整合Biot-Gassmann流体替代方法和基于机器学习的速度-应力关系,以估计现场应力
Ayyaz Mustafa1, Guanyi Lu1, Andrew P Bunger1,2
1Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh 15261, Pennsylvania, United States.
ACS omega
|February 16, 2026
概括
这项研究增强了机器/深度学习 (ML/DL) 模型,用于预测现场压力,使用来自干岩数据的低频声速率. 改进的工作流准确地估计了地下应力,推动了地热能源勘探.
科学领域:
- 地质物理学和岩石力学 岩石力学
- 机器学习在地球科学中的应用
背景情况:
- 准确的现场应力估计对于地下工程,包括地热能提取至关重要.
- 传统方法通常依赖于和岩石数据,但低频解读可能受益于干岩的特性.
- 频率分散对在现场应力预测中的速度-应力关系的影响仍然是一个悬而未决的问题.
研究的目的:
- 增强机器/深度学习 (ML/DL) 工作流程,用于现场压力预测.
- 调查使用干岩数据的生物-加斯曼衍生等效和速度用于ML/DL模型训练的有效性.
- 评估低频声速对现场应力预测准确性的影响.
主要方法:
- 从各种应力配置下的干核样本中获取真三轴超声速 (TUV) 数据.
- 生物-加斯曼流体替代的应用,从干岩超声速获得等效的和速度.
- 训练和验证ML/DL模型,使用来自犹他州FORGE站点核心的衍生低频等效和速度和TUV数据.
主要成果:
- 用同等和速度训练的ML/DL模型在现场应力方面取得了高的预测性能.
- 验证/测试结果为垂直,最小水平和最大水平应力分别为0.86,0.971和0.975的R平方值.
- 沙普利增量解释 (SHAP) 分析证实了模型的可靠性,并改善了对速度-压力关系的科学理解.
结论:
- 从干岩中使用低频声速的增强ML/DL工作流是现场应力预测的可行和准确的方法.
- 这种方法提供了一种可靠的替代方法,用于解释地下地质环境中的低频度测量.
- 该研究验证了使用Biot-Gassmann理论与ML/DL结合用于强大的地质物理应力分析的有效性.
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