Seismic Spectral Inversion Based On A Compressed Sensing Algorithm

With the increasing degree of oil and gas exploration,seismic exploration tasks are also facing more complicated situations.The prediction of thin reservoir oil and gas reservoirs becomes more and more important.The accuracy of thin reservoirs with thicknesses less than the traditional resolution of seismic data Forecasting has become one of the key points in the field of seismic exploration.Based on this actual situation,the paper studied in detail the spectral inversion method of seismic data to improve resolution processing.Spectral inversion is a new processing technology developed in recent years to improve the resolution of seismic data.The basic principle is to establish a target function between the calculated and actual reflection coefficient sequence spectrum by selecting a reasonable frequency interval in the frequency domain and inverting the size and position of the reflection coefficient.In spectral inversion,it is necessary to input the spectrum of seismic wavelet.Therefore,the estimation of seismic wavelet spectrum is an important prerequisite for spectral inversion.In the process of spectral inversion of actual seismic data,the estimation of seismic wavelet spectrum has always been a difficult point,especially for seismic wavelets with time and space changes.Therefore,how to extract accurate seismic wavelet spectrum to ensure the accuracy of spectral inversion results is also very important.The time thickness of the thin layer is expressed by the sampling interval of the seismic data,and the top and bottom interfaces of the thin layer are expressed by two adjacent sampling points.Based on the parity decomposition theorem,the objective function of the spectral inversion method is detailedly pushed to,And the corresponding spectral inversion objective function matrix equations are obtained.The thesis uses orthogonal matching tracking algorithm(Orthogonal Matching Pursuit)and basic tracking algorithm(Basis Pursuit)in the compressed sensing theory to solve the spectral inversion objective function,and finally obtains the reflection coefficient of the entire time period,and each sample point has a numerical value.Represents the reflection coefficient,and a value of zero means that there is no reflection coefficient.Under the assumption of sparse strata,the spectrum inversion method is realized by obtaining the seismic data spectrum and seismic wavelet spectrum of some frequency bands.The two algorithms are compared and considered,and the base tracking algorithm is selected as the actual seismic data to improve the resolution.The paper proposes to use the secondary spectrum of seismic data to estimate the seismic wavelet spectrum to calculate the seismic wavelet spectrum,and use the model and actual seismic data to test the wavelet spectrum estimation.The results show that the seismic wavelet extraction method based on the secondary spectrum of seismic records has Very high accuracy.The test of synthetic data and the application of actual seismic data prove the effectiveness of the method.The thesis combines the method of estimating seismic wavelet spectrum using the secondary spectrum of seismic data to form a spectral inversion method and data processing flow based on the compressed sensing algorithm.Finally,the proposed method is used to perform frequency extension processing on the seismic data of the actual work area.By comparing and processing the previous seismic data spectrum curves,it can be seen that a good extension frequency processing effect has been achieved.While the resolution of seismic data has been significantly improved,the signal-to-noise ratio of seismic data processed by frequency extension can also be well maintained.