Extracting preseismic electric signals from noisy Earth's electric field data recordings. The "noise injection" method

TL;DR

This paper introduces a novel 'noise injection' method utilizing Dirac Delta Functions to extract preseismic electric signals from noisy data, tested on real earthquake data from Greece and Turkey.

Contribution

The paper presents a new noise injection technique that improves detection of preseismic signals without traditional filtering, using convergence criteria across multiple sites.

Findings

Successfully identified preseismic signals before earthquakes in Greece and Turkey

Demonstrated effectiveness of the noise injection method on real noisy data

Provided a new approach for seismic signal extraction without conventional filtering

Abstract

An entirely different approach is used for the extraction of preseismic electric signals from highly contaminated by noise data series. The basic element of this method is the "Dirac Delta Function". Noise is injected, instead of applying any known method of filtering, in the data series at different amplitude levels (p value) and the generated "family" of filtered data series is compared to different convergence criteria. In the case of preseismic electric signals identification the most appropriate (p) value is selected by testing the convergence of generated intersections of more than three "families" of filtered data which were generated from more than three monitoring sites. The "noise injection" method was tested against real data recorded long before two large earthquakes in Greece and one in Turkey.