Site-dependent organization structure of seismic microtremors

Telesca, Luciano; Lovallo, Michele; Alcaz, V.; Ilies, Ion

doi:10.1016/j.physa.2014.11.061

Cited by 4 publications

(1 citation statement)

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“…Tsunamigenic and non-tsunamigenic earthquakes were efficiently separated in the Fisher-Shannon information plane, using FSC . Micro-tremors time series were identified depending on the soil characteristics of the measurement sites (Telesca et al, 2015b). Telesca et al (2015a) proposed a classifier of (non-)tsunamigenic potential of earthquake build on several time series features, including FIM, SEP, FSC.…”

Section: Introductionmentioning

confidence: 99%

Advanced Analysis of Temporal Data Using Fisher-Shannon Information: Theoretical Development and Application in Geosciences

et al. 2020

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Complex non-linear time series are ubiquitous in geosciences. Quantifying complexity and non-stationarity of these data is a challenging task, and advanced complexity-based exploratory tool are required for understanding and visualizing such data. This paper discusses the Fisher-Shannon method, from which one can obtain a complexity measure and detect non-stationarity, as an efficient data exploration tool. The state-of-the-art studies related to the Fisher-Shannon measures are collected, and new analytical formulas for positive unimodal skewed distributions are proposed. Case studies on both synthetic and real data illustrate the usefulness of the Fisher-Shannon method, which can find application in different domains including time series discrimination and generation of times series features for clustering, modeling and forecasting. The paper is accompanied with Python and R libraries for the non-parametric estimation of the proposed measures.

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Section: Introductionmentioning

confidence: 99%