic inversion setup to infer the magnetic moments of the 24 targets. The purpose of the study, is to try and distinguish between different types of ferromagnetic objects (UXO, non-UXO) using magnetic anomaly data. The inversion methodology uses different forward models (prolate spheroids, rectangular prisms) to infer target shape, size and orientation in an attempt to discriminate between UXO and non-UXO items. Stochastic inversions are carried out using different prior assumptions of remanent magnetization strength ($10\%$, $50\%$ and $80\%)$ of the induced dipole moment. Among the three levels of remanent magnetization strength in the prior, only some cases of discrimination seem evident for the lowest strength of remanence. One item is correctly classified as a true-negative (i.e. non-UXO) when assuming low remanent magnetization strength ($10\%)$ of the induced moment). However, at low remanent strength, one false-negative classification emerges, making any discrimination unreliable when assuming such low remanent magnetization. In addition to the discrimination study, different covariance models are utilized to optimize the inversion by addressing correlated errors and noise in the triple-sensor data set. Three covariance models are tested to try and account for spatially correlated noise and potential errors among the three sensors of each overflight. In many cases, the covariance models presented show a potential increase in sampling efficiency and consistency between data and the noise model, suggesting a more robust approach to a noise model in magnetic anomaly inversions. If the noise model is poor, however, it may bias the results by addressing the anomaly signal as noise. The inversions with correlated noise models are compared with inversions using a simple uncorrelated noise model. For several cases of data anomalies, differences between the inversion estimates when using correlated and uncorrelated noise models were evident, indicating that some bias may appear when assuming uncorrelated noise. Due to the general high presence of correlated signals in magnetic survey data, correlated noise models can significantly improve the overall uncertainty estimate of the estimated dipole moment. The study demonstrates, in terms of the 24 targets considered, that discrimination between UXO and non-UXO using magnetics is difficult. However, when using scalar magnetic data of high quality and resolution, the estimated dipole moments are often well resolved and uniquely defined in magnitude and position. This could provide valuable posterior information for future inversion studies by building a library of inferred magnetic moments from targets that have been found and inspected.