Identifying archaeological features through geophysical data interpretation remains a key challenge in non-invasive prospection. Geophysical surveys are increasingly combined with unsupervised machine learning, particularly K-means clustering, to enhance the detection of buried remains. However, it operates only in attribute space, ignoring the spatial nature of archaeological structures. This study assesses whether adding spatial coordinates in K-means clustering of Frequency Domain ElectroMagnetic data improves the delineation of anthropogenic features. A 2D approach, based only on electromagnetic components, is compared with a 4D configuration including geographic coordinates. FDEM data from the Iron Age site of Torre Galli (Italy) show that adding spatial coordinates does not outperform the 2D approach. Instead, results become more sensitive to normalization and often reflect spatial proximity rather than geophysical contrasts, reducing interpretability. These findings indicate that incorporating spatial coordinates into a standard K-means framework does not systematically improve archaeological feature delineation and increases sensitivity to data preprocessing.

An accurate assessment of leachate levels necessitates the integration of various parameters. Traditional geophysical prospecting methods often lack measurable accuracy because they focus on individual parameters rather than effectively integrating data. This may lead to inconsistent estimates of leachate depth and make the evaluation of prediction reliability challenging. In this study, we exploit hard and soft cluster analyses to improve the effectiveness of geoelectrical methods in identifying the extent of leachate accumulation zones. A machine learning-based approach employing hard clustering on resistivity and induced polarization data was recently proposed to obtain integrated model sections that highlight leachate accumulation zones in municipal solid waste landfills. In those models, areas with different colours represent areas characterized by specific ranges of values of the considered physical quantities and have strictly defined boundaries. This is an intrinsic limitation of hard clustering that carries out cluster assignments without providing an assessment of the reliability of the reconstruction. In contrast, soft clustering approaches provide estimation of the cluster membership that allows a refinement of cluster boundaries, improving the identification of groups in the data. We apply hard and soft cluster analyses to geoelectrical data for detecting leachate accumulation zones in a landfill located on a steep slope in Central Italy. There, leachate may not only contaminate groundwater but also trigger instability phenomena. Among the different clustering algorithms, we selected K-means due to its simplicity of implementation, its ability to identify clusters that are both compact and distinct, and its faster performance compared to Fuzzy Cmeans. The clusters associated with the leachate accumulation zones represent approximately the 11% of total investigated subsoil and are characterized by values of resistivity, chargeability and normalized chargeability in the ranges of about 1.5-5 Omega m, 10-70 mV/V and 4.5-37 mS/m, respectively. Then, we applied the Fuzzy C-means algorithm to obtain the degree of membership of points belonging to such areas and better outline their boundaries. By considering a fuzzy membership greater than 0.5, we achieve an accuracy exceeding 90% in identifying leachate in wells. Furthermore, identifying zones with lower membership we delineate the boundaries of less saturated regions as well as those that are more saturated, providing a reconstruction of potential preferential leachate flows within the waste mass. These findings have important practical implications as they contribute to cost reductions for future drilling and monitoring processes.

The Frequency Domain Electromagnetic (FDEM) method is a cost-effective geophysical technique that simultaneously studies the electrical and magnetic properties of a medium, providing data as in-phase and out-of-phase components of the electromagnetic field. Although FDEM yields valuable insights, its results can be complex to interpret, and the two EM field components are normally only visually inspected to support findings from other techniques. This study aims to enhance FDEM data interpretation using an unsupervised learning technique. The proposed approach seeks to automate and expedite the interpretative phase. By applying the K-Means clustering algorithm, we divided the FDEM data into several clusters based on specific intervals of the in-phase and quadrature components, resulting in integrated maps of EM components. Combining these maps with geological and archaeological insights helped identifying areas of potential archaeological interest. This method was applied to the Torre Galli archaeological site in Calabria, Italy, known for its significance in Iron Age studies.Based on comparisons with the findings of earlier excavations and results from a magnetic survey, the proposed procedure shows promise in improving the efficiency and accuracy of the FDEM method in identifying areas of archaeological interest. This suggests that automating the interpretation process could lead to a better cost management and time optimization in geophysical and archaeological studies.

The 1688 Sannio–Matese earthquake, with a macroseismically derived magnitude of Mw = 7 and an epicentral intensity of IMCS = XI, had a deep impact on Southern Italy, causing thousands of casualties, extensive damage and significant environmental effects (EEEs) in the epicentral area. Despite a comprehensive knowledge of its economic and social impacts, information regarding the earthquake’s environmental effects remains poorly studied and far from complete, hindering accurate intensity calculations by the Environmental Seismic Intensity Scale (ESI-07). This study aims to address this knowledge gap by compiling a thorough dataset of the EEEs induced by the earthquake. By consulting over one hundred historical, geological and scientific reports, we have collected and classified, using the ESI-07 scale, its primary and secondary EEEs, most of which were previously undocumented in the literature. We verified the historical sources regarding some of these effects through reconnaissance field mapping. Analysis of the obtained dataset reveals some primary effects (surface faulting) and extensive secondary effects, such as slope movements, ground cracks, hydrological anomalies, liquefaction and gas exhalation, which affected numerous towns. These findings enabled us to reassess the Sannio earthquake intensity, considering its environmental impact and comparing traditional macroseismic scales with the ESI-07. Our analysis allowed us to provide an epicentral intensity ESI of I = X, one degree lower than the published IMCS = XI. This study highlights the importance of combining traditional scales with the ESI-07 for more accurate hazard assessments. The macroseismic revision provides valuable insights for seismic hazard evaluation and land-use planning in the Sannio–Matese region, especially considering the distribution of the secondary effects.

A comprehensive analysis of the gravity and magnetic fields of the Phlegrean Fields volcanic area reveals a complex structural setting. High resolution techniques, including multiscale boundary analysis and field transformations, highlight the position of density and magnetization boundaries of the Phlegrean Fields. The structural elements of the two datasets appear in a general agreement, but at the same time present differences, so yielding a complex and rich picture of the collapsed Phlegrean caldera and of the surrounding areas. Inside the caldera, a good consistence among some structural elements identified from our analysis and the seismicity can be established. Some hypotheses about the geological significance of the gravity and magnetic anomalies in the frame of the Phlegrean volcanological context are finally set up.

This study proposes a procedure to improve the interpretation of data from the Frequency Domain ElectroMagnetic method (FDEM), a geophysical technique with high benefit-cost ratios in archaeology. This method enables the simultaneous analysis of electrical and magnetic properties of the investigated medium, providing data as in-phase and out-of-phase (quadrature) components of the electromagnetic field. Traditionally, FDEM produces individual maps for these components that are typically inspected and related to each other only visually. The proposed procedure is based on unsupervised machine learning that, by providing integrated maps of these two components, overcomes the limitations of visual inspection and streamlines the interpretative phase. The FDEM data acquired at the Torre Galli archaeological site in Calabria, Italy, were clustered using K-means, known for its effectiveness in partitioning densely distributed spatial data. The optimal number of clusters was determined through silhouette analysis. Once identified, the clusters were classified as either representative of the natural background or indicative of archaeological interest and mapped in real space. This analysis identified several areas with anomalous geophysical characteristics compared to the natural context, characterized by distinct ranges of EM property values, suggesting the presence of different archaeological targets, such as road, walls and iron/bronze tools, which are distributed heterogeneously in the subsoil. This classification facilitated the rapid delineation of areas with significant archaeological potential that may warrant further investigation, resulting in a reduction of both the study area size and the archaeological effort associated with excavation. A comparison of these areas with previous excavations and the results of a magnetic survey shows that the proposed procedure is promising in enhancing the efficiency and accuracy of the FDEM method for identifying areas of archaeological interest. The findings suggest a move towards automation in the interpretation process, which could improve cost-effectiveness and time optimization in geophysical and archaeological investigations.

With the continuous development of society, the rapid urbanization of cities, and the increasing construction of large-scale infrastructure projects, seismic hazard studies are becoming increasingly necessary [...]

The Irpinia Fault, also known as the Monte Marzano Fault System, located in the Southern Apennines (Italy), is one of the most seismically active structures in the Mediterranean. It is the source of the 1980, Ms 6.9, multi-segment rupture earthquake that caused significant damage and nearly 3,000 casualties. Paleoseismological surveys indicate that this structure has generated at least four Mw ~ 7 surface-rupturing earthquakes in the past 2 ka. This paper presents a comprehensive, high-resolution geophysical investigation focused on the southernmost fault segment of the Monte Marzano Fault System, i.e., the Pantano-Ripa Rossa Fault, outcropping within the Pantano di San Gregorio Magno intramontane basin. The project, named TEst Site IRpinia fAult (TESIRA), was supported by the University of Napoli Federico II to study the near-surface structure of this intra-basin fault splay that repeatedly ruptured co-seismically in the past thousands of years. Our imaging approach included 2D and 3D electrical and seismic surveys, gravimetry, 3D FullWaver electrical tomography, drone-borne GPR and magnetic surveys, and CO2 soil flux assessment across the surface rupture. This multidisciplinary investigation improved our understanding of the basin shallow structure, providing an image of a rather complex subsurface fault and basin geometry. Seismic data suggest that fault activity at the Pantano segment of MMFS is characterized by a near-surface cumulative displacement greater than previous estimations, calling into question earlier assumptions about the timing of its activation. Despite some challenges with our drone-mounted survey equipment, the integrated dataset provides a comprehensive and reliable image of the subsurface structure. This work demonstrates the utility of developing an integrated approach at high-resolution geophysical imaging and interpretation of fault zones with weak morphological expressions.

In this chapter, we review the main results of electrical and electromagnetic prospecting applied to the characterization and monitoring of municipal waste landfills in the last decade. Among all the geophysical surveys, these methods are the most used for subsurface investigations of landfills since they provide a cost-effective approach that allows for detailed and non-invasive imaging of the subsurface in terms of the electrical properties, down to depths which generally vary from a few tens of centimeters to several tens of meters. Nevertheless, the indirect geophysical mapping needs the direct even if punctual information from boreholes and wells for an accurate reconstruction of the contaminated zones. Electrical and electromagnetic methods are used for multiple purposes that include mapping landfill boundaries, measuring waste volume and composition, as well as identifying and tracking leachate plumes. In particular, electrical methods are widely used for leachate detection (both inside and outside the landfill) and for the geometrical reconstruction of the landfill using electrical conductivity and chargeability as the main proxies. Low-frequency electromagnetic methods are mostly used for a hydrogeological characterization and extensive screening of the high-conductive areas associated to the leachate accumulation. These methods have lower resolution compared to the electrical techniques but often allow greater depth of investigation. High-frequency electromagnetic surveys are instead mainly focused on the shallow part of the landfill for detection of defects on the covering liner and characterization of the covering layer. We discuss recent results related to the topic providing updated references in relation to the specific applications and emphasizing the importance of site-specific validation through direct information. At last, a special focus is given to novel trends, emerging techniques and data integration by machine learning-based approaches for mapping and monitoring of municipal solid waste landfills.

In this work, we present an integrated and quantitative approach to detect and localize geophysical targets associated with both geological and anthropogenic complex scenarios. We complement electrical and seismic tomographic techniques and a machine-learning (ML) unsupervised algorithm [fuzzy C-means (FCM)] 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 from 0 to 1, with one 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 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-to-cost ratio of projects, where new constructions or remediation interventions have to be planned.

We conducted large-depth Ground-Penetrating Radar investigations of the seismogenic Casamicciola fault system at the volcanic island of Ischia, with the aim of constraining the source characteristics of this active and capable fault system. On 21 August 2017, a shallow (hypocentral depth of 1.2 km), moderate (Md = 4.0) earthquake hit the island, causing severe damage and two fatalities. This was the first damaging earthquake recorded on the volcanic island of Ischia from the beginning of the instrumental era. Our survey was performed using the Loza low-frequency (15–25 MHz) GPR system calibrated by TDEM results. The data highlighted variations in the electromagnetic signal due to the presence of contacts, i.e., faults down to a depth larger than 100 m below the surface. These signal variations match with the position of the synthetic and antithetic active fault system bordering the Casamicciola Holocene graben. Our study highlights the importance of employing large-depth Ground-Penetrating Radar geophysical techniques for investigating active fault systems not only in their shallower parts, but also down to a few hundred meters’ depth, providing a contribution to the knowledge of seismic hazard studies on the island of Ischia and elsewhere.

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Electrical resistivity tomography (ERT) is an effective method for detecting the leachate plume due to the plume's very low resistivity values. However, it is well-known that identifying contaminated areas in landfill sites based only on the distribution of electrical resistivity values is highly ambiguous, especially in the presence of clayey soils, given the low resistivity values that generally characterize both wet/saturated clays and contamination plumes. To overcome this problem, the ERT method is usually combined with the induced polarization method to derive useful information on leachate detection from the resistivity, chargeability, and ratio values. In this study, we developed a tentative methodology for leachate detection based on clustering analysis of geoelectrical data. The k-means algorithm was applied to perform a cluster analysis of the inverted resistivity and chargeability data acquired in a landfill site in the Campania region (southern Italy). This site is in a geological context characterized by silty-clayey deposits, with intercalations of graded sandstones from the Miocene age. Therefore, it represents a meaningful test bench for investigations integrating different geophysical datasets.

The scientific project TESIRA (TEst Site IRpinia fAult), funded in 2021 by the University of Naples “Federico II”, aims at acquiring multidisciplinary geophysical data above an active fault in an intramontane basin of the Southern Apennines and to achieve, through the integration of this multivariate dataset, an accurate 3D geophysical imaging of the shallow structure of the fault zone in order to understand the link between shallow faulting and petrophysical changes, which affect rock permeability and surface degassing. The target structure is the southern branch of the Irpinia Fault, one of the structures with highest seismogenic potential in the Mediterranean region, causing the 4th Italian earthquake of last century (1980, Ms=6.9, Pantosti & Valensise, 1990) and generating a modest surface throw at Pantano San Gregorio Magno (Salerno).A microgravimetric survey and a 3D and 2D Electrical Resistivity measurements survey were acquired between September 2021 and January 2022. 3D seismic data were acquired in July 2022, using two overlapping arrays with a dense geophone distribution covering an area of about four hectares, with a detail of 2.5x2.5m. Moreover, four 2D seismic profiles intersect the 3D volume. An aeromagnetic survey, an extension of the gravimetric survey and a sampling of the CO2 surface degassing will be completed within this year. We will show the preliminary results of the individual surveys. Later, the different geophysical and geochemical measurements will be integrated using cooperative inversion and machine learning techniques. We hope that this multidisciplinary approach will provide a more comprehensive understanding of the interaction between surface faulting and basin development in this key area of the Southern Apennines. ReferencesPantosti, D.; Valensise, G.; [1990] Faulting Mechanism and Complexity of the November 23, 1980, Campania-Lucania Earthquake, Inferred From Surface Observations, JGR, 95, 319-34

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.

Leachate is the main source of pollution in landfills and its negative impacts continue for several years even after landfill closure. In recent years, geophysical methods are recognized as effective tools for providing an imaging of the leachate plume. However, they produce subsurface cross-sections in terms of individual physical quantities, leaving room for ambiguities on interpretation of geophysical models and uncertainties in the definition of contaminated zones. In this work, we propose a machine learning-based approach for mapping leachate contamination through an effective integration of geoelectrical tomographic data. We apply the proposed approach for the characterization of two urban landfills. For both cases, we perform a multivariate analysis on datasets consisting of electrical resistivity, chargeability and normalized chargeability (chargeability-to-resistivity ratio) data extracted from previously inverted model sections. By executing a K-Means cluster analysis, we find that the best partition of the two datasets contains ten and eleven classes, respectively. From such classes and also introducing a distance-based colour code, we get updated cross-sections and provide an easy and less ambiguous identification of the leachate accumulation zones. The latter turn out to be characterized by coordinate values of cluster centroids<3 Ωm and >27 mV/V and 11 mS/m. Our findings, also supported by borehole data for one of the investigation sites, show that the combined use of geophysical imaging and unsupervised machine learning is promising and can yield new perspectives for the characterization of leachate distribution and pollution assessment in landfills.

•In this “Comment” article to “P. Mancinelli, V. Scisciani, S. Patruno, G. Minelli” published on Tectonophysics We want to point out that their gravity model is based on a low-resolution dataset unable to represent the gravity contribution of shallow structures and the modelling results appear compromised by methodological errors.

In this study, we performed two electric resistivity tomography (ERT) profiles and a 3-D volume to characterize and reconstruct an active fault located at “Pantano di San Gregorio Magno”, an intramontane basin of Southern Apennines, Italy, shaped by the active tectonics and hit by the Ms= 6.9, Irpinia 1980 earthquake, generated by a fault rupturing the soil in Pantano. The overall Irpinia Fault has a total length of about 40 km and with a thickness of the seismogenic layer of 8-10 km. The Irpinia Fault is among the faults with highest seismogenic potential in the Mediterranean region, causing the 4th Italian earthquake of last century (1980, Ms=6.9, Pantosti & Valensise, 1990) and generating a modest surface throw at Pantano San Gregorio Magno (SA). The basin is filled by lacustrine sediments, bassing laterally into alluvial fans and slope debris, laying over Mesozoic carbonate bedrock. The two 2D ERT surveys and the 3D volume allowed us to obtain high resolution images of the electrical resistivity of the subsoil both in 2D and in 3D. The results provided us with an opportunity to achieve a better understanding of the geometry of the active fault. The filling sediments are characterized by resistivity ranging from 10 to 150 Ohm m while the Mesozoic carbonate bedrock from 500 up to 1500 Ohm m. Furthermore, strong resistivity gradients are consistent with the tectonic structure characterizing the active fault.

We describe here the results of the characterization of subsurface structures in an area of the south-eastern edge of the Bohemian Massif, in Austria by high-resolution geophysical survey techniques and advanced analysis methods of potential fields. The employed methods included potential field multiscale techniques for source-edge location and characterization of sources at depth. Our results confirmed the presence of already known structures: the location of the Diendorf Fault and the Moldanubian Shearzone are clearly recognized in the data at the same location as on the geological maps, even where the Diendorf fault is covered with sediments of the Molasse Basin. In addition, we detected several geological contacts between different rock types in the Bohemian Massif west of the Diendorf Fault. From our results, we were also able to quickly identify and image, without a priori information, previously unknown structures, such as faults with-depth-to-the top of about 500 m and magmatic intrusions about 400 m deep.

Sediments infilling in intermontane basins in areas with high seismic activity can strongly affect ground-shaking phenomena at the surface. Estimates of thickness and density distribution within these basin infills are crucial for ground motion amplification analysis, especially where demographic growth in human settlements has implied increasing seismic risk. We employed a 3D gravity modeling technique (ITerative RESCaling-ITRESC) to investigate the Fucino Basin (Apennines, central Italy), a half-graben basin in which intense seismic activity has recently occurred. For the first time in this region, a 3D model of the Meso-Cenozoic carbonate basement morphology was retrieved through the inversion of gravity data. Taking advantage of the ITRESC technique, (1) we were able to (1) perform an integration of geophysical and geological data constraints and (2) determine a density contrast function through a data-driven process. Thus, we avoided assuming a priori information. Finally, we provided a model that honored the gravity anomalies field by integrating many different kinds of depth constraints. Our results confirmed evidence from previous studies concerning the overall shape of the basin; however, we also highlighted several local discrepancies, such as: (a) the position of several fault lines, (b) the position of the main depocenter, and (c) the isopach map. We also pointed out the existence of a new, unknown fault, and of new features concerning known faults. All of these elements provided useful contributions to the study of the tectono-sedimentary evolution of the basin, as well as key information for assessing the local site-response effects, in terms of seismic hazards.