Giorgio De Donno

The characterization of aquifers is critical for managing freshwater resources threatened by urbanization, agriculture, and climate change. Traditional geophysical techniques, such as electrical resistivity tomography (ERT) and seismic refraction tomography (SRT), provide insights into subsurface structures but can be limited by resolution and interpretational ambiguities. This study introduces a novel approach that combines ERT, SRT, and time-domain induced polarization (TDIP) data in a petrophysical framework, including a direct transformation model (numerical three-phase model [N3PM]) and a comprehensive petrophysical joint inversion scheme. By integrating these complementary data sets, our method enhances the high-resolution imaging of coastal aquifers, enabling more accurate identification of the water table, porosity, and salinization. This approach was tested on a synthetic example and two field sites in the Pontina Plain, Italy. Results demonstrated that N3PM allows for a fast petrophysical screening where prior knowledge is available and the contribution of surface conduction is properly acknowledged, whereas the joint inversion, particularly with the inclusion of TDIP, ensured the reliable characterization of aquifer layers, capturing fine-grained and saline-impacted zones. This approach advances the noninvasive, quantitative assessment of aquifers, offering a valuable tool for groundwater management in coastal regions.

The increasing demand for high-resolution subsurface imaging has driven significant advances in geophysical inversion methodologies. Despite the availability of various software packages for electrical resistivity tomography (ERT), time-domain induced polarization (TDIP), and seismic refraction tomography (SRT), significant challenges remain in selecting optimal regularization parameters and in the effective incorporation of prior information into the inversion process. In this study, we propose new strategies to address these critical issues by developing versatile and flexible tools for electrical and seismic tomographic data inversion. Specifically, we introduce two automated procedures for regularization parameter selection: a full loop method (fixed-λ optimization) where the regularization parameter is kept constant during the inversion process, and a single-inversion approach (automaticLam) where it varies throughout the iterations. Additionally, we present a novel constrained inversion strategy that effectively balances prior information, minimizes data misfit, and promotes model smoothness. This approach is thoroughly compared with the state-of-the-art methods, demonstrating its superiority in maintaining model reliability and reducing dependence on subjective operator choices. Applications to synthetic, laboratory, and real-world case studies validate the efficacy of our strategies, showcasing their potential to enhance the robustness of geophysical models and standardize the inversion process, ensuring its independence from operator decisions.

In this work, we present an integrated and quantitative approach to detect and localize geophysical targets associated to both geological and anthropogenic complex scenarios. We complement electrical and seismic tomographic techniques and a machine-learning unsupervised algorithm (Fuzzy C-Means) to get a final clustered section validated by borehole and well data, where the reliability is evaluated by the membership function. This is a good estimator of the reliability of the proposed procedure, as it ranges 0-1, with 1 reflecting a high reliability of the clustering analysis. This method is applied to two case studies, related to the detection of leachate in a municipal solid waste landfill, and to the detection of the bedrock surface in a site prone to instability. For both cases, we also set up synthetic simulations by reproducing realistic models with similar layering and ranges of geophysical parameters as per the field cases. The results show the effectiveness of the method in providing a unique detection of the cluster associated with the desired targets, as highlighted by the matching with direct information. However, the accuracy of the reconstruction is reduced in areas where the resolution of geophysical methods is lower. We demonstrate that the proposed method can achieve a reliable reconstruction if it focuses on searching for one desired target only, rather than a comprehensive reconstruction of the whole layering at the site. This approach can be a valuable automatic tool for improving the benefit-cost ratio of projects where new constructions or remediation interventions have to be planned.

Uncontrolled accumulation of leachate in municipal solid waste landfills can represent a potential hazard in municipal solid waste (MSW) landfills, since it is a liquid with a high pollutant content that can contaminate aquifers. Electrical resistivity tomography (ERT) in combination with induced polarization (IP) method can be highly effective for detecting areas with presence of leachate, given the electrical properties of leachate (highly conductive and chargeable) compared to the unsaturated waste mass and the surroundings areas (covering and bottom layers). However, the geoelectrical reconstruction expressed in terms of individual geophysical quantities (resistivity and chargeability), leaves room for ambiguities in such a complex scenario that involves several resistive-conductive transitions. In this study, we use a machine learning-based approach to integrate ERT and IP tomographic data for improving the accuracy in the detection of leachate accumulation zones. After geophysical data inversion, we perform the cluster analyses on the inverted models (resistivity, chargeability and normalized chargeability) by using both hard (K-Means) and soft (Fuzzy C-Means) clustering algorithms. We finally achieve a comprehensive integrated model of the landfill with also an assessment of the reliability of the reconstruction through computation of validation indices and, only for soft clustering, of the uncertainty by means of the membership function. We apply the proposed procedure to synthetic examples and to a real-world investigation of a MSW landfill located in Central Italy. We firstly carry out synthetic examples to test the efficiency of hard and soft clustering in imaging leachate accumulation zones, by developing multi-layer models simulating a typical MSW geometry. Then, the results of the application of the proposed approach to the real-world example show an improvement of the accuracy in the detection of leachate accumulation zones as well as an assessment of the uncertainty both in the lateral and vertical directions. Therefore, this approach can be an effective and fast tool in real cases where leachate pollution detection is required at short time intervals for waste management.

The detection and imaging of landfills is a challenging task for geophysical methods because major pitfalls may arise, in such complex areas, from the speculative interpretation of geophysical anomalies as geological or antrophic features. In fact, when we face a multi-layered scenario, with numerous resistive to conductive transitions (that is the case of landfills), the actual shape and position of the anomalies (e.g. due to leachate accumulation) can be biased. The use of electrical resistivity tomography (ERT) in combination with the induced-polarization (IP) method, can help in this sense, even though may be not sufficient to completely remove ambiguities in interpretation of inverted models.In this work, we present an application of an unsupervised machine learning k-means algorithm to ERT and IP data acquired in two urban waste disposal sites. The aim of the cluster analysis is to reduce the ambiguity on geophysical model interpretation and to improve the accuracy on detection of anomalous zones related to leachate accumulation. Experimental 2D field data were firstly inverted separately for resistivity and chargeability, using a Gauss-Newton algorithm. Then, joint 2D sections were obtained using k-means clustering of electrical resistivity, chargeability and normalized chargeability (chargeability divided by the resistivity) data. The retrieved model sections provide a quantitative integration of distinct geophysical data, which can offer new perspectives for the characterization of leachate distribution in landfills.