Location and characterisation of magnetic objects from measured magnetic data has long been a research interest with the difficulty in handling the non-uniqueness in the inversion process. Ground-surface methods, which are widely used for objects buried in shallow depthes, become ineffective for those sinking into deep soil, because the anomaly field diminishes rapidly by distance and is heavily interfered by the metal debris distributed in the ground surface. A total-field borehole magnetometer can penetrate to such depths and collect relatively quiet data. However, conventional interpretation techniques suffer from the inherent non-uniqueness in the borehole dimension. In this paper, a constrained optimisation method is utilised for the interpretation of total-field borehole data for the detection of deeply buried objects. The major advantage over conventional techniques comes from the analytically-derived constraints imposed on the parameter vector by excluding the non-geosensible results from consideration and hence reducing the non-uniqueness to a minimal level. A test site has been developed for the evaluation using real world data. The interpretation results demonstrate its superior capability in handling real-world problems with high non-uniqueness. Furthermore, this method provides a way to estimate the moment strength without knowing the exact position. Together with the modelled signature data for different objects, characterisation of a particular item is possible from the inversion of a single total-field borehole profile.
It is estimated that 10% of war-time bombs did not explode and can be found at the ground surface or buried at a depth of up to 8 meters depending on the formation of the soil. These unexploded bombs or ordnance (UXO) pose a real danger to construction workers and properties. Ground surface based methods become ineffective for objects sinking into deep places due to rapidly diminishing anomalous field and interfering metal debris distributed over ground surface.
To overcome the difficulties, a unique inversion algorithm is proposed in this work with advantages of fast convergence and maximization of information extracted from individual hole measurement. It is more reliable than traditional methods by examining the possibilities within a number of estimations. The information from individual hole measurement is fully interpreted hence suggestion can be made for the positioning of next drilling in order to minimize the number of holes required for clearance. Based upon the recovered information, a comparison method is proposed for the identification and discrimination of UXO items from other objects that may be found in the environment, such as steel pipes and steel barrels. It is not sensitive to the interference in the data once the dipole moment is recovered. The results from a test site demonstrates its supreme capability to deal with real-world inversion problems having small number of available data points.