石油地球物理勘探2024，Vol.59Issue(4)：664-674,11.DOI:10.13810/j.cnki.issn.1000-7210.2024.04.002
基于改进pix2pix GAN的多次波压制算法
Multiple attenuation algorithm based on improved pix2pix GAN network
摘要
Abstract
The effective attenuation of seismic multiples plays a crucial role in the seismic data processing work-flow.Despite the existence of numerous multiple attenuation methods,traditional approaches heavily rely on prior geological structure information and require extensive calculations,resulting in slow attenuation speed.This poses an even greater challenge for multiple attenuation under complex geological conditions.To over-come the limitations of traditional methods and improve efficiency,this paper applies the pix2pix GAN network to the problem of multiple attenuation and utilizes the feature learning capability of neural networks to improve the processing speed.It proposes an enhanced multiple attenuation method for the pix2pix GAN network,which integrates ResNet and U-Net as the network generator to avoid gradient vanishing or exploding phenomena used by deep netwoorks,while incorporating the SE attention mechanism.The improved generator can better per-ceive the characteristics of both first-order and multiples,thereby enhancing its performance.Additionally,a multi-scale discriminator is employed to discern detailed features and texture information on finer seismic images for accurate identification of authenticity.The input data for the network consists of full wave field data labeled as primary wave data,with training conducted using a dataset synthesized from two simple formation models and a public Sigbee2B model.Experimental results demonstrate that the improved GAN network exhibits superior accu-racy in multiple attenuation compared to pix2pix GAN,effectively improving attenuation speed.关键词
多次波消除/深度学习/注意力机制/ResNet/Sigbee2BKey words
multiple attenuation/deep learning/attention mechanism/ResNet/Sigbee2B分类
天文与地球科学引用本文复制引用
张全,吕晓雨,雷芩,黄懿璇,彭博,李艳..基于改进pix2pix GAN的多次波压制算法[J].石油地球物理勘探,2024,59(4):664-674,11.基金项目
本项研究受油气藏地质及开发工程国家重点实验室开放基金项目"石油钻井环境异常工况智能识别技术研究"(PLN2022-51)、"基于高性能计算与卷积神经网络的地震多次波压制方法研究"(PLN2021-21)和四川省南充市科技局开放基金项目"地震多次波高效压制与深度学习集成研究"(23XNSYSX0089)联合资助. （PLN2022-51）
