Identifying long-range correlated signals upon significant periodic data loss

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).

Research Highlights
► SES activities are identified even after severe data loss due to man made noise.
► SES can be recognized in Japan where noise from DC trains lasts 70% of the 24h.
► SES recognition is made after data loss by combining natural time analysis and DFA.