  Extraction of information in the form of oscillations from noisy data of
natural phenomena such as sounds, earthquakes, ionospheric and brain activity,
and various emissions from cosmic objects is extremely difficult. As a method
for finding periodicity in such challenging data sets, the 2D Hybrid approach,
which employs wavelets, is presented. Our technique produces a wavelet
transform correlation intensity contour map for two (or one) time series on a
period plane defined by two independent period axes. Notably, by spreading
peaks across the second dimension, our method improves apparent resolution of
detected oscillations in the period plane and identifies the direction of
signal changes using correlation coefficients. We demonstrate the performance
of the 2D Hybrid technique on a very low frequency (VLF) signal emitted in
Italy and recorded in Serbia in time vicinity of the occurrence of an
earthquake on November 3, 2010, near Kraljevo, Serbia. We identified a distinct
signal in the range 120-130 s that appears only in association with the
considered earthquake. Other wavelets, such as Superlets, which may detect fast
transient oscillations, will be employed in the future analysis.
