Seismic Random Noise Suppression Based On Multi-Scale Mathematical Morphology And Empirical Mode Decomposition

Seismic exploration method has been playing an important role in the study of earth structure,prediction of geological disasters,search for mineral resources,dynamic monitoring of mining areas and so on.The actual seismic data acquisition process will be subject to a variety of interference,almost all data have random noise.Unlike coherent noise,which can be suppressed by propagation law,random noise is widely distributed and has a wide range of frequency bands,which is numerically unpredictable,making it difficult to suppress.With the development of the times,many filtering methods are constantly maturing.In recent years,mathematical morphology method has been introduced into seismic signal processing as a new filtering technology.This method uses the waveform difference between seismic data and random noise to filter,and multi-scale mathematical morphology is a further step.It decomposes the noisy signal at each scale,so that it can accurately express the details of different scales while achieving the filtering effect.Empirical mode decomposition(EMD)decomposes the signal into a series of intrinsic mode functions(IMF)according to the time scale characteristics of the data itself.This method does not need to set any basis function in advance,and has obvious advantages in processing non-stationary and nonlinear data,and has high signal-to-noise ratio.Based on this,this paper focuses on the multi-scale mathematical morphology and empirical mode decomposition method to carry out the research on random noise suppression of seismic data.Firstly,the development process of empirical mode decomposition is reviewed,and the endpoint effect,false component,stop criterion and mode aliasing phenomenon of EMD are displayed,and the suppression methods of these phenomena are introduced.The improvement process from empirical mode decomposition to complementary ensemble empirical mode decomposition(CEEMD)and complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)for suppressing mode aliasing is mainly shown.Secondly,it shows the development of mathematical morphology from binary morphology to gray morphology,from image processing to digital signal processing,and from single scale to multi-scale morphology.In this process,the influence factors such as the shape and size of structural elements are discussed,and the processing effect of multi-scale morphology on seismic data is displayed.At the same time,the multi-scale mathematical morphology filtering is improved based on the improved idea of empirical mode decomposition,and the multi-scale morphological decomposition and reconstruction method is improved by introducing appropriate white noise.Finally,the advantages and disadvantages of empirical mode decomposition,ensemble empirical mode decomposition,complementary ensemble empirical mode decomposition and complete ensemble empirical mode decomposition with adaptive noise are compared and analyzed,and the joint strategy of multi-scale mathematical morphology and empirical mode decomposition is proposed.A series of trial calculations from simple to complex models have proved the effectiveness of the proposed method and its adaptability to complex geological conditions.