A CNN-accelerated workflow for stochastic seismic property estimation

工作流程 财产(哲学) 计算机科学 估计 地质学 地震学 工程类 数据库 认识论 哲学 系统工程
作者
Haibin Di,Aria Abubakar
出处
期刊:Geophysics [Society of Exploration Geophysicists]
卷期号:: 1-48
标识
DOI:10.1190/geo2024-0152.1
摘要

As one of the major tools in resolving the non-uniqueness challenge in subsurface interpretation and reservoir characterization, stochastic inversion from post- and pre-stack seismic data remains challenging, which not only requires heavy computational resources but also relies on intensive manual supervision. Inspired by the recent advances in deep learning particularly convolutional neural networks (CNNs) for interdisciplinary data integration, this study proposes a deep learning workflow that enables stochastic property estimation by efficiently integrating seismic images with sparse wells. It starts with sampling a set of property prior models (PPMs) from densely-measured properties at well locations and corrupting local seismic patterns with Gaussian noise. The core idea is to train a structure-guided CNN by mapping the contaminated seismic with the sampled PPMs while enforcing structural consistency to avoid overfitting in the presence of sparse wells. Finally, the baseline and uncertainty of target properties are estimated by running multiple realizations of the trained CNN. As demonstrated by three examples, with minimum efforts of CNN architecture customization according to data availability, the proposed workflow can accommodate various use cases, including rock acoustic/elastic property estimation from 3D post-/angle-stack seismic and soil geotechnical properties from 2D ultra-high-resolution seismic. In all examples, the machine predictions match seismic patterns well and are of high lateral consistency.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
2秒前
3秒前
xixi完成签到 ,获得积分10
3秒前
杜熙完成签到,获得积分10
3秒前
5秒前
6秒前
gougoutu发布了新的文献求助10
7秒前
liugm发布了新的文献求助10
7秒前
泡沫发布了新的文献求助10
7秒前
mysong发布了新的文献求助10
8秒前
嘻嘻哈哈完成签到 ,获得积分10
8秒前
黄晟钊完成签到,获得积分10
9秒前
lihua完成签到,获得积分10
11秒前
yar发布了新的文献求助1200
11秒前
Huang发布了新的文献求助10
12秒前
12秒前
123完成签到,获得积分10
13秒前
14秒前
小树完成签到 ,获得积分10
14秒前
liugm完成签到,获得积分10
15秒前
wwdd完成签到,获得积分10
15秒前
15秒前
Lucas应助gougoutu采纳,获得10
15秒前
泡沫完成签到,获得积分10
16秒前
洋洋爱吃枣完成签到 ,获得积分10
16秒前
芳泽完成签到,获得积分10
16秒前
Ava应助慢慢人采纳,获得10
16秒前
17秒前
黄晟钊发布了新的文献求助10
17秒前
炙热的雨双完成签到 ,获得积分10
17秒前
杜熙发布了新的文献求助10
19秒前
19秒前
彩色的恋风完成签到,获得积分10
19秒前
无辜的秀发布了新的文献求助10
19秒前
20秒前
爆米花应助AaronDP采纳,获得10
20秒前
yuzhi完成签到,获得积分10
21秒前
Jasper应助shan采纳,获得10
21秒前
翟大有完成签到 ,获得积分10
22秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
Research on Disturbance Rejection Control Algorithm for Aerial Operation Robots 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038619
求助须知:如何正确求助?哪些是违规求助? 3576294
关于积分的说明 11375058
捐赠科研通 3306084
什么是DOI,文献DOI怎么找? 1819374
邀请新用户注册赠送积分活动 892698
科研通“疑难数据库(出版商)”最低求助积分说明 815066