Comparing the applications of EMD and EEMD on time–frequency analysis of seismic signal

The Hilbert–Huang transform (HHT) is a novel signal analysis method in seismic exploration. It integrates empirical mode decomposition (EMD) and classical Hilbert transform (HT), which can express the intrinsic essence using simple and understandable algorithm. But there is a serious mode mixing phenomenon in EMD. To solve the mode mixing problem, a noise‐assisted data analysis method called ensemble empirical mode decomposition (EEMD) is adopted instead of EMD. In this paper, the applications of EMD and EEMD on time–frequency analyzing behaviors were compared, and the results show that (1) EMD decomposes an original nonlinear and non-stationary signal into a series of simple intrinsic mode functions (IMFs), but with the mode mixing phenomenon. (2) EEMD skillfully solves the mode mixing problem by adding a white noise to the original signal. (3) The synthetic signal example reveals the remarkable ability of EEMD to decompose the signal into different IMFs and analyze the time–frequency distribution of the signal. (4) The time–frequency spectrum obtained by EEMD more realistically reflects the real geology than by EMD.

► EMD decomposes a complex original signal to a series of simple IMFs.
► EEMD skillfully solves the mode mixing phenomenon.
► EEMD accurately decomposes the synthetic signal example into simple IMFs.
► EEMD exactly analyzes and reflects the time–frequency distribution of the signal.