Multifractal Fluctuations in Seismic Interspike Series

Multifractal fluctuations in the time dynamics of seismicity data have been analyzed. We investigated the interspike intervals (times between successive earthquakes) of one of the most seismically active areas of central Italy by using the Multifractal Detrended Fluctuation Analysis (MF-DFA). Analyzing the time evolution of the multifractality degree of the series, a loss of multifractality during the aftershocks is revealed. This study aims to suggest another approach to investigate the complex dynamics of earthquakes

Complexity analysis in particulate matter measurements

We investigated the complex temporal fluctuations of particulate matter data recorded in London area by using the Fisher-Shannon (FS) information plane. In the FS plane the PM10 and PM2.5 data are aggregated in two different clusters, characterized by different degrees of order and organization. This results could be related to different sources of the particulate matter

Site Specific Ground Motion Modeling and Seismic Response Analysis for Microzonation of Baku, Azerbaijan

We investigated ground response for Baku (Azerbaijan) from two earthquakes of magnitude M6.3 occurred in Caspian Sea (characterized as a near event) and M7.5 in Shamakhi (characterized as a remote extreme event). S-wave velocity with the average shear wave velocity over the topmost 30 m of soil is obtained by experimental method from the V P values measured for the soils. The downtown part of Baku city is characterized by low VS30 values (< 250 m/s), related to sand, water-saturated sand, gravel-pebble, and limestone with clay. High surface PGA of 240 gal for the M7.5 event and of about 190 gal for the M6.3 event, and hence a high ground motion amplification, is observed in the shoreline area, through downtown, in the north-west, and in the east parts of Baku city with soft clays, loamy sands, gravel, sediments

Global versus local clustering of seismicity. Implications with earthquake prediction

The estimation of the maximum expected magnitude is crucial for seismic hazard assessment. It is usually inferred via Bayesian analysis; alternatively, the size of the largest possible event can be roughly obtained from the extent of the seismogenic source and the depth of the brittle–ductile transition. However, the effectiveness of the first approach is strongly limited by catalog completeness and the intensity of recorded seismicity, so that it can be of practical use only for aftershocks, while the second is affected by extremely large uncertainties. In this article, we investigate whether it may be possible to assess the magnitude of the largest event using some statistical properties of seismic activity. Our analysis shows that, while local features are not appropriate for modeling the emergence of peaks of seismicity, some global properties (e.g., the global coefficient of variation of interevent times and the fractal dimension of epicenters) seem correlated with the largest magnitude. Unlike several scientific articles suggest, the b-value of the Gutenberg–Richter law is not observed to have a predictive power in this case, which can be explained in the light of heterogeneous tectonic settings hosting fault systems with different extension

Long-range fluctuations and multifractality in connectivity density time series of a wind speed monitoring network

This paper studies the daily connectivity time series of a wind speed-monitoring network using multifractal detrended fluctuation analysis. It investigates the long-range fluctuation and multifractality in the residuals of the connectivity time series. Our findings reveal that the daily connectivity of the correlation-based network is persistent for any correlation threshold. Further, the multifractality degree is higher for larger absolute values of the correlation threshol

Variable seismic responsiveness to stress perturbations along the shallow section of subduction zones. The role of different slip modes and implications for the stability of fault segments

Assessing the stability state of fault interfaces is a task of primary interest not only for seismic hazards, but also for understanding how the earthquake machine works. Nowadays it is well known that a relationship exists between slow and fast earthquakes;moreover, it is more and more evident that such a connection is quite diffuse all over the Earth. In this paper, we perform a spatial and temporal analysis of both geodetic and seismic—non-volcanic tremors, low-frequency events (LFEs), and regular earthquakes—time series. We focus on the relationship between the clustering of properties of the different kinds of seismicity and their response to stress perturbations. Earth tides and large earthquakes are used as a source of additional stress. Seismic activity hosted in the Cascadia subduction zone, Manawatu region in New Zealand, and Japan during the last two decades is considered. Our analysis suggests that tremors become more and more sensitive to Earthtide perturbations as the fault interface is seismically locked. Therefore, tremors and regular events show a similar response to tidal stress perturbations. This feature is also accompanied by relatively lower spatial and temporal coefficients of variation. A series of recordings by several GNSS stations along the Hikurangi Trench, North Island, New Zealand, and along the Nankai coasts in Japan is taken into account for studying how large thrust-faulting earthquakes affect silent events and geodetic signals and vice-versa. In the last section, a simple model for grasping a glimpse of the local stability condition of the Earth’s crust and for explaining previous observations is provided

Visibility graph analysis of synthetic earthquakes generated by the Olami-Feder-Christensen spring-block model.

In this study, we investigate the relationship between topological and seismological parameters of earthquake sequences generated by the Olami–Feder–Christensen (OFC) [Olami et al., Phys. Rev. Lett. 68(8), 1244 (1992)] spring-block model and converted in undirected graphs by using the visibility graph method [Lacasa et al., Proc. Natl. Acad. Sci. U.S.A. 105(13), 4972–4975 (2008)]. In particular, we study the relationship between the Gutenberg–Richter b-value and the so-called K–M slope, which describes the relationship between magnitudes and connectivity degrees. This relationship was found to follow a rather universal law in observational earthquake sequences, and, thus, in the present work, we aim at verifying such universality also in earthquake sequences generated by the OFC spring-block model. We found that for ⟨b⟩ between approximately 1 and 2, which is nearly the range of variation for most of the real seismicity cases observed worldwide, the relationship between ⟨b⟩ and ⟨K–M slope⟩ does not depend on the lattice size L. Furthermore, the slope of the regression line between ⟨b⟩ and ⟨K–M slope⟩ in the range of ⟨b⟩ between 1 and 2 changes with the definition of magnitude and the length of the earthquake sequence

Community detection analysis in wind speed-monitoring systems using mutual information-based complex network

A mutual information-based weighted network representation of a wide wind speed-monitoring system in Switzerland was analyzed in order to detect communities. Two communities have been revealed, corresponding to two clusters of sensors situated, respectively, on the Alps and on the Jura-Plateau that define the two major climatic zones of Switzerland. The silhouette measure is used to evaluate the obtained communities and confirm the membership of each sensor to its cluster

Predicción de caudales en río Colorado, Argentina

The identification of suitable models for predicting daily water flow is important for planning and management of water storage in reservoirs of Argentina. Long-term prediction of water flow is crucial for regulating reservoirs and hydroelectric plants, for assessing environmental protection and sustainable development, for guaranteeing correct operation of public water supply in cities like Catriel, 25 de Mayo, Colorado River and potentially also Bahía Blanca. In this paper, we analyze in Buta Ranquil flow time series upstream reservoir and hydroelectric plant in order to model and predict daily fluctuations. We compare results obtained by using a three-layer artificial neural network (ANN), and an autoregressive (AR) model, using 18 years of data, of which the last 3 years are used for model validation by means of the root mean square error (RMSE), and measure of certainty (Skill). Our results point out to the better performance to predict daily water flow or refill them of the ANN model performance respect to the AR model. La identificación de modelos adecuados para predecir caudales diarios es importante para la planificación y la gestión de almacenamiento de agua en los embalses de la Argentina. La predicción a largo plazo del caudal es crucial para la regulación de los embalses y centrales hidroeléctricas, evaluar la protección del medio ambiente y el desarrollo sostenible, garantizar el correcto funcionamiento del abastecimiento público de agua en ciudades como Catriel, 25 de Mayo, río Colorado y también, eventualmente, en Bahía Blanca. En este trabajo, se analizan series de tiempo de caudales de agua, arriba del embalse y de la planta hidroeléctrica en Buta Ranquil, para modelar y predecir las fluctuaciones diarias. Se comparan los resultados obtenidos mediante el uso de una red neuronal artificial (ANN) de tres capas y un modelo autoregresivo (AR), con 18 años de datos, cuyos últimos 3 años se utilizan para la validación del modelo por medio de la raíz del error cuadrado medio (RMSE) y medida de certeza (Skill). Para predecir o rellenar el caudal diario, los resultados indican que el mejor desempeño es del ANN con respecto al modelo AR.Fil: Pierini, Jorge Omar. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; ArgentinaFil: Gomez, Eduardo Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Tecnologica Nacional; ArgentinaFil: Telesca, Luciano. Istituto di Metodologie per l’Analisi Ambientale; Itali