Research On Separation Method Of Blended Data Using Seislet Domain

Simultaneous-source seismic data acquisition is an efficient seismic acquisition technology and a major innovation in traditional single-shot acquisition technology.This new acquisition method can greatly improve the collection efficienc y and significantly reduce the acquisition cost.However,since multiple sources are excited at the same time or with a certain delay,the seismic records overlap in time.If direct imaging introduces severe crosstalk noise on the imaging profile,the imaging is poor.This can be avoided by sources separation for Simultaneous-source blended data.Multi-seismic blended data separation does not require changes to subsequent conventional seismic data processing flows,and can provide reliable data for subsequent processing.Most of the multi-source acquisition uses the time delay coding excitation method,so that the seismic record is in the non-common shot field(common point domain,common offset or common receiver domain,etc.),the effective signal is continuous and aliased.The noise is discrete so that the blended data can be separated from the filtering perspective.And because in the multi-source acquisition process,the receiver is recorded at the same time,the number of excited sources is greater than the number of seismic records,so the separation of multiple source data can also be regarded as an underdetermined inversion problem.In this paper,the research status and development trend of multi-seismic aliasing data separation at home and abroad are summarized.The separation method of multi-source blended data is discussed,including the separation method based on filtering idea and the separation method based on inversion idea.The method of data separation effect.Finally,based on the advantage of seismic data in the Seislet domain,this paper uses the idea of shaping regularization to combine the improved Bregman iterative algorithm with the threshold algorithm to separate the blended data.The experimental results show that this method can improve the accuracy and effectiveness of blended data separation.