High resolution seismic data processing is the key to obtaining high-quality seismic data and achieving good seismic geological interpretation of thin reservoirs.The traditional methods for impro-ving seismic resolution have strict application conditions,complex calculation of key parameters,and are subject to many limitations in practical applications.The U-net network in deep learning has the ad-vantage of pure data-driven learning,which can learn the nonlinear relationship from low resolution seis-mic records to high-resolution labels,achieving high-resolution processing of seismic data.This article designs a residual U-net network structure and proposes a method for generating a set of identically dis-tributed reflection coefficients based on probability density function control.The probability density function of logging reflection coefficients is incorporated into the training samples as a prior constraint information,which not only ensures sufficient identically distributed samples for training the network,but also ensures that the training samples are more in line with the actual situation of the work area,thereby improving the accuracy of model prediction.The results of model testing and practical data ap-plication show that the method proposed in this paper can effectively improve the resolution of seismic data and broaden the frequency band.
关键词
提高分辨率/U-net/残差结构/同分布
Key words
resolution enhancement/U-net/residual structure/identically distribution
维普
万方数据