摘要：Deep and ultra-deep reservoirs, unconventional hydrocarbons, and other complex reservoirs are being developed for oil and gas exploration, high-resolution seismic data with a high signal-to-noise ratio is required for accurate reservoir description. The traditional high-resolution processing techniques, such as the inverse Q-filtering technique based on the stratum attenuation model and the convolution model-based technique, are entirely model-dependent. In this study, we built a deep learning network based on the U-net and suggested a processing technique to boost seismic data resolution. We incorporated ResPath structure into the network and employ a weighted MAE and MS-SSIM combination as the loss function, and added a training strategy to the data processing workflow. Finally, the network is validated using both field data, our suggested network can further minimize the loss of low-frequency components during conventional deep learning high-resolution processing, effectively enhancing the ability to perceive low-frequency seismic data components, the signal-to-noise ratio and resolution of seismic data have both been significantly enhanced.

关键字：U-net; deep learning; pre-training strategy; seismic resolution; data-driven

ISSN号：0963-0651

卷、期、页：卷32期4: 315-336

发表日期：2023-08-01

期刊分区（SCI为中科院分区）：四区

收录情况：SCI（科学引文索引印刷版）,SCIE（科学引文索引网络版）

发表期刊名称：JOURNAL OF SEISMIC EXPLORATION

参与作者：Zhu, Chenghong

通讯作者：李泽钰,王国权

第一作者：陈双全

论文类型：期刊论文

论文概要：李泽钰,王国权,Zhu, Chenghong,陈双全，Enhancing seismic resolution based on U-Net deep learning network，JOURNAL OF SEISMIC EXPLORATION，2023，卷32期4: 315-336

论文题目：Enhancing seismic resolution based on U-Net deep learning network