Multifractal variability in self-potential signals measured in seismic areas
Luciano Telesca, Vincenzo Lapenna, Maria Macchiato, 2006. "Multifractal variability in self-potential signals measured in seismic areas", Fractal Analysis for Natural Hazards, G. Cello, B. D. Malamud
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Multifractal variability in the time dynamics of geoelectrical data, recorded in a seismic area of southern Italy, was studied by means of Multifractal Detrended Fluctuation Analysis (MF-DFA), which allows the detection of multifractality in nonstationary signals. Our findings show that the multifractality of the geoelectrical time series recorded in the study area is mainly due to the different long-range correlations for small and large fluctuations. Furthermore, the singularity spectrum has led to a better description of the signal, revealing a clear enhancement of its degree of multifractality (measured by the variation of the standard deviation of the generalized Hurst exponents h(q) or the Hölder exponents α) in association with the occurrence of the largest earthquake. However, in order to assess significant correlations between large earthquakes and patterns of multifractal parameters, an investigation of data sets covering longer periods and different seismotectonic environments is needed. The study also furnishes details on our approach for investigating the complex dynamics of earthquake-related geoelectrical signals.
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Fractal Analysis for Natural Hazards
In the Earth sciences, the concept of fractals and scale invariance is well recognized in many natural objects. However, the use of fractals for spatial and temporal analyses of natural hazards has been less used (and accepted) in the Earth sciences. This book brings together 12 contributions that emphasize the role of fractal analyses in natural hazard research, including andslides, wildfires, floods, catastrophic rock fractures and earthquakes. A wide variety of spatial and temporal fractal-related approaches and techniques are applied to ‘natural’ data, experimental data and computer simulations. These approaches include probabilistic hazard analysis, cellular-automata models, spatial analyses, temporal variability, prediction and self-organizing behaviour. The main aims of this volume are (a) to present current research on fractal analyses as applied to natural hazards and (b) to stimulate the curiosity of advanced Earth science students and researchers in the use of fractals analyses for the better understanding of natural hazards.