Effect of significant data loss on identifying electric signals that precede rupture by detrended fluctuation analysis in natural time

Abstract

Electric field variations that appear before rupture have been recently studied by employing the detrended fluctuation analysis (DFA) as a scaling method to quantify long-range temporal correlations. These studies revealed that seismic electric signals (SES) activities exhibit a scale invariant feature with an exponent ${\alpha }_{DFA}\approx 1$ over all scales investigated (around five orders of magnitude). Here, we study what happens upon significant data loss, which is a question of primary practical importance, and show that the DFA applied to the natural time representation of the remaining data still reveals for SES activities an exponent close to 1.0, which markedly exceeds the exponent found in artificial (man-made) noises. This, in combination with natural time analysis, enables the identification of a SES activity with probability 75% even after a significant (70%) data loss. The probability increases to 90% or larger for 50% data loss.

12 Pages, 11 Figures

References in corpus (6)

• Investigation of the seismicity after the initiation of a Seismic Electric Signal activity until the main shock
• Effect of extreme data loss on long-range correlated and anti-correlated signals quantified by detrended fluctuation analysis
• Entropy in the natural time-domain
• Detrended fluctuation analysis of the magnetic and electric field variations that precede rupture
• Natural entropy fluctuations discriminate similar looking electric signals emitted from systems of different dynamics
• Effect of significant data loss on identifying electric signals that precede rupture by detrended fluctuation analysis in natural time

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