4.7.13 Evolutionary Correlation Coefficient (eCOCO) - mingsongli/acycle GitHub Wiki

The method is applied using a sliding stratigraphic window to track variable sedimentation rates along the proxy series, in a procedure termed “eCOCO” (evolutionary correlation coefficient) analysis. (Li et al., 2018c)

Waning: the data series must have a unit in meter.

Step 1: same as that in COCO.

Step 2: same as that in COCO.

Step 3: most parameters are the same as those in COCO (see above).

Two new parameters:

DATA: running window (m): default window is 35% of the total length of the data series.

DATA: Number of steps (#): sliding steps. The default value will give about ~300 sliding windows for publication quality results.

Click the OK button, Monte Carlo simulation steps can be displayed in the Command Window of MatLab.

A log file and the related *.AC.fig file will be generated recording all parameters used in the evolutionary correlation coefficient analysis.

The user needs to decide which figure output should be saved or not.

Tips: Users may save the main window using “File”  “save ac.fig” menu anytime. This will save the data stored in the main window figure, and the user doesn’t have to re-run the eCOCO using the same parameters.

Tips: User can plot eCOCO results anytime at “Plot” --> “ECOCO plot” menu.

Q: Which window size should I use?

A: A window that covers 1.5-2 * long eccentricity cycles will give a reliable result. If your series is dominated by 35 m cycles (405 kyr), then a 70 m window (= 35 * 2) may be good to keep the balance: A large window eCOCO losses resolution of variable sedimentation rates and a small window may not give correct results.