Imaging distributed acoustic sensing-to-geophone conversion data: A field application to CO<sub>2</sub> sequestration data

检波器 地球物理成像 地质学 垂直地震剖面 遥感 分布式声传感 数据集 地震学 计算机科学 人工智能 光纤传感器 光纤 电信
作者
Yong Ma,Lei Fu,Weichang Li
出处
期刊:Interpretation [Society of Exploration Geophysicists]
卷期号:: 1-43
标识
DOI:10.1190/int-2022-0098.1
摘要

Compared with conventional geophone data, distributed fiber-optic sensing, including distributed acoustic sensing (DAS), can provide better spatial coverage for imaging the subsurface with finer spatial sampling. Because DAS measures subsurface seismic responses differently than the geophone, imaging technologies (e.g., reverse time migration and full-waveform inversion) that are developed for conventional geophone data cannot be readily applied to original DAS data without causing uncertainties in phase or depth, especially when one compares the DAS imaging results against the usual geophone imaging results. Based on vertical seismic profile field data from a CO 2 sequestration site, we have compared the imaging results of the CO 2 storage reservoir associated with the DAS and the geophone data, respectively, and we illustrate the differences between the imaging results of the DAS and geophone data. The difference between the DAS and geophone imaging results could be critical in obtaining time-lapse signals for monitoring reservoir changes, e.g., in subsurface CO 2 sequestration. We develop to convert DAS to geophone data so that we can reduce the discrepancies between DAS and geophone imaging results and we therefore can reuse existing seismic imaging technologies. Two conversion methods, one physics-based and one deep-learning (DL)-based, are used for the DAS-to-geophone transformation. Field data results demonstrate that the DL-based approach can better successfully improve the alignment between the DAS and geophone images, whereas the physics-based solution is constrained by its assumption.
最长约 10秒,即可获得该文献文件

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
娜娜完成签到 ,获得积分10
刚刚
传奇3应助微眠采纳,获得10
1秒前
1秒前
汪洋完成签到,获得积分10
1秒前
jyu完成签到,获得积分10
1秒前
Singularity应助一只橙子采纳,获得10
1秒前
叨叨完成签到,获得积分10
1秒前
晨雨完成签到,获得积分10
2秒前
可以完成签到,获得积分10
2秒前
爆米花应助林少玮采纳,获得10
2秒前
41完成签到,获得积分10
3秒前
牛牛完成签到,获得积分10
3秒前
土壤情缘完成签到,获得积分10
3秒前
小白完成签到,获得积分10
3秒前
学术咸鱼发布了新的文献求助10
4秒前
everglow完成签到,获得积分10
4秒前
yuaasusanaann完成签到,获得积分10
4秒前
跳跳熊完成签到,获得积分10
5秒前
dyk完成签到,获得积分10
5秒前
达笙完成签到 ,获得积分10
5秒前
海慕云完成签到,获得积分10
6秒前
Hello应助tangyangzju采纳,获得10
6秒前
6秒前
8秒前
风趣问雁完成签到 ,获得积分10
8秒前
兜里只有三块钱完成签到,获得积分10
8秒前
鱼儿123完成签到,获得积分10
8秒前
8秒前
chenqingyu完成签到,获得积分10
8秒前
ccc完成签到,获得积分10
9秒前
笑点低一手完成签到,获得积分10
9秒前
9秒前
10秒前
老马哥完成签到,获得积分0
10秒前
10秒前
ccdk2025完成签到,获得积分10
11秒前
听风轻语完成签到,获得积分10
11秒前
娟娟完成签到 ,获得积分10
11秒前
阔达代芹完成签到,获得积分10
12秒前
热心的诗蕊完成签到,获得积分10
12秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
Immigrant Incorporation in East Asian Democracies 600
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 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 3968637
求助须知:如何正确求助?哪些是违规求助? 3513552
关于积分的说明 11168493
捐赠科研通 3248935
什么是DOI,文献DOI怎么找? 1794554
邀请新用户注册赠送积分活动 875188
科研通“疑难数据库(出版商)”最低求助积分说明 804691