有监督深度学习的地震资料提高分辨率处理方法OA北大核心CSTPCD

The resolution of seismic data directly influences the subsequent processing and interpretation preci-sion,thus attracting considerable attention.Deep learning is widely used in solving reverse problems due to its capacity for automatic extraction of deep features and excellent nonlinear approximation.In the field of seismic exploration,the convolution operators in deep convolutional networks are consistent with the convolutional model of seismic data,which has the potential to significantly improve the resolution of seismic data through in-telligent means.Currently,enhancing the resolution of seismic data through convolutional neural networks has become a research hotspot.The key to addressing this issue lies in designing suitable and effective network structures and loss functions for resolution enhancement.Therefore,a high-resolution seismic data processing method based on strong supervised deep learning is proposed.Drawing inspiration from image super-resolution reconstruction,this method makes full use of the spatial continuity of the underground structure,and a genera-tive adversarial network structure is designed to enhance the longitudinal resolution of seismic data.Additionally,a loss function combining L1 loss and multi-scale structural similarity loss is employed to improve the perceived quality of deep learning networks.The experimental results of seismic data and actual seismic data show that compared to the conventional loss function,the loss function presented in this study can significantly enhance the high-resolution processing performance of intelligent algorithms.It notably improves the continuity of the seismic events and enriches the high-frequency detail information of seismic data,and the feasibility and effec-tiveness of the proposed method are verified.