使用ML方法预测地震异性质:在海上碳酸盐油田的案例研究
Guibin Zhao1, Fateh Bouchaala1, Mohamed S Jouini2
1Earth Sciences Department, Khalifa University of Sciences and Technology, Abu Dhabi, UAE.
PloS one
|January 8, 2025
概括
机器学习准确地估计了地震异性质参数,如在破碎介质中的森参数 (ε 和 δ). 这种方法克服了传统基于物理的模型的局限性,提供可重现和可靠的地质解释.
科学领域:
- 地质物理学 地质物理学
- 机器学习 机器学习
- 地震无极性地震无极性
背景情况:
- 估计地震异构度参数对于分析断裂和分层地质构造至关重要.
- 传统的反转方法通常依赖于具有主观初始假设的复杂物理模型,导致无法重现的结果.
- 机器学习为克服地震数据分析中的这些局限性提供了一个有希望的替代方案.
研究的目的:
- 开发和应用机器学习方法来准确估计森的参数 (ε 和 δ).
- 建立一个系统的地震特征选择工作流程,以估计异构性.
- 通过基于物理的模型验证机器学习预测.
主要方法:
- 使用支持向量回归,极端梯度提升,多层感知子和卷积神经网络.
- 开发了一种工作流程,用于从合成数据中检查和选择最佳的地震特征.
- 使用有限差数值建模生成合成地震数据.
- 选择时间和频率领域的波幅作为输入特征.
- 优化机器学习超参数,用于高精度的训练和测试.
主要成果:
- 成功训练和测试机器学习模型,准确度高.
- 预测森的参数 (ε 和 δ) 对于页岩形成.
- 与基于物理的模型对比验证了预测,实现了较低的相对误差 (2.92%7.14%).
结论:
- 机器学习方法提供准确和可重现的地震异性质参数估计.
- 开发的工作流程有效地为模型培训选择了相关的地震特征.
- 这种方法提高了地质介质中断裂和层层解释的可靠性.
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