基于深度学习的同时源数据混合域除错方法

IF 2.2 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY Journal of Applied Geophysics Pub Date : 2024-07-14 DOI:10.1016/j.jappgeo.2024.105451
Shengqiang Mu , Wenda Li , Tianqi Wu , Guoxu Shu , Shoudong Huo
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引用次数: 0

摘要

同步震源采集技术打破了传统地震勘探的局限性,它允许多个震源几乎同时发射。在勘探时间固定的情况下,同步震源采集可以增加震源数量,而在震源数量固定的情况下,该技术可以大大缩短勘探时间。目前,这种高效率采集技术的巨大优势已受到学术界和工业界的广泛关注,研究人员提出了一系列的除杂方法,并取得了良好的效果。近年来,深度学习的快速发展为排错提供了一种新的解决方案,与传统方法相比,它在处理大规模地震数据时具有明显的计算时间优势。我们提出了一种基于深度学习的新型迭代除错方法,该方法综合了不同领域地震数据处理的优势。在所提出的方法中,通过选择适当的域组合,分离质量与单个域的排错结果相比有了显著提高。通过对合成数据和野外数据进行排错,验证了所提方法的有效性;通过与多级中值滤波方法和基于深度学习的传统方法进行比较,证明了所提方法具有更好的性能。
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A mixed domain deblending approach for simultaneous source data based on deep learning

The simultaneous source acquisition technology breaks the limitations of traditional seismic survey, which allows more than one source to fire almost at the same time. When the survey time is fixed, simultaneous source acquisition can increase the number of sources, while when the number of sources is fixed, this technique can greatly reduce the survey time. At present, the great advantages of this high-efficiency acquisition technology have received wide attention from academia and industry, and researchers have proposed a series of deblending methods and obtained good results. In recent years, the rapid development of deep learning provides a new solution for deblending, and it has obvious advantages in computational time compared to traditional methods when processing large-scale seismic data. We proposed a novel iterative deblending method based on deep learning, which integrates the advantages of seismic data processing in different domains. In the proposed method, by selecting the appropriate combination of domains, the separation quality is significantly improved compared to the deblended results in a single domain. The effectiveness of the proposed method is verified by deblending the synthetic and field data, and the better performance of the proposed method are demonstrated by comparing it with the multilevel median filter method and conventional deep learning-based methods.

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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.
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