煤田地质与勘探2024，Vol.52Issue(11)：141-150,10.DOI:10.12363/issn.1001-1986.24.02.0129
准噶尔盆地叠后三维地震资料噪声智能压制
Intelligent noise suppression for 3D post-stack seismic data of the Junggar Basin
摘要
Abstract
[Objective]The Junggar Basin is recognized as a significant petroliferous basin in China,and its hydrocar-bon exploration targets have shifted to deeper strata.However,the 3D seismic data of this basin suffer from low signal-to-noise ratios(SNRs)and high data volumes due to the basin's complex near-surface conditions,the great depths of ex-ploration targets,and seismic data acquisition methods characterized by wide azimuths,broadbands,and high density.This complicates the identification of hydrocarbon exploration targets,rendering the improvement in the quality of the 3D seismic data by noise suppression vitally important.[Methods]The progress in the deep learning theory and the en-hancement of hardware performance have significantly boosted the learning capability and processing efficiency of deep neural networks.Based on residual learning and batch normalization techniques,this study developed a three-dimension-al denoising convolutional neural network(3D-DnCNN)and a deep learning-based noise suppression workflow applic-able to the 3D seismic data of the Junggar Basin.[Results and Conclusions]To meet the actual demand of a large con-tiguous surveyed area in the Junggar Basin,high-quality labels were constructed using the noise suppression results of zones with high seismic coverage and SNRs,and the trained 3D-DnCNN was then applied to the entire study area.Com-pared to the conventional industrial workflow,the workflow developed in this study yielded more consistent seismic events,more intact faults preserved,and clearer top boundary and inner layers of the Carboniferous strata.Additionally,since the 3D-DnCNN learned the characteristics of offset-related arc noise in high-SNR zones,it outperformed the con-ventional industrial workflow in suppressing such noise across the entire surveyed area.By adjusting network paramet-ers such as the network depth,convolution kernel size,and the strategy for selecting training samples,the 3D-DnCNN can be further optimized to adapt to seismic data from different areas,thereby enhancing the applicability and effective-ness of the seismic noise suppression technique.关键词
准噶尔盆地/深度学习/卷积神经网络/噪声压制Key words
Junggar Basin/deep learning/convolutional neural network/noise suppression分类
天文与地球科学引用本文复制引用
毛海波,周鑫,李晓峰,潘龙,林娟,刘达伟,王晓凯..准噶尔盆地叠后三维地震资料噪声智能压制[J].煤田地质与勘探,2024,52(11):141-150,10.基金项目
国家油气重大专项课题(2023ZZ14YJ05) （2023ZZ14YJ05）
国家自然科学基金面上项目(42374135) （42374135）
