A study of stability analysis of pyroclastic covers based on electrical resistivity measurements

Usually, the degree of stability of a slope is quantified by the Factor of Safety whose values depend on physical and mechanical soil properties analyzed on samples of much reduced sizes or referring to very small soil volumes around porous probes. To overcome the limit of punctual information, we propose a semi-empirical approach based on the use of geophysical methods and the employment of a geophysical Factor of Safety recently introduced by the authors in terms of local resistivities and slope angles. In this paper, we show an application of our proposal on a test area of about 2000 m2 on Sarno Mountains (Campania Region - Southern Italy), where shallow landslides involving pyroclastic soils periodically occur triggered by critical rainfall events. Starting from two resistivity tomography surveys performed on the test area in autumn and spring, we obtained maps of the geophysical Factor of Safety at different depths for the two seasons. We also estimated the values of the Factor of Safety by using the infinite slope model in the dry and saturated scenario. A comparison between the values of the geophysical and geotechnical Factor of Safety shows advantages and disadvantages of our approach.Comment: 16 pages, 5 figure

Revealing the spatiotemporal complexity of the magnitude distribution and b-value during an earthquake sequence

The Magnitude–Frequency-Distribution (MFD) of earthquakes is typically modeled with the (tapered) Gutenberg–Richter relation. The main parameter of this relation, the b-value, controls the relative rate of small and large earthquakes. Resolving spatiotemporal variations of the b-value is critical to understanding the earthquake occurrence process and improving earthquake forecasting. However, this variation is not well understood. Here we present remarkable MFD variability during the complex 2016/17 central Italy sequence using a high-resolution earthquake catalog. Isolating seismically active volumes (‘clusters’) reveals that the MFD differed in nearby clusters, varied or remained constant in time depending on the cluster, and increased in b-value in the cluster where the largest earthquake eventually occurred. These findings suggest that the fault system’s heterogeneity and complexity influence the MFD. Our findings raise the question “b-value of what?”: interpreting and using MFD variability needs a spatiotemporal scale that is physically meaningful, like the one proposed here

3-D spatial cluster analysis of seismic sequences through density-based algorithms

With seismic catalogues becoming progressively larger, extracting information becomes challenging and calls upon using sophisticated statistical analysis. Data are typically clustered by machine learning algorithms to find patterns or identify regions of interest that require further exploration. Here, we investigate two density-based clustering algorithms, DBSCAN and OPTICS, for their capability to analyse the spatial distribution of seismicity and their effectiveness in discovering highly active seismic volumes of arbitrary shapes in large data sets. In particular, we study the influence of varying input parameters on the cluster solutions. By exploring the parameter space, we identify a crossover region with optimal solutions in between two phases with opposite behaviours (i.e. only clustered and only unclustered data points). Using a synthetic case with various geometric structures, we find that solutions in the crossover region consistently have the largest clusters and best represent the individual structures. For identifying strong anisotropic structures, we illustrate the usefulness of data rescaling. Applying the clustering algorithms to seismic catalogues of recent earthquake sequences (2016 Central Italy and 2016 Kumamoto) confirms that cluster solutions in the crossover region are the best candidates to identify 3-D features of tectonic structures that were activated in a seismic sequence. Finally, we propose a list of recipes that generalizes our analyses to obtain such solutions for other seismic sequences

A Study of the Correlation Between Electrical Resistivity and Matric Suction for Unsaturated Ash-Fall Pyroclastic Soils in the Campania Region (Southern Italy)

In the territory of the Campania region (southern Italy), critical rainfall events periodically trigger dangerous fast slope movements involving ashy and pyroclastic soils originated by the explosive phases of the Mt. Somma-Vesuvius volcano and deposited along the surrounding mountain ranges. In this paper, an integration of engineering-geological and geophysical measurements is presented to characterize unsaturated pyroclastic samples collected in a test area on the Sarno Mountains (Salerno and Avellino provinces, Campania region). The laboratory analyses were aimed at defining both soil water retention and electrical resistivity curves versus water content. From the matching of the experimental data, a direct relationship between electrical resistivity and matric suction is retrieved for the investigated soil horizons typical of a ash-fall pyroclastic succession. The obtained relation turns out to be helpful in characterizing soils up to close saturation, which is a critical condition for the trigger of slope failure. In such a regime, the water content and the matric suction have small variations, while electrical resistivity variations can be appreciated in a larger range of values. For this reason, besides suction measurements on very small soil volumes through classical tensiometers, our analyses suggest the direct monitoring of in-situ electrical resistivity values as an effective tool to recognise the hydrological state of larger and more representative soil volumes and to improve early warning of dangerous slope movements.Comment: 23 pages, 10 figures, 2 table

Simulations of landslide hazard scenarios by a geophysical safety factor

Soil response to rainfall is a complex phenomenon that requires modeling of many sources of heterogeneity, whose variations can be relevant on various timescales and whose precise description requires a large amount of data inputs. Due to the great complexity of the problem, many simplifying assumptions are usually made in modeling landslides triggered by rainfall. As regards rainfall-induced shallow landslides, conventional approaches base slope stability analyses on the infinite slope model combined with hydrological models, which provide the time evolution of groundwater pressure head and volumetric water content. On the other hand, the response of geophysical quantities to water changes depends also on the variations in mechanical and hydrological properties. For this reason, we attempt a different approach to the problem of slope stability assessment by shifting the focus on the analysis of variations in geophysical properties. In this paper, starting from experimental resistivity data acquired in a test area, we perform a series of numerical simulations to study how changes in soil resistivity spatial distributions may affect the size of unstable areas. We use a simple cellular automaton whose states are defined by the values of a local and time-dependent geophysical factor of safety, which depends on soil electrical resistivity and slope inclination. We studied the probability of occurrence of rainfall-induced shallow landslide events by driving the system to instability through a decrease in electrical resistivity values. Numerical simulations are performed by varying number and intensity of the applied perturbations. Hazard scenarios obtained by in situ distributions of resistivity values are compared with those coming from initial random distributed resistivity values. Our results suggest possible critical rates of resistivity changes for triggering instability in the investigated area and point out the crucial role of resistivity variations in prediction of larger events

Water storage mapping of pyroclastic covers through electrical resistivity measurements

The knowledge of the geological setting of pyroclastic covers and their water content distribution represents crucial information for stability analyses of slopes potentially subject to debris-flow phenomena. The study we here present would provide a contribution to this issue by means of an approach based on electrical resistivitymeasurements. Specifically, we describe the results of high-resolution 2D resistivity surveys carried out in a test area on SarnoMountains (Campania Region – Southern Italy), where shallowlandslides involving pyroclastic soils periodically occur triggered by critical rainfall events. We discuss the results in relation to the geology of the area in order to locate characteristic horizons of pyroclastic soils below the ground surface. Then, on the basis of a laboratory characterization of pyroclastic samples collected from the same test area at representative depths, we provide an estimation of the soil water content distribution in the field. Finally, we analyze temporal variations of the soil water content distribution by comparing the data of two surveys carried out in the autumnal and spring seasons, respectively

High-resolution spectral analysis methods for self-potential data inversion

In the last few decades, the spectral analysis approach has been successfully applied for depth estimation of gravity and magnetic anomaly sources by analyzing the signal power distribution as a function of spatial frequencies. In the present work, application of high-resolution spectral methods is proposed for inversion of self-potential (SP) data. In particular, Periodogram Method (PM), Maximum Entropy Method (MEM) and Multi Taper Method (MTM) are used to invert synthetic SP data generated by cylinder and sheet sources. From analysis, MEM was found to be better in providing more accurate estimates of the source depth as compared to PM and MTM. Finally, the application of the proposed methods to field data is presented and the estimated depths are compared with those obtained by other numerical methods

Source depth estimation of self-potential anomalies by spectral methods

Spectral analysis of the self-potential (SP) field for geometrically simple anomalous bodies is studied. In particular, three spectral techniques, i.e. Periodogram (PM), Multi Taper (MTM) and Maximum Entropy (MEM) methods, are proposed to derive the depth of the anomalous bodies. An extensive numerical analysis at varying the source parameters outlines that MEM is successful in determining the source depth with a percent error less than 5%. The application of the proposed spectral approach to the interpretation of field datasets has provided depth estimations of the SP anomaly sources in very good agreement with those obtained by other numerical methods