Deep Stochastic Inversion

L. Mosser, O. Dubrule, M. Blunt
{"title":"Deep Stochastic Inversion","authors":"L. Mosser, O. Dubrule, M. Blunt","doi":"10.3997/2214-4609.201902199","DOIUrl":null,"url":null,"abstract":"Summary Numerous geophysical tasks require the solution of ill-posed inverse problems where we seek to find a distribution of earth models that match observed data such as reflected acoustic waveforms or produced hydrocarbon volumes. We present a framework to create stochastic samples of posterior property distributions for ill-posed inverse problems using a gradient-based approach. The spatial distribution of petrophysical properties is created by a deep generative model and controlled by a set of latent variables. A generative adversarial network (GAN) is used to represent a prior distribution of geological models based on a training set of object-based models. We minimize the mismatch between observed ground-truth data and numerical forward-models of the generator output by first computing gradients of the objective function with respect to grid-block properties and using neural network backpropagation to obtain gradients with respect to the latent variables. Synthetic test cases of acoustic waveform inversion and reservoir history matching are presented. In seismic inversion, we use a Metropolis adjusted Langevin algorithm (MALA) to obtain posterior samples. For both synthetic cases, we show that deep generative models such as GANs can be combined in an end-to-end framework to obtain stochastic solutions to geophysical inverse problems.","PeriodicalId":186806,"journal":{"name":"Petroleum Geostatistics 2019","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Petroleum Geostatistics 2019","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3997/2214-4609.201902199","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Summary Numerous geophysical tasks require the solution of ill-posed inverse problems where we seek to find a distribution of earth models that match observed data such as reflected acoustic waveforms or produced hydrocarbon volumes. We present a framework to create stochastic samples of posterior property distributions for ill-posed inverse problems using a gradient-based approach. The spatial distribution of petrophysical properties is created by a deep generative model and controlled by a set of latent variables. A generative adversarial network (GAN) is used to represent a prior distribution of geological models based on a training set of object-based models. We minimize the mismatch between observed ground-truth data and numerical forward-models of the generator output by first computing gradients of the objective function with respect to grid-block properties and using neural network backpropagation to obtain gradients with respect to the latent variables. Synthetic test cases of acoustic waveform inversion and reservoir history matching are presented. In seismic inversion, we use a Metropolis adjusted Langevin algorithm (MALA) to obtain posterior samples. For both synthetic cases, we show that deep generative models such as GANs can be combined in an end-to-end framework to obtain stochastic solutions to geophysical inverse problems.
深度随机反演
许多地球物理任务需要解决不适定逆问题,在这些问题中,我们寻求找到与观测数据(如反射声波波形或产出的碳氢化合物体积)相匹配的地球模型分布。我们提出了一个框架,使用基于梯度的方法为病态逆问题创建后验性质分布的随机样本。岩石物性的空间分布由深层生成模型生成,并由一组潜在变量控制。在基于对象的地质模型训练集的基础上,使用生成对抗网络(GAN)来表示地质模型的先验分布。我们通过首先计算目标函数相对于网格块属性的梯度,并使用神经网络反向传播来获得相对于潜在变量的梯度,从而最大限度地减少观测到的真实数据与发电机输出的数值正演模型之间的不匹配。给出了声波波形反演和储层历史拟合的综合测试案例。在地震反演中,我们使用Metropolis - adjusted Langevin算法(MALA)获得后验样本。对于这两种综合情况,我们表明深度生成模型(如gan)可以在端到端框架中组合,以获得地球物理逆问题的随机解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信