基于深度学习的受控源电磁源近场效应校正方法和应用程序
Wei Luo1,2,3, Xianjie Chen2,3, Shixing Wang1
1China Railway Eryuan Engineering Group CO., LTD, Chengdu, China.
PloS one
|November 11, 2024
概括
本研究介绍了一种使用LSTM-CNN的深度学习方法,用于在控制源电磁 (CSEM) 调查中纠正近场效应. 这种方法精确地改进了地质物理数据,改善了地质结构的表示.
科学领域:
- 地质物理学 地质物理学
- 深度学习应用程序
- 电磁方法 电磁方法
背景情况:
- 控制源电磁 (CSEM) 方法中的近场效应在地物理勘探中带来了挑战.
- 对CSEM数据的准确解释对于理解地下地质结构至关重要.
研究的目的:
- 开发和验证基于深度学习的方法来纠正CSEM数据中的近场效应.
- 通过减轻近场影响来提高地质结构解释的准确性.
主要方法:
- 用前向模拟为分层地质模型生成各种数据集.
- 构建一个结合长短期记忆 (LSTM) 和卷积神经网络 (CNN) 架构的深度学习网络.
- 在模拟和测量CSEM数据上的实验验证,包括噪声弹性测试.
主要成果:
- LSTM-CNN网络展示了高准确性,训练数据与理论数据集的模拟数据密切匹配.
- 对测量的CSEM数据的应用有效地消除了在较低频率观察到的虚假高电阻异常.
- 该方法在减轻近场效应和噪声方面显著改善.
结论:
- 提出的基于深度学习的校正方法有效地消除了CSEM中近场效应的影响.
- 这种技术通过提供更真实的地质结构表示,为地质物理勘探提供了实际的好处.
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