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数据驱动的机器学习模型用于预测深海沉积物的工程性质
Jungmin Yun1, Junghee Park2, Hyunwook Choo3
1Geotechnical DivisionKunhwa Engineering, 11, Olympic-Ro 35Ga-Gil, Songpa-Gu, Seoul, South Korea.
Scientific reports
|November 22, 2025
概括
预测深海沉积物的特性对于了解过去的海洋至关重要. 一个新的机器学习框架,使用极端梯度提升 (XGBoost),准确地预测沉积物的特性,如孔隙性和密度.
科学领域:
- 海洋地质学和地球物理学
- 数据驱动的预测建模数据驱动的预测建模
- 海洋学沉积物学
背景情况:
- 深海沉积物特性为古海洋学条件提供了关键的见解.
- 深海沉积物的高空间变性使准确的属性预测变得复杂.
- 了解沉积物组成,地层学和地化学对于气候重建至关重要.
研究的目的:
- 开发和验证数据驱动的机器学习框架,用于预测深海沉积物的关键性质.
- 确定影响沉积物属性预测的最有影响力的特征.
- 量化预测的不确定性和评估模型的稳定性.
主要方法:
- 开发一种机器学习框架,使用五种预测场景,并进行量身定制的预处理和超参数调整.
- 应用极端梯度提升 (XGBoost) 算法作为主要预测模型.
- 使用沙普利添加式解释 (SHAP) 进行特征重要性分析和理解深度和沉积物特性之间的关系.
主要成果:
- 与其他四种算法相比,极端梯度增强 (XGBoost) 模型表现出优异的预测性能.
- 深度和压缩波速度被确定为孔隙度,粒度密度,石含量和导热率的最重要的预测因素.
- 该XGBoost模型提供了深度依赖的预测与量化的不确定性,突出其稳定性.
结论:
- 拟议的机器学习框架为预测深海沉积物特性提供了强大而准确的方法.
- 特性重要性分析揭示了沉积物属性估计的关键驱动因素,特别是深度和地震速度.
- 该框架能够提供量化的不确定性,这提高了其在古海洋学研究中的实用性.
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