通过异构扩散和深度转移学习来增强海洋磁异常解释
J Ghosh1, S Thoram1, Jiajia Sun2
1University of Houston, Houston, TX, USA.
Scientific reports
|December 5, 2025
概括
深度学习模型,增强了异构扩散和转移学习,准确地解释了海洋磁异常. 这种方法克服了海洋地演变研究中的数据限制和主观性.
科学领域:
- 地质物理学 地质物理学
- 海洋地质 海洋地质学
- 深度学习应用程序
背景情况:
- 线性磁异常 (LMA) 对于理解海洋地的形成和演变至关重要.
- 由于船舶轨迹稀疏,不规则,解释LMA具有挑战性,导致主观性和时间消耗.
- 现有的方法在海洋磁性数据中遇到不连续性问题.
研究的目的:
- 尽量减少主观性,并通过使用深度学习 (DL) 加快海洋磁异常的解释.
- 为了应对海洋磁性DL应用中的数据不连续性和有限的标记数据集的挑战.
主要方法:
- 实施深度学习模型,包括标准卷积神经网络 (CNN) 和转移学习.
- 利用异型扩散来增强沿着局部方向的LMA连续性.
- 研究了带有和没有异型扩散的转移学习的有效性.
主要成果:
- 与标准CNN相比,转移学习显著提高了预测准确性.
- 当与转移学习一起使用时, anisotropic扩散进一步提高了预测准确性.
- 最具表现力的模型应用于沙特斯基和阿佐尔斯高原的数据,成功识别了从蔓延山脊火山活动中传播的LMA.
结论:
- 深度学习,特别是转移学习与异型扩散相结合,为解释海洋磁异常提供了强大的解决方案.
- 这种方法提高了准确性和效率,克服了传统方法的局限性.
- 该模型成功地确定了火山特征,并将非线性异常与复杂的构造学设置相关联.
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