石油地球物理勘探2024，Vol.59Issue(4)：702-713,12.DOI:10.13810/j.cnki.issn.1000-7210.2024.04.006
有监督深度学习的地震资料提高分辨率处理方法
High-resolution seismic data processing method based on supervised deep learning
摘要
Abstract
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.关键词
有监督深度学习/多尺度结构相似性损失/L1损失/生成对抗网络/图像超分辨率重建Key words
supervised deep learning/multi-scale structural similarity loss(MS-SSIM)/L1 loss function/genera-tive adversarial network/image super-resolution reconstruction分类
天文与地球科学引用本文复制引用
李斐,牛文利,刘达伟,王永刚,黄研..有监督深度学习的地震资料提高分辨率处理方法[J].石油地球物理勘探,2024,59(4):702-713,12.基金项目
本项研究受国家自然科学基金面上项目"基于频率空间域信号子空间优化的叠前地震资料噪声压制方法"(42374135)和中国石油集团重大专项"致密砂岩气藏提高采收率关键技术研究"(2023ZZ25)联合资助. （42374135）
