Automatic detection of UXO magnetic anomalies using extended Euler deconvolution

Abstract

We have developed an algorithm for the automatic detection of prospective unexploded ordnance (UXO) anomalies in total-field or gradient magnetic data based on the concept of the structural index (SI) of a magnetic anomaly. Identifying magnetic anomalies having specific structural indices enables the direct detection of potential UXO targets. The total magnetic field produced by a dipolelike source, such as a UXO, decays with inverse distance cubed and therefore has an SI of three, whereas the gradient data have an SI of four. The developed extended Euler deconvolution method based on the Hilbert transform provides a reliable means for calculating the spatial location, depth, and SI of compact and isolated anomalies; it has enabled us to perform automatic anomaly selection for further analysis. Our method first examines the anomaly decay and selects possible UXO anomalies based on the expected SI. We refine the result further by post-Euler amplitude analysis using the relative source strength of the anomalies selected in the first stage. The amplitude analysis statistically identifies weak anomalies that are due to noise in the data. This enhances the final result and eliminates automatic picks that fall within the noise level. We have demonstrated the effectiveness of the method using synthetic and field data sets.

Improving UXO Detection and Discrimination in Magnetic Environments

“…The poor target picking result is presented in Figure 5.15 by the single white '+'-mark at the lower left corner, demonstrating that none of the UXO targets are identified for this site. Such a result is not surprising, as Davis et al (2005) describes difficulties of the approach in identifying potential UXO targets in the presence of large regional magnetic anomalies. We have therefore modified the approach by applying the extended Euler deconvolution method to data which have undergone preprocessing, using both iterative Wiener and wavelet filters, to remove the geologic component of the data.…”

“…The iterative Wiener and wavelet filter have both shown value for such separation. As commented by Davis et al (2005) and observed within our field datasets, the presence of overlapping anomalies, either between UXO responses or between UXO and some geologic response, tend to degrade the Euler solutions and create false target picks. We add that dipolelike features in the magnetic data associated with geology likewise generate such false positives.…”

“…Euler deconvolution method of Nabighian and Hansen (2001), modified by Davis et al (2005) for automated target picking of UXO like targets. We have found that the method provides an efficient means for calculating the SI, and enables automatic anomaly detection based on computed SI.…”

“…The method is described in greater details in Davis et al (2005). We note that the workflow presented is only a component of the procedure we are developing here, which also incorporates pre-processing of magnetic data (when appropriate) using the iterative Wiener and wavelet filters described in the earlier parts of this chapter.…”

“…The goal of this component is to better understand and develop practical procedures, appropriate to a geologic setting, for integrating new filtering technologies with newly evolving methodologies for detecting UXO in magnetic environments. In this section, we briefly describe the Hilbert transform-based extended Euler deconvolution technique (Nabighian and Hansen, 2001), outline a workflow for applying the method to detection of potential UXO targets (Davis et al, 2005), and present results of the method applied to three magnetic datasets, filtered and unfiltered, collected at Chevalier Ranch, MT.…”