Linear and nonlinear dynamics in electrical precursory time series: implications for earthquake prediction

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Abstract The time dynamics of geoelectrical precursory time series has been investigated using autoregressive models. The approach used here allows us to obtain information on the geophysical system producing the electrical phenomena in seismic active areas when the only information about the time series comes from the time series themselves. It is based on two forecasting approaches: the global autoregressive approximation and the local autoregressive approximation. The first approach views the data as a realization of a linear stochastic process, whereas the second approach considers the data points as the results of a deterministic process, supposedly nonlinear. The comparison of the predictive skills of the two techniques is a strong test to distinguish between low-dimensional chaos and random dynamics. Our findings give us insight in the inner dynamics of the geophysical process under study: an estimate of the number of degrees of freedom of the dynamical system governing the electrical phenomena is obtained. The stochastic nature of the electrical precursory signals has been highlighted and the possible implications for the earthquake prediction problem are discussed. The processed time series are geoelectrical measurements recorded by an automatic station located in Tito (Southern Italy), which is one of the most seismic areas of the Mediterranean region. We found that the global (linear) approach is superior to the local one, and the physical system governing the phenomena of electrical nature is characterized by a large number of degrees of freedom. The results are confirmed by the presence of rich scaling properties in the power spectra of the processed electrical signals.