基于机器学习的分析地震引起的斜坡位移
Jiyu Wang1, Niaz Muhammad Shahani1, Xigui Zheng1
1School of Mines, China University of Mining and Technology, Xuzhou, Jiangsu Province, China.
PloS one
|February 6, 2025
概括
XGBoost的机器学习模型准确地预测了地震引起的斜率变化,其性能优于其他模型. 最大水平加速是影响位移的关键因素,有助于斜坡稳定性管理.
科学领域:
- 地质技术工程 地质技术工程
- 地震学 地震学
- 机器学习 机器学习
背景情况:
- 准确评估地震引起的斜坡位移对于地震斜坡设计至关重要.
- 各种机器学习模型正在探索它们在这个领域的有效性.
研究的目的:
- 评估和比较人工神经网络 (ANN),支持矢量机 (SVM),随机森林 (RF) 和极端梯度增强 (XGBoost) 模型的性能.
- 确定最有效的机器学习模型来预测地震引起的斜坡移位.
主要方法:
- 使用了45个样本的数据集,其中70%用于培训,30%用于测试.
- 用重复的5倍交叉验证来提高模型的稳定性.
- 用R2值和灵敏度分析来评估模型性能.
主要成果:
- 在训练和测试数据上,XGBoost实现了0.99的优异R2值.
- ANN,SVM和RF模型显示较低的R2值 (0.63-0.94用于训练,0.80-0.87用于测试).
- 灵敏度分析显示最大水平加速 (kmax) 是影响斜坡位移的最重要因素.
结论:
- XGBoost模型显示了地震引起的斜坡移位的高预测精度.
- 开发的XGBoost模型为地震预警系统和斜坡稳定性管理提供了宝贵的见解.
- 最大水平加速是预测地震事件期间斜坡移位的关键参数.
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