# Identification of Seismic Electric Signals upon significant data loss

## TL;DR

This paper demonstrates that seismic electric signals can still be identified in noisy geophysical data segments by analyzing the remaining noise-free data using natural time and detrended fluctuation analysis, despite significant data loss.

## Contribution

It introduces a method to detect seismic electric signals in contaminated data segments using natural time and DFA, even with substantial data loss.

## Key findings

- Seismic electric signals can be identified despite 70% data loss.
- Natural time and DFA effectively analyze noise-free data segments.
- Method applicable to noisy geophysical data with significant contamination.

## Abstract

When monitoring geophysical parameters, data from segments that are contaminated by noise may have to be abandoned. This is the case, for example, in the geoelectrical field measurements at some sites in Japan, where high noise -due mainly to leakage currents from DC driven trains- prevails almost during 70\% of the 24 hour operational time. We show that even in such a case, the identification of seismic electric signals (SES), which are long-range correlated signals, may be possible, if the remaining noise free data are analyzed in natural time along with detrended fluctuation analysis (DFA).