Turbulent-Like Behavior of Seismic Time Series

TL;DR

This paper applies turbulence analysis methods to seismic data, revealing changes in statistical properties that can serve as precursors for earthquake detection within 5-10 hours.

Contribution

It introduces a novel stochastic analysis approach for seismic time series based on turbulence methods, identifying a potential earthquake precursor.

Findings

PDF shape changes before earthquakes

Long-range correlations in seismic data

Transition time of 5-10 hours as a precursor

Abstract

We report on a novel stochastic analysis of seismic time series for the Earth's vertical velocity, by using methods originally developed for complex hierarchical systems, and in particular for turbulent flows. Analysis of the fluctuations of the detrended increments of the series reveals a pronounced change of the shapes of the probability density functions (PDF) of the series' increments. Before and close to an earthquake the shape of the PDF and the long-range correlation in the increments both manifest significant changes. For a moderate or large-size earthquake the typical time at which the PDF undergoes the transition from a Gaussian to a non-Gaussian is about 5-10 hours. Thus, the transition represents a new precursor for detecting such earthquakes.