Seismic random noise attenuation based on empirical mode decomposition of fractal dimension

Empirical mode decomposition (EMD) is a noise suppression algorithm by using wave field separation, which is based on the scale differences between effective signal and noise. However, because the complexity of the real seismic wave field can result in serious aliasing modes, it is not ideal and effective to denoise using this method alone. Based on the multi-scale decomposition characteristics of the EMD algorithm for signal, combining with Hausdorff dimension constraints, we propose a new method for seismic random noise attenuation. Firstly, we apply EMD algorithm adaptive decomposition of seismic data to obtain a series of IMF components with different scales. On this basis, based on the difference of Hausdorff dimension between effective signals and random noise, we identify IMF component mixed with random noise. Then we use the threshold correlation filtering process to separate the valid signal and random noise effectively. This method includes three steps, i.e. simulation signal experiment, the seismic model data processing and real seismic data processing. Compared with traditional EMD method, this new method of seismic random noise attenuation has a better suppression effect.