Enhancing magnetic signals in unexploded ordnances (UXO) detection based on edge-preserved stable downward continuation method

This paper describes an efficient edge-preserved regularization algorithm for downward continuation of magnetic data in detecting unexploded ordnance (UXO). The magnetic anomalies arising from multi-source UXO can overlap at a height over the ground surface while causative sources may not be readily separated due to low level of signal-to-noise ratio of the observed data. To effectively work the magnetic method in the cleanup stage of contaminated area with UXO, the magnetic anomalies of UXO sources should be enhanced in order to separate the locations of different sources. The stable downward continuation of magnetic data can increase the signal-to-noise ratio, which subsequently causes the separation of UXO sources by enhancing the signals. In this study the researchers formulated the downward continuation as a linear ill-posed deconvolution problem. To obtain a reasonable downward continued field, the proposed filter is stabilized in a Fourier domain to regularize the downward solution using the edge-preserved (or total-variation) algorithm. The L-curve method was used to choose the optimum value of the regularization parameter, which is a trade-off between the misfit and the solution norms in the cost function of optimization problem. A synthetic magnetic field was constructed from isolated multi-source UXO anomalies, the results of which show that the data can be stably downward continued to the ground surface. Likewise, a field data set was provided to demonstrate the capability of the applied method in UXO detection. The results of the synthetic and real case study revealed that the observed magnetic anomalies at a specific height of survey over the ground surface have low amplitude, indeed, the causative UXO sources may not be readily distinguished in detection process, especially anomalies from small UXOs. It was shown that the continued data can enhance the locations of UXOs while small ones are not distinguishable in the primary data.