Geoscience Reference
In-Depth Information
information about l uctuations in the amplitudes of these peaks. h e non-
evolutionary power spectrum simply represents an average of the spectral
information contained in the data.
We now use the function
spectrogram
to map the changes in the power
spectrum with time. By default, the time series is divided into eight segments
with a 50% overlap. Each segment is windowed with a Hamming window
to suppress spectral leakage (Section 5.3). h e function
spectrogram
uses
similar input parameters to those used in
periodogram
in Section 5.3. We then
compute the evolutionary power spectrum for a window of 64 data points
with a 50 data point overlap. h e STFT is computed for
nfft
=256. Since the
spacing of the interpolated time vector is 3 kyrs, the sampling frequency is
1/3 kyr
-1
.
spectrogram(series3L,64,50,256,1/3)
title('Evolutionary Power Spectrum')
xlabel('Frequency (1/kyr)')
ylabel('Time (kyr)')
colormap(jet)
Fig. 5.12
Power spectrum for the complete time series. showing signii cant peaks at 100, 40
and 20 kyrs. h e plot, however, does not provide any information on the temporal behavior
of the cyclicities.
