The collected seismic data contains various noises,which leads to the low signal-to-noise ratio of seismic data,which brings great difficulties to the subsequent interpretation work.In order to understand underground geological information more accurately,it is necessary to improve the signal-to-noise ratio.In response to the random noise in seismic data,three methods were used:mirror symmetric extension,boundary local feature scale extension,and polynomial fitting.The extreme points were found in the com-posite signal of 60 Hz sine signal and white noise,and the mean was calculated to obtain the intrinsic mode function.After reconstructing the seismic signal,the seismic noise was effectively suppressed.The results show that the end effect of seismic signals can be eliminated and aliasing can be suppressed effectively by u-sing the three methods.The similarity coefficient obtained by the polynomial fitting is the largest,while by the boundary local feature scale extension is the smallest,and the average relative error is the smallest.The boundary local feature scale extension is the fastest,while the polynomial fitting takes longer time.The mir-ror symmetric extension and the boundary local feature scale extension methods not only effectively suppress the endpoint effect in traditional EMD decomposition,but also increase the orthogonality of the IMF obtained from EMD decomposition,improving the accuracy of EMD decomposition.
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