基于生成对抗网络的半监督地震波阻抗反演  

Semi-supervised Seismic Wave Impedance Inversion Based on Generative Adversarial Networks

作　　者：王永昌 刘彩云[2] 熊杰[1] 王康 胡焕发 康佳帅 WANG Yongchang;LIU Caiyun;XIONG Jie;WANG Kang;HU Huanfa;KANG Jiashuai(School of Electronic Information,Yangtze University,Jingzhou,Hubei 434023,China;School of Information and Mathematics,Yangtze University,Jingzhou,Hubei 434023,China)

机构地区：[1]长江大学电子信息学院,湖北荆州434023 [2]长江大学信息与数学学院,湖北荆州434023

出　　处：《现代地质》2024年第6期1585-1593,共9页Geoscience

基　　金：国家自然科学基金项目(62273060,61673006);长江大学大学生创新创业项目(Yz2022055)。

摘　　要：深度学习波阻抗反演通常需要大量的标记数据对网络进行训练,然而在实际应用中标记数据(测井数据)往往是有限的,针对此问题,本文提出了一种基于生成对抗网络(GAN)的半监督地震反演新方法。该方法采用有条件的GAN(cGAN)改进传统GAN网络结构,重新设计U型卷积神经网络(Unet)的生成器和残差网络(Resnet)的判别器,并采用Wasserstein GAN(WGAN)构建新的目标函数。网络训练分两个阶段,先用少量标记数据训练判别器,再用少量标记数据和大量未标记数据训练生成器,其中生成器受到正演褶积模型约束。合成数据实验结果表明,本文提出的方法适用于少量标记数据的波阻抗反演问题,可以准确反演出波阻抗模型,且具有较好的抗噪性能;实测资料反演结果表明本方法具有较好的实用性。该方法对解决地震波阻抗反演中标记数据少的问题提供了新的参考方法,具有较好的实际应用前景。Deep learning impedance inversion usually requires a large amount of labeled data for network training.However,in practical applications,labeled data(well logging data)is often limited.To address this issue,this paper proposes a new semi-supervised seismic inversion method based on Generative Adversarial Networks(GANs).The method improves the traditional GAN network structure by using a conditional GAN(cGAN),redesigning the generator with a Unet structure and the discriminator with a Resnet structure,and employing Wasserstein GAN(WGAN)to construct a new objective function.The network training is divided into two stages:first,training the discriminator with a small amount of labeled data,then,training the generator with a small amount of labeled data and a large amount of unlabeled data,where the generator is constrained by the forward convolutional model.Experimental results on synthetic data demonstrate that the proposed method is suitable for impedance inversion with limited labeled data,accurately reconstructs impedance models,and exhibits good noise resistance.Inversion results on real field data also indicate the practicality of this method.This method offers a novel approach for addressing the challenge of limited labeled data in seismic wave impedance inversion,holding promising prospects for practical applications.

关 键 词：深度学习 地震波阻抗反演 生成对抗网络 半监督学习 

分 类 号：P631.4[天文地球—地质矿产勘探]