智能识别和预测屋顶恶化区域,基于钻井过程中的测量
Jing Wu1, Zhi-Qiang Zhao1, Xiao-He Wang1
1School of Energy and Mining Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
这项研究使用钻探信号来识别道路上的屋顶恶化区域. 一个深度学习算法准确地预测这些区域,提高挖掘安全性.
科学领域:
- 地质技术工程 地质技术工程
- 采矿工程 采矿工程 采矿工程
- 人工智能的人工智能
背景情况:
- 道路挖掘的稳定性受到诸如分层空间和弱层间层等屋顶恶化区域的威胁.
- 及时识别和预测这些区域对于有效的道路支持和安全至关重要.
研究的目的:
- 为了研究钻探测量信号对岩层变化的反应.
- 开发一种智能方法,使用深度学习来识别和预测屋顶恶化区域.
主要方法:
- 理论分析,实验室实验,ABAQUS动态数值模拟和现场测量.
- 在钻井过程中分析推力,扭矩和Y方向振动信号.
- 使用长短期记忆 (LSTM) 和随机森林开发深度学习算法.
主要成果:
- 钻井参数有效地描述了岩石结构和强度变化.
- 在屋顶恶化区域观察到明显的信号特征.
- 拟议的深度学习算法实现了对层次空间的100%识别和对恶化区域的96.6%准确性.
结论:
- 开发的深度学习方法可以智能识别和预测屋顶恶化区域.
- 该方法增强了实时监控和控制,提高了道路挖掘的安全性和稳定性.
- 这项研究为地下工程的实际应用和未来研究提供了宝贵的见解.
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