1,721,072 research outputs found
Analysis of geoelectric data through machine learning algorithms for waste leachate detection
No full textElectrical 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
Analysis of geoelectric data through machine learning algorithms for waste leachate detection
No full textElectrical resistivity tomography (ERT) is an effective method for detection of the leachate plume due to the very low resistivity values of plumes. However, it is well known that the identification of contaminated areas in landfill sites based only on the distribution of electrical resistivity values is highly ambiguous especially in 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 generally used in combination with the induced polarization method to derive useful information on leachate detection from the values of resistivity, chargeability, and their ratio. In this study, we develop a tentative methodology for leachate detection based on clustering analysis of geoelectrical data. k-means algorithm is applied to perform a cluster analysis of the inverted resistivity and chargeability data acquired in a landfill site located 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 and, therefore, it represents a meaningful test bench for investigations integrating different geophysical datasets
Aspetti medico-legali sulla responsabilità professionale medica, con particolare riferimento a quella dell'odontoiatra.
No full text
Size and time distributions of landslide events
No full textSurprisingly the analysis of landslide inventories has shown that landslide events associated with different triggers can be characterized by the same probability distribution. Inventory maps show that such a probability distribution exhibits two regimes: an increasing behaviour for small landslides and a power-law scaling, with a negative scaling exponent, for large landslides. Conversely from other approaches that retrieve the characteristic distribution a posteriori as the best fit of data sets of specific events, we propose a cellular automaton model able to
reproduce the landslide size distribution a priori by means of some characteristic parameters. From the comparison between our synthetic probability distribution and the landslide area probability distribution of three landslide inventories, we estimated the typical size of a single cell of our cellular automaton model, ranging from 35 sq m to 100 sq m, which is a crucial information if we are interested in monitoring a test area. We characterize the landslide frequency-size distribution by varying the model parameters and find that to determine the probability of
occurrence of a landslide of size s it is crucial to get information about the rate at which the system is approaching instability rather than the nature of the trigger. As the rate is increased, the model has a crossover from a critical regime with power-laws to non power-law behaviors. We also introduce a landslide-event magnitude scale based on the driving rate. Large values of the proposed intensity scale are related to landslide events with a fast approach to instability in a long distance of time, while small values are related to landslide events close together in time
and approaching instability slowly. Another key aspect for hazard prediction is the inter-event occurrence time statistics. The inter-event occurrence time is the interval between events whose sizes are above a given threshold. Analyses of time series of events
have shown that the inter-event occurrence time distribution seems to obey a Weibull distribution with the shape parameter less than one. Such a feature suggests temporal clustering of landslide events. We use our cellular automaton model to study whether simulated landslide events are correlated in time. We characterize the inter-event time distribution by varying the size threshold and the parameters of the model. We find that as the
rate at which the system approaches instability is increased, the time distribution has a crossover from different regimes. This result states again the key role of such a rate for landslide hazard assessment
Geotechnical vs. Geophysical models for slope stability
No full textTraditionally, the calculation of the Factor of Safety is based on accurate geotechnical measurements, which provide information on the internal structure and the mechanical properties of the investigated soils through the analysis of samples of very reduced
size. Hence, both empirical and physically based traditional approaches are based on point information, which refer to very small rainwater collecting areas of rain gauges and very small soil volumes around porous probes. To overcome the limit of point-sampled information, we propose a semi-empirical approach based on the use of a geophysical Factor of Safety introduced in terms of local resistivities and slope angles. Starting from two
resistivity tomography surveys performed on a test area on Sarno Mountains (Southern Italy) during the autumnal and spring seasons, we present an application of the proposed geophysical approach and compared the results with those coming from the infinite slope analysis
La zonazione a grande scala: Caratterizzazione geofisica in sito e in laboratorio.
No full textIn questo lavoro si propone un metodo non-invasivo basato su un’interpretazione congiunta di misure di resistività elettrica di sito e di laboratorio, finalizzato sia alla conoscenza dell’assetto geologico di coltri piroclastiche sia alla determinazione della distribuzione del contenuto d’acqua al loro interno. I risultati di un’applicazione del metodo a un’area test dei Mt. di Sarno (Salerno, Italy), evidenziano le potenzialità dell’approccio proposto nell’analisi di stabilità di versanti potenzialmente soggetti a fenomeni franosi indotti da piogge, e suggeriscono un monitoraggio in continuo di tale parametro elettrico come uno strumento efficace per la definizione di soglie di allarme di potenziali frane di tipo debris-flow
Clustering analysis of ERT/IP data for leachate mapping in urban waste landfills
No full textIn this work, we present a quantitative integration of electrical resistivity and induced polarization tomographic data for imaging leachate in urban waste landfills. The main goal is to reduce the residual ambiguities, often arising from a speculative interpretation of the geophysical models in such complex scenarios, providing a more effective image of the hazardous zones linked to the leachate accumulation. Field data, acquired in a municipal waste site, are firstly inverted for resistivity and chargeability. Then, we use log-transformed inverted parameters (resistivity and normalized chargeability) as the input for a clustering analysis performed by the K-means unsupervised machine learning-based algorithm. We found in the log-transformed space a clear decreasing trend from the hazardous to the non-hazardous areas, which eases the clustering labelling in terms of different hazard levels. The most hazardous areas, likely due to the leachate accumulation zones, are found not only at the bottom of the landfill, as expected from a standalone data inversion, but also in the shallower part, accordingly to well data. Our findings show the potential of machine learning-based techniques for data integration, offering new perspectives for the characterization of landfills
Modeling of magnetic anomalies generated by simple geological structures through Genetic-Price inversion algorithm
No full textA new approach to the interpretation of magnetic anomalies generated by geological structures resembling simple geometrical bodies is presented. The method is based on the Genetic-Price hybrid Algorithm (GPA) recently proposed by the authors for the inversion of potential-field data, specifically of self-potential signals. In this paper, the effectiveness of the proposed algorithm is tested on magnetic data for retrieving the parameters of the anomaly causative sources. First, the testing is performed on synthetic noise-free and noisy signals due to magnetized sphere-, dike- and fault-like models, then the analysis is extended to field magnetic anomalies. Concerning the synthetic data analyses, a very good agreement is obtained between assumed and retrieved source parameters. Specifically, the error between true and inverted parameter sets was found to be no higher than 9% even by adding 15% of Gaussian white random noise to the initial dataset. As for the study of field data, the values of depth, horizontal position, effective magnetization intensity and angle provided by the proposed GPA method compare well with those obtained by other interpretative approaches. Finally, the results of the GPA application to the inversion of magnetic data measured in the Mt. Somma-Vesuvius volcanic area are reported. In particular, the interpretation of the magnetic anomalies along a SW-NE profile in terms of multiple dikes provides information about depth and location of buried volcanic structures that match well those from other geophysical and geological analyses
Cellular automaton simulations of the temporal pattern of activity of a volcano with an application to Vesuvius activity between 1631 and 1944
No full textWe show the results of simulations of basaltic strato-volcano activity by using a cellular automaton model where magma is
allowed to rise through self-organized crack networks. Magma rises toward the surface by filling connected paths of fractures until the magma’s density is less than, or equal to that of the surrounding rocks. We simulate the temporal evolution of such pathway of dikes which magma may eventually utilize to reach the surface with the occurrence of an eruption. Magma degassing is also taken into account by means of the relationship between the pressure-controlled water solubility and the lithostatic pressure. We study the statistical properties of the automaton by varying the model parameters and, in particular, the thickness of the uppermost rock layer, which controls the buoyancy rate of magma rise because of its low value
of density. An application of the model to the statistics of the eruptive activity of the Somma-Vesuvius volcano for the
1631-1944 period is discussed
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