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

TL;DR

This study investigates the robustness of detrended fluctuation analysis in identifying seismic electric signals before rupture, demonstrating that key features persist despite significant data loss, thus aiding early earthquake detection.

Contribution

It shows that DFA applied to natural time remains effective in detecting SES activities even after substantial data loss, improving practical earthquake prediction methods.

Findings

DFA exponent remains close to 1 after data loss

SES signals can be identified with 75% probability after 70% data loss

Detection probability exceeds 90% with 50% data loss

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.