Geoscience Reference
In-Depth Information
Since both the standard and the evolutionary power spectrum methods
require evenly-spaced data, we interpolate the data to an evenly-spaced time
vector
t
, as demonstrated in Section 5.5.
t = 0 : 3 : 1000;
series3L = interp1(series3(:,1),series3(:,2),t,'linear');
We then compute a non-evolutionary power spectrum for the full length of
the time series (Fig. 5.12). h is exercise helps us to compare the dif erences
between the results of the standard and the evolutionary power spectrum
methods.
[Pxx,f] = periodogram(series3L,[],1024,1/3);
plot(f,Pxx)
xlabel('Frequency')
ylabel('Power')
title('Power Spectrum')
h e auto-spectrum shows signii cant peaks at 100, 40 and 20 kyr cyclicities,
as well as some noise. h e power spectrum, however, does not provide any
Audio
5.3
Fig. 5.11
Synthetic data set containing three main periodicities of 100, 40, and 20 kyrs and
additive Gaussian noise. Whereas the 100 and 20 kyr cycles are present throughout the time
series, the 40 kyr cycle only appears at around 450 kyrs before present.
