Advanced Geophysical Classification of WWII-era Unexploded Bombs Using Borehole Electromagnetics

The legacy of World War II-era unexploded bombs (UXB) is an ongoing public safety hazard throughout Europe, and especially in Germany. Large, air-dropped bombs that are a legacy of Allied bombing campaigns are discovered on a weekly basis in Germany, requiring evacuations and disposal efforts costing hundreds of thousands of Euros in some instances. This article presents recent work done by Black Tusk Geophysics using advanced geophysical classification (AGC) to reliably identify hazardous ordnance at urban sites in Germany. After briefly describing electromagnetic (EM) sensors and data processing required for AGC, this article will discuss survey and design considerations for characterization of large, deep UXBs in urban environments

PPE Development and Needs in HMA

As written in the International Mine Action Standards (IMAS) 10.30 on personal protective equipment (PPE), “the primary means of preventing explosive injury in the workplace is by the supervised use of demining tools and processes that reduce the likelihood of an unintended detonation.” The IMAS goes on to state that PPE “should be the final protective measure after all planning, training and procedural efforts to reduce risk have been taken.” To date the “final protective measure” has been to provide PPE that is practical but that does not provide full protection

Localisation of buried ferromagnetic objects based on minimum-norm-estimations: a simulation study

Purpose – The purpose of this paper is to examine the localisation of ferromagnetic objects buried in the underground. More specifically, it deals with the reconstruction of the XY-positions, the depths (Z-positions), the number, and the extension of the objects based on geomagnetic measurements. This paper introduces a minimum-norm reconstruction approach and evaluates its performance in a simulation study. Design/methodology/approach – Aminimum-L2-norm estimation based on the truncated singular value decomposition method with lead field weighting is proposed in order to localise geomagnetic sources. The sensor setup and positions are taken from real measurements. The source space is formed by an automatically generated grid. At each grid point, a magneto-static dipole is assumed. Findings – Sources with different depths and XY-positions could be successfully reconstructed. The proposed approach is not overly sensitive to errors/noise in measurement values and sensor positions. Originality/value – The approach described in this paper can be used for applications like geoprospection, archaeology, mine clearing, and the clean-up of former waste deposits

Detection and Characterisation of Conductive Objects Using Electromagnetic Induction and a Fluxgate Magnetometer

Eddy currents induced in electrically conductive objects can be used to locate metallic objects as well as to assess the properties of materials non-destructively without physical contact. This technique is useful for material identification, such as measuring conductivity and for discriminating whether a sample is magnetic or non-magnetic. In this study, we carried out experiments and numerical simulations for the evaluation of conductive objects. We investigated the frequency dependence of the secondary magnetic field generated by induced eddy currents when a conductive object is placed in a primary oscillating magnetic field. According to electromagnetic theory, conductive objects have different responses at different frequencies. Using a table-top setup consisting of a fluxgate magnetometer and a primary coil generating a magnetic field with frequency up to 1 kHz, we were able to detect aluminium and steel cylinders using the principle of electromagnetic induction. The experimental results were compared to numerical simulations, with good overall agreement. This technique enables the identification and characterisation of objects using their electrical conductivity and magnetic permeability

The detection problem: an eight-decade challenge: the difficulty of practically detecting and discriminating mines, booby traps, and victim operated improvised explosive devices

© The AuthorsReliably detecting and discriminating mines, booby traps, and victim operated improvised explosive devices remains a stubborn problem for both humanitarian demining organizations and the military. Since mines were widely used during the Second World War, much effort has been expended on the detection problem, with limited success. The aim of being able to positively identify a device first time remains elusive since the scientific challenge of positively identifying different substances in the ground is formidable. This article critically examines the detection problem and suggests that in the continued absence of a ‘silver bullet’ technological solution, the best means currently available to manage the risk of concealed explosive devices is the systematic collection and analysis of relevant operational data from the field

The Journal of Conventional Weapons Destruction Issue 22.1 (2017)

Editorial: The Evolution of PPE in HMA Feature: BAC in Urban Areas in the Spotlight: Europe Field Notes Research and Development Field Note

Novel AI-assisted computational solutions for GPR data interpretation and electromagnetic data fusion to detect buried utilities

This research presents a number of novel computational solutions using artificial intelligence (AI) to interpret ground penetrating radar (GPR) data as well as fusing GPR data with data from other sensing modalities, including electromagnetic conductivity (EMC) and electromagnetic locating (EML). The application of the proposed computational solution is predominantly for detecting and locating buried utilities (e.g. pipes and cables) and ground anomalies (e.g. ground disturbances) in the shallow subsurface environment although the work can be extended to detect other buried anomalies. Processing GPR data is usually a subjective and time-consuming practise which involves expert intervention. Thus, the quality of the interpretation of such data depends on user experience and knowledge. Whilst several numerical approaches are available in the literature for post-processing GPR data, they all suffer from various shortcomings including lack of accuracy and/or excessive computational time. The issue is similar (or often worse) for data fusion between GPR and other sensors e.g. EMC and EML. To tackle some of these issues, in this research, four new computational procedures were developed. Three of these computational procedures are based on Kalman Filtering (KF), a less-studied approach to process GPR radargrams despite its great potential in efficient data analysis, and genetic algorithm (GA) as a machine learning based global optimisation tool. The final computational procedure combines finite element modelling and genetic algorithm to infer fused EML-GPR data. For the first two numerical methods, new algorithms were developed to optimise KF parameters using GA to remove noises from GPR radargrams and detect targets. The proposed procedures were validated against data from field and their performance was assessed against additional unseen dataset different to that of the validation to identify their potential limitations. Furthermore, their performances were compared against existing GPR data processing methods and differences were highlighted. The other two computational packages focused on data fusion from GPR and EMC/EML. The first of these two, extended the above KF algorithm to fuse data from GPR and EML as well as GPR and EMC. The results showed that the proposed data fusion algorithm significantly enhanced the quality of locating conductors and conductive regions in the subsurface compared to the individual techniques which were either incapable of defining the material of the buried target or the geometry of conductive anomalies. Finally, a novel inversion algorithm was developed by integrating finite element modelling of a coupled magnetic field and GA for detecting and locating buried live cables using GPR and EML. It was demonstrated that the proposed inversion can successfully detect the location of the buried cables as well as their intensity