A comprehensive review of deep learning techniques for salt dome segmentation in seismic images

IF 2.2 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY
Muhammad Saif Ul Islam, Aamir Wali
{"title":"A comprehensive review of deep learning techniques for salt dome segmentation in seismic images","authors":"Muhammad Saif Ul Islam,&nbsp;Aamir Wali","doi":"10.1016/j.jappgeo.2024.105504","DOIUrl":null,"url":null,"abstract":"<div><p>Salt dome detection in seismic images is a critical aspect of hydrocarbon exploration and production. Salt domes are subsurface structures formed from the accumulation of salt deposits and can trap oil and gas reservoirs. Seismic imaging techniques are used to visualize the subsurface structures and identify the presence of salt domes. Historically, the process of detecting salt domes in seismic images was done manually, which was time-consuming and required the input of domain experts. However, in recent years, automated methods using seismic attributes and machine learning algorithms have been developed to improve the efficiency of salt dome detection. Deep learning-based methods have shown promising results in salt body segmentation, and several techniques have been proposed in recent years. This review examines recent deep-learning architectures for salt body segmentation in seismic images, offering a concise overview of the various models proposed in the literature. It delves into established benchmark datasets, highlighting potential limitations and emphasizing the importance of data quality for robust models. It explores performance evaluation metrics used in the literature to capture a more comprehensive picture of segmentation performance. This paper identifies several promising areas for further research and development opportunities to refine and enhance the current state-of-the-art salt body segmentation in seismic images. This comprehensive analysis provides a valuable roadmap for researchers and practitioners interested in understanding how deep learning can be utilized for salt body classification in seismic exploration.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"230 ","pages":"Article 105504"},"PeriodicalIF":2.2000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Geophysics","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0926985124002209","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Salt dome detection in seismic images is a critical aspect of hydrocarbon exploration and production. Salt domes are subsurface structures formed from the accumulation of salt deposits and can trap oil and gas reservoirs. Seismic imaging techniques are used to visualize the subsurface structures and identify the presence of salt domes. Historically, the process of detecting salt domes in seismic images was done manually, which was time-consuming and required the input of domain experts. However, in recent years, automated methods using seismic attributes and machine learning algorithms have been developed to improve the efficiency of salt dome detection. Deep learning-based methods have shown promising results in salt body segmentation, and several techniques have been proposed in recent years. This review examines recent deep-learning architectures for salt body segmentation in seismic images, offering a concise overview of the various models proposed in the literature. It delves into established benchmark datasets, highlighting potential limitations and emphasizing the importance of data quality for robust models. It explores performance evaluation metrics used in the literature to capture a more comprehensive picture of segmentation performance. This paper identifies several promising areas for further research and development opportunities to refine and enhance the current state-of-the-art salt body segmentation in seismic images. This comprehensive analysis provides a valuable roadmap for researchers and practitioners interested in understanding how deep learning can be utilized for salt body classification in seismic exploration.

地震图像中盐丘分割的深度学习技术综述
地震图像中的盐穹顶探测是油气勘探和生产的一个重要方面。盐穹是盐沉积积累形成的地表下结构,可以捕获油气藏。地震成像技术用于观察地下结构和识别盐穹的存在。一直以来,在地震图像中检测盐穹的过程都是人工完成的,不仅耗时,而且需要领域专家的输入。但近年来,人们开发出了使用地震属性和机器学习算法的自动化方法,以提高盐穹检测的效率。基于深度学习的方法在盐体分割方面取得了可喜的成果,近年来已提出了几种技术。本综述探讨了最近用于地震图像盐体分割的深度学习架构,对文献中提出的各种模型进行了简要概述。它深入探讨了已建立的基准数据集,强调了潜在的局限性,并强调了数据质量对稳健模型的重要性。论文还探讨了文献中使用的性能评估指标,以便更全面地了解分割性能。本文确定了几个有望进一步研究和开发的领域,以完善和增强当前最先进的地震图像盐体分割技术。这一全面分析为有兴趣了解如何在地震勘探中利用深度学习进行盐体分类的研究人员和从业人员提供了宝贵的路线图。
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
Journal of Applied Geophysics
Journal of Applied Geophysics 地学-地球科学综合
CiteScore
3.60
自引率
10.00%
发文量
274
审稿时长
4 months
期刊介绍: The Journal of Applied Geophysics with its key objective of responding to pertinent and timely needs, places particular emphasis on methodological developments and innovative applications of geophysical techniques for addressing environmental, engineering, and hydrological problems. Related topical research in exploration geophysics and in soil and rock physics is also covered by the Journal of Applied Geophysics.
×
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