Fractal Analysis of Pre-Seismic Electromagnetic and Radon Precursors: A Systematic Approach

Abstract

This paper reports characteristic pre-seismic disturbances of electromagnetic radiation of the MHz range and of radon in soil. All the disturbances were collected in an organised manner from a telemetric network operating in Greece in a period of five distinct years during which a total of thirty seven earthquakes occurred. This study includes thirty-three pre-seismic MHz electromagnetic disturbances with duration of a few days up to one month and four lengthy radon precursors, all recorded prior to earthquakes that occurred in years 2007, 2008, 2009, 2014, 2015 with M L ≥ 5.0. The paper is a systematic investigation of the outputs of the fractal analysis from the thirty seven earthquakes. The results indicated that the majority of the investigated signals (both electromagnetic radiation and radon) exhibited characteristic epochs with fractal organisation. Continuous epochs were detected in several one-month, electromagnetic radiation, signals. As fractal epochs were considered those with successive (r2 ≥ 0.95) power-law behaviour and b - exponent in the fBm class (1 ≤ b ≤ 3). As enhanced precursory fractal epochs were considered the successive fBm ones with many successive (r2 ≥ 0.95) segments above 1.5 with extra attention given to the ones above 2.0. These epochs indicated well-established long-memory dynamics well away from fGn randomness. Several successive ( r2 ≥ 0.95) fractal electromagnetic and radon segments showed anti-persistency (1.5 ≤ b < 2.0). Nevertheless, numerous persistent ( 2.0 < b ≤ 3.0 ) parts were detected. Switching between persistency and anti-persistency was identified. This switching was considered of enhanced precursory value of the electromagnetic and radon signals. The findings indicated selforganised critical state characteristics of the last stages of the investigated earthquakes. It was concluded that the fractal analysis can be employed as a first screening method for the identification of long-memory patterns hidden in pre-seismic time-series. The method is reliable in identifying pre-earthquake patterns.