Random noise attenuation by f-x spatial projection-based complex empirical mode decomposition predictive filtering

Abstract

The frequency-space (f-x) empirical mode decomposition (EMD) denoising method has two limitations when applied to nonstationary seismic data. First, subtracting the first intrinsic mode function (IMF) results in signal damage and limited denoising. Second, decomposing the real and imaginary parts of complex data may lead to inconsistent decomposition numbers. Thus, we propose a new method named f-x spatial projection-based complex empirical mode decomposition (CEMD) prediction filtering. The proposed approach directly decomposes complex seismic data into a series of complex IMFs (CIMFs) using the spatial projection-based CEMD algorithm and then applies f-x predictive filtering to the stationary CIMFs to improve the signal-to-noise ratio. Synthetic and real data examples were used to demonstrate the performance of the new method in random noise attenuation and seismic signal preservation.