Journal: Scientific Reports
Article Title: New insights and best practices for the successful use of Empirical Mode Decomposition, Iterative Filtering and derived algorithms
doi: 10.1038/s41598-020-72193-2
Figure Lengend Snippet: Boundary problems Real Life Example. Left: Adapted from Sarlis et al. paper . EMD decomposition of the magnitude time-series of GCMT collected from 1 January 1976 to 1 October 2014. The red boxes highlight artifact wave peaks at the boundaries of the IMFs, while the blue asterisks pinpoint anomalous IMFs amplitudes (larger than the original signal). Right: Decomposition of the GCMT magnitude time-series from January 1st 1976 to October 1st 2014 produced using the EEMD function released on March 04 2009 by Zhaohua Wu. The red line in each panel represents the zero reference line. Total computational time: 213.9069 s.
Article Snippet: The global earthquake magnitude time series is shown in the top row of Fig. . We run the decomposition of this signal using both the EMD algorithm, included in matlab distribution 2018a and later versions, and the eemd algorithm (it can be downloaded from the official website of the Taiwanese Research Center for Adaptive Data Analysis https://in.ncu.edu.tw/~ncu34951/research1.htm and is contained in the repository https://in.ncu.edu.tw/~ncu34951/Matlab_runcode.zip ) written by Zhaohua Wu in 2009 .
Techniques: Produced