CHARACTERIZATION OF PAST METALLURGICAL RESIDUES USING GEOPHYSICAL IMAGING: A CASE STUDY OF DUFERCO SITE (BELGIUM)

Ancient metallurgical industries produced large amounts of residues which were typically
deposited in heaps or tailing ponds. They were derived from mineral processing and metallurgical treatments that were not as efficient as the extraction processes used nowadays. On one hand, metallurgical wastes could represent a potential source of pollution, being an environmental and sanitary threat even centuries after the end of industrial activities. On the other hand, these residues may still contain valuable ferrous materials, non-ferrous metals and other elements considered as critical raw materials. In this regard, remediation strategies can be improved by integrating the valorization of
metallurgical residues and potential resource recovery. To this purpose, it is crucial to have in-depth information about the composition of the metallurgical waste, and spatial information to identify and quantify volumes of these residues. Geophysical methods represent suitable non-invasive technologies for subsurface site characterization, imaging lateral and vertical variations in the physical properties of geological environments including anthropogenic deposits. Complemented with ground truth data from excavations, geophysical imagery can be quantitatively interpreted in terms of material(s) composition and zonation, volume(s) estimation, etc. In this contribution, we present the results of an integrated geophysical investigation carried out in a slag heap of the former iron and steel factory of Duferco, located in Belgium. First, we carried out a geophysical survey in the field with a 3D Electrical Resistivity Tomography (ERT) and Induced Polarization (IP) acquisition. Based on these results, we designed a targeted sampling, i.e., excavation and collection of samples at different locations and depths. In the laboratory, we measured ERT and IP in the samples and compared them with elemental chemical analyses. We investigated correlations between the laboratory data and identified different types of slags or ‘categories’, i.e., slags richer in Fe, Ca and Si. Then we used a probabilistic approach to classify or predict the categories in the whole domain of field acquisition (where no samples are available). To this
aim we used the ERT and IP field data co-located with the samples and the elevation at which these samples were taken. Overall, the combination of geophysical measurements in the field, targeted sampling, geophysical measurements in the laboratory and correlations with chemical analyses, may be promising to quantify the metallic content or materials of interest with a discriminating geophysical signature.