Discussion On The Application Of Compressed Sensing Method In Seismic Data

With the constantly increasing of the energy exploitation and the steeply growth of the complexity of the reserved geology,the technology of seismic exploration that is the most fundamental in the prospecting needs much progress.The more complex geological environment in the exploration,the higher quality of seismic data are required.However,because of some factors influence data acquisition,the seismic data we collected is not complete.Data resolution is often lower than the Nyquist frequency,which for band-limited signals,if the sampling frequency is not less than twice the maximum frequency of the target signal,the original signal can be fully retained,otherwise there will be aliasing.This phenomenon seriously affects the quality of seismic data.It is difficult to accurately predict the reservoir.In the field of modern signals,the development of compressive sensing theory provides a new research tool to solve the problem of undersampling in seismic signals.According to the theory of compressive sensing,under the certain conditions,even the unsampling data may be recovered to meet certain accuracy requirements.Different from the Nyquist theorem,the sparse transformation is made in some transform domain through the sparse characteristic of data,while collecting and storing data.Finally,the complete data is obtained from the small amount of data that contain structural features.In terms of low sampling rate or lack of seismic data obtained in the poor environment,this article has weakened the aliasing in rule sampling of seismic data,based on the methods of the Improved-Morlet wavelet transform.At the same time using the generalized orthogonal matching(g-OMP)algorithm to solve the problem of data reconstruction.The reconstruction effect is more obviously improved than by using a single transformation base or general matching algorithm.In addition,aiming at the problem of seismic data resolution,we sparse representation and reconstruction of the high frequency missing signal in the effective frequency by using the iterative hard threshold(IHT)reconstruction algorithm.It improves the resolution of seismic data and provides the possibility to improve the quality of seismic data.We has tested to recover the high accuracy of the target layer by a small amount of data,while identifying the general distribution of the ground mass.This shows that the method is effective in practical application.It is of great significance to further explore the conditions of restoration with the sparse function in the geological model,the reconstruction effect analysis and the correlation.