predictability in the spatial direction. In reality, seismic events do not follow Canales’s assumptions exactly. They are spatially nonstationary. The “signal” is no longer mapped to a superposition of simple harmonics but rather a superposition of nonstationary ones. Empirical mode decomposition (EMD) method (Huang et al., 1998) is used for analyzing nonlinear and nonstationary data. Since the recorded seismic data are usually nonlinear and nonstationary this method can provide us with a powerful device for data analysis and data processing (Battista et al., 2007). One of the applications of this method is to design data-adaptive filters for the reduction of seismic random noise (Bekara and van der Baan, 2009). For many data sets, the random noise makes a significantly larger contribution to the high-wave number energy in the f-x domain than any desired signal. Empirical mode decomposition decomposes a data series into a finite set of signals called intrinsic mode functions (IMFs). They represent the different oscillations embedded in the data. The first IMF represents the fastest oscillations in the data, i.e., it contains the largest wave number components in a constant-frequency slice in the f-x domain. Therefore, signal-to-noise enhancement can be achieved by subtracting first IMF from the data.


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