Geoscience Reference
In-Depth Information
(Sections 5.3 and 5.4). We must i rst load the two time series:
clear
series1 = load('series1.txt');
series2 = load('series2.txt');
Both synthetic data sets contain a two-column matrix with 339 rows. h ei rst
column contains ages in kiloyears, which are unevenly spaced. h e second
column contains oxygen-isotope values measured on calcareous micro-
fossils (foraminifera). h e data sets contain 100, 40 and 20 kyr cyclicities
and they are overlain by Gaussian noise. In the 100 kyr frequency band, the
second data series has shit ed by 5 kyrs with respect to the i rst data series.
To plot the data we type
plot(series1(:,1),series1(:,2))
figure
plot(series2(:,1),series2(:,2))
h e statistics for the spacing of the i rst data series can be computed by
intv1 = diff(series1(:,1));
plot(intv1)
h e plot shows that the spacing varies around a mean interval of 3 kyrs, with
a standard deviation of ca. 1 kyr. h e minimum and maximum values for the
time axis
min(series1(:,1))
max(series1(:,1))
of
t
min
=0 and
t
max
=997 kyrs provide some information about the temporal
range of the data. h e second data series
intv2 = diff(series2(:,1));
plot(intv2)
min(series2(:,1))
max(series2(:,1))
has a similar range, from 0 to 997 kyrs. We see that both series have a mean
spacing of 3 kyrs and range from 0 to ca. 1000 kyrs. We now interpolate the
data to an evenly-spaced time axis. While doing this, we follow the rule that
the number of data points should not be increased. h e new time axis runs
from 0 to 996 kyrs, with 3 kyr intervals.
t = 0 : 3 : 996;
