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深度学习用于高分辨率的地震成像.

Liyun Ma1, Liguo Han2, Qiang Feng1

  • 1Jilin University, College of Geoexploration Science and Technology, Changchun, 130026, China.

Scientific reports
|May 5, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种深度学习的地震成像方法,使用神经网络进行高分辨率的地下解释. 有效的方法准确地重建了地质结构,克服了传统技术的局限性.

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科学领域:

  • 地质物理学 地质物理学
  • 人工智能的人工智能
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 地震成像对于地下地质解释至关重要.
  • 由于理论和计算的局限性,传统的地震方法在高分辨率上扎.

研究的目的:

  • 开发用于高分辨率地震成像的深度学习框架.
  • 将地震数据直接映射到反射模型中,绕过后处理.

主要方法:

  • 变压器和卷积神经网络 (CNN) 架构的集成.
  • 使用自适应空间特征融合 (ASFF) 的增强.
  • 将地震数据直接映射到反射模型.

主要成果:

  • 准确推断地下地质结构.
  • 通过RMSE,CC和SSIM指标证明的地下特征的高保真重建.
  • 即使有噪音注入,性能也可靠.

结论:

  • 拟议的深度学习方法实现了高分辨率的地震成像.
  • 这种方法克服了传统地震解释的局限性.
  • 深度学习显示了推进地震成像技术的巨大潜力.