基于交互注意力分解融合的地震速度建模  

Seismic Velocity Building Based on Interactive Attention DeMulti Unite

作　　者：李灿伟 吴春雷[1] 路静 王春霖 朱明飞 LI Canwei;WU Chunlei;LU Jing;WANG Chunlin;ZHU Mingfei(College of Computer Science and Technology,China University of Petroleum,Qingdao 266580,China)

机构地区：[1]中国石油大学(华东)计算机科学与技术学院,山东青岛266580

出　　处：《计算机与现代化》2025年第1期7-14,19,共9页Computer and Modernization

基　　金：国家自然科学基金资助项目(42174138);中央高校基本科研业务费专项资金资助项目(20CX05018A);中国石油天然气集团公司重大科技专项(ZD2019-183-001)。

摘　　要：地震波传播速度参数贯穿于整个地震勘探过程,对于地震成像至关重要。传统的地震速度建模方法存在计算效率低等问题,本文提出一种基于交互注意力分解融合的地震速度建模网络,即IADMU模型。本文网络提出分解融合卷积(DMUC)和交互注意力模块(IAB)这2个组件。原始地震炮记录经过分解融合卷积模块,从全局和局部维度提取其高级特征信息,送入反卷积和交互注意力模块,在跨通道交互的帮助下,预测对应的速度模型。在模拟数据和SEG salt data数据集上进行大量实验,消融实验验证了本文提出的DMUC和IAB的有效性;对比实验结果显示,相比U-Net、Res-UNet和DeepLabV3网络,本文网络在这2种数据集上都有更好的性能,验证了本文所提网络的优越性。The velocity parameter of seismic wave runs through the whole seismic exploration process and is very important for seismic imaging.Due to the low computational efficiency of traditional seismic velocity building methods,this paper proposes a seismic velocity building based on interaction attention DeMulti unite,namely IADMU model.Two parts of the network are proposed:DeMulti Unite Convolution(DMUC)and Interactive Attention Block(IAB).The original seismic shot record passes through the DMUC to extract its advanced feature information from the global and local dimensions.The deconvolution and interactive attention modules are then fed to directly predict the corresponding velocity models with the help of cross-channel interactions.This paper conducts a lot of experiments on simulated data and SEG salt data sets,ablation experiment proves the effectiveness of the proposed DMUC and IAB;Comparative experiments demonstrated that,compared to U-Net,Res-UNet,and DeepLabV3 networks,this network exhibited superior performance on both datasets,validating the superiority of the proposed network in this paper.

关 键 词：地震速度建模 分解融合 交互注意力 跨通道交互 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]