1. 国网宁夏电力有限公司超高压公司
2. 中国电力科学研究院有限公司输变电工程研究所
3. 国网电力工程研究院有限公司
纸质出版:2025
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曹凯, 卢渊, 庞小龙, 等. 基于微震事件频次的采空区沉陷变形智能预警方法[J]. 地质科技通报, 2025,44(4):78-89.
曹凯, 卢渊, 庞小龙, et al. 基于微震事件频次的采空区沉陷变形智能预警方法[J]. Bulletin of Geological Science and Technology, 2025, 44(4): 78-89.
曹凯, 卢渊, 庞小龙, 等. 基于微震事件频次的采空区沉陷变形智能预警方法[J]. 地质科技通报, 2025,44(4):78-89. DOI: 10.19509/j.cnki.dzkq.tb20240646.
曹凯, 卢渊, 庞小龙, et al. 基于微震事件频次的采空区沉陷变形智能预警方法[J]. Bulletin of Geological Science and Technology, 2025, 44(4): 78-89. DOI: 10.19509/j.cnki.dzkq.tb20240646.
“三下”开采引发的沉陷变形对输电线路等地表建(构)筑物的安全构成威胁,亟需一种采空区沉陷变形早期感知与智能预警方法。为此,提出了一种基于微震事件频次的采空区沉陷变形智能预警框架。该框架利用分布式声波传感(distributed acoustic sensing,简称DAS)系统采集微震数据,通过STA/LTA算法提取微震事件,借助结合自编码器(autoEncoder,简称AE)与高斯混合模型(Gaussian mixture models,简称GMM)的深度聚类方法对其分类,基于微震事件频次与沉陷变形数据之间的相关系数筛选诱发沉陷变形的微震事件,进而采用VGG-16深度学习模型实现对这类微震事件的智能识别,通过设定预警阈值开展实时预警。以我国西部某典型煤矿采空区为例,将该框架应用于实地监测。结果表明,该框架将采集到的采空区微震事件分为五大类,从中提取出诱发沉陷变形的一类微震事件,结合微震事件智能识别模型,成功对沉陷变形引发的杆塔倾斜度激增事件作出预警。实例证明该框架能够有效捕捉微震事件与沉陷变形的关联,实现对采空区沉陷变形的预警,具有实践可行性和工程应用价值。
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