Seismic Data Reconstruction Research Based On Seislet Transform

With the gradual expansion of exploration areas in my country,the targets of oil and gas exploration have gradually shifted to complex structures,stratigraphic and lithological traps,and the target layers of exploration have shifted from medium-shallow layers to deep or ultradeep layers.The requirements for seismic data processing technology are also increasing.Higher.The deep geological conditions are complex,the effective signal energy in the deep part of the seismic data is weak,and the space aliasing phenomenon is serious.By improving the regularity,signal-to-noise ratio,resolution and fidelity of seismic data,it can be used for subsequent migration imaging and full waveform reflection.The interpretation of seismic data and seismic data provides reliable data protection,which is helpful for judging the condition of the target reservoir.In recent years,many scholars have conducted research on compressed sensing.Compressed sensing theory breaks through the traditional Nyquist sampling method.Compressed sensing theory uses the sparseness of the signal itself or the characteristics of sparseness in a certain domain,and then solves the regularization inversion problem to recover the signal with high precision.This article first analyzes the basic principles of compressed sensing and Seislet transform.In the reconstruction of seismic data based on compressed sensing,the convex set projection(POCS)algorithm is a more commonly used seismic data reconstruction algorithm.Its main parameters include threshold function and threshold model..Another key issue of compressed sensing theory is the transform domain.The Seislet transform is a mathematical transformation specifically for seismic data.Seismic data has good sparsity in the Seislet domain.Secondly,in view of the shortcomings of the traditional hard threshold function and soft threshold function,combined with the Riemann-Liouville fractional integral theory,this paper proposes a fractional threshold function.Compared with the hard threshold function,the fractional threshold function enhances the continuity of the threshold point.Compared with the soft threshold function,the fractional threshold function reduces the deviation from the original coefficient.In this paper,a weighting factor is added between the linear threshold model and the data-driven threshold model,and then the weighted threshold model is obtained.In this paper,the fractional threshold function and weighted threshold model are applied to the POCS algorithm in the Seislet domain to obtain an improved POCS algorithm in the Seislet domain.Finally,the improved POCS algorithm in the Seislet domain is applied to the model data and actual seismic data,and the performance tests of the fractional threshold function and weighted threshold model are carried out to verify the effectiveness of the method in this paper.In the Seislet domain model test and real seismic data test,the fractional threshold function can effectively improve the reconstruction accuracy of seismic data compared with the traditional threshold function,while the weighted threshold model can improve the reconstruction efficiency under the premise of ensuring the seismic data.And the accuracy of data reconstruction verify the correctness and effectiveness of the method proposed in this paper,and provide new ideas for improving the regularity,signal-to-noise ratio and fidelity of seismic data.