一种基于机器学习的方法,用于识别水岩相互作用下的裂砂岩的故障风险状态
Jinbo Qu1,2, Cheng Song1,2,3, Jinwen Bai1,2
1College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
机器学习模型使用声辐射参数准确预测裂纹砂岩的不稳定性. 随机森林模型实现了97.87%的准确性,识别了早期预警系统的关键前体.
科学领域:
- 地质技术工程 地质技术工程
- 材料科学 材料科学 材料科学
- 计算科学 计算科学
背景情况:
- 裂纹砂岩的机械性能由于水岩相互作用而降低.
- 早期检测裂纹砂岩的不稳定性对于安全和基础设施至关重要.
- 声辐射 (AE) 参数为不稳定提供了潜在的前体信息.
研究的目的:
- 调查AE参数作为裂砂岩不稳定的前体的潜力.
- 开发和测试用于识别不稳定性风险的机器学习模型.
- 在自然和和条件下为预警系统提供技术支持.
主要方法:
- 建立了586个声辐射实验的实验数据集.
- 采用了三个机器学习模型:AdaBoost,MLP和随机森林 (RF).
- 使用十倍交叉验证来进行可靠的模型培训和评估.
主要成果:
- 随机森林模型在识别裂纹砂岩的不稳定性方面取得了97.87%的准确性.
- 影响不稳定性预测的关键参数包括砂岩状态和b值.
- 斯皮尔曼相关性评估了特征关系.
结论:
- 机器学习模型,特别是射频,有效地识别了裂砂岩的不稳定性风险.
- 这项研究为在不同水位条件下实用的预警方法提供了基础.
- 机器学习为分析AE数据的传统方法提供了更快,更准确的替代方案.
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