Advanced Geophysical Imaging of Landfills with ERT/TDIP Data: Full-Waveform Processing, Stochastic Inversion and Clustering Analysis

Municipal solid waste landfills are complex systems in which leachate accumulation can generate both environmental and geotechnical hazards. In this context, Electrical Resistivity Tomography (ERT) and Time-Domain Induced Polarization (TDIP) provide complementary sensitivity to pore-fluid conductivity and interfacial polarization processes, thus offering significant potential for non-invasive landfill monitoring. However, the practical use of these methods is still limited by three major issues: the poor quality of TDIP data, the lack of rigorous uncertainty quantification in local deterministic inversions, and the difficulty of integrating multiple geoelectrical outputs into a single, operationally meaningful interpretation. This thesis addresses these limitations through three complementary research lines. The first focuses on the improvement of TDIP data quality by means of fullwaveform acquisition and advanced signal processing specifically designed for 100% duty-cycle measurements, enabling a full-decay forward modelling and inversion. The second investigates uncertainty analysis through a global inversion framework for joint ERT/TDIP data, with the aim of moving beyond single deterministic models toward ensembles of plausible solutions and associated statistical indicators. The third develops machine-learning clustering approaches for the integrated interpretation of multiparameter tomographic models, progressively moving from hard clustering to target-oriented fuzzy mapping. Within the broader framework of environmental engineering, these developments are relevant because they contribute to the identification, delineation, and monitoring of leachate-related targets in complex landfill systems, thus supporting more informed site characterization and management decisions. Rather than proposing a single unified workflow from the outset, the thesis develops three distinct but converging methodological directions, providing both advances in applied geophysics and practical tools for more reliable landfill monitoring and management.