在DAS-VSP记录中用于强烈噪声抑制的MSAACNN
Haodong He1,2, Wei Wang3, Sibo Wang4
1Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology (Ministry of Education), 132012, Jilin, China.
Scientific reports
|September 30, 2024
概括
一个新的多尺度稀疏不对称的注意力卷积神经网络 (MSAACNN) 在分布式光纤传感 (DAS) 地震数据中有效抑制背景噪声. 这种方法显著改善了信号与噪声比 (SNR) 以获得更清晰的地震勘探结果.
科学领域:
- 地质物理学 地质物理学
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 分布式光纤传感 (DAS) 越来越多地被用于地震勘探,因为它的获取和部署优势.
- DAS记录经常受到强烈的背景噪声引起的信号噪声比率 (SNR) 低的影响.
- 在DAS数据中有效抑制噪音对于地震数据处理至关重要.
研究的目的:
- 为了应对DAS记录中强烈的背景噪声抑制的挑战.
- 提出一种新的深度学习模型,以提高DAS数据质量.
- 为了提高DAS地震数据的信号噪声比 (SNR).
主要方法:
- 一个多尺度稀疏不对称的注意力卷积神经网络 (MSAACNN) 被开发出来.
- 该网络使用扩展卷来扩大受体场和不对称卷来增强特征提取.
- 一个金字塔的注意力模块被纳入,以改进功能和提高denoising性能.
主要成果:
- 在DAS记录中,MSAACNN有效抑制了复杂的背景噪声.
- 与传统方法和标准CNN相比,MSAACNN表现出优越的无色化能力.
- 恢复的信号组件清晰和完整,SNR显著改善.
结论:
- 拟议的MSAACNN是DAS背景噪声抑制的强大工具.
- 这种深度学习方法显著提高了通过DAS获得的地震数据的质量.
- 该方法为使用DAS技术在地震勘探中改进SNR提供了一个有希望的解决方案.
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