岩石爆发的分析方法是完全机械化的顶层煤洞穴采矿,采矿在的倾斜厚厚的煤层中
Pengfei Shan1,2, Zhongming Yan3,4, Xingping Lai1,2
1School of Energy Engineering, Xi'an University of Science and Technology, Xi'an, 710054, Shaanxi, China.
Scientific reports
|January 5, 2024
概括
这项研究引入了一种深度学习方法,使用微地震数据预测岩石爆发. 该方法分析了b值时间序列,为煤炭开采业务提供了更好的安全性.
科学领域:
- 地质科学 地质科学
- 采矿工程 采矿工程 采矿工程
- 数据科学数据科学数据科学
背景情况:
- 岩石爆发是煤矿的重要动态灾害,对安全构成风险.
- 从微地震数据中获得的b值对于监测和预测岩石爆发至关重要.
- 目前的预测方法需要提高准确性和可靠性.
研究的目的:
- 开发一种基于深度学习的方法,用于预测岩石爆破危险时期.
- 用微地震监测数据提高岩石爆发预测的准确性.
- 为在特定的采矿条件下提供可行的技术解决方案,防止岩石破裂.
主要方法:
- 从微地震监测数据计算b值.
- 使用动态时间扭曲 (DTW) 构建和重构b值时间序列.
- 采用双向短期和长期记忆 (BiLSTM) 网络,通过差异进化算法和注意力机制进行优化.
主要成果:
- 拟议的BiLSTM模型与基本的LSTM算法相比显示出更高的性能.
- 该预测模型在工程案例分析中显示出良好的准确性和可行性.
- 关键性能指标,如R平方,证实了该模型的有效性.
结论:
- 深度学习方法为预测岩石爆破危险时期提供了一个有希望的方法.
- 优化的BiLSTM模型为防止岩石爆破提供了可靠的技术支持.
- 这种方法特别适用于在地震活跃地区的坡厚厚的煤层.
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