A multivariate regression analysis model was developed to predict floor failure depth in deep mines using field measured data from 39 coal mining sites in the eastern mining area of the north China coalfields. A Brillouin optical time domain reflectometry system was built with distributed optical fiber sensors embedded in the floor of a coalface to measure the actual failure depth of the mine floor. The measured and predicted results were in good agreement. This study provides an effective scientific basis for preventing and controlling floor water inrush in deep mines in the north China coalfield.

中文翻译：

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用多因素影响指数回归分析预测深层煤矿底板破坏深度

利用华北煤田东部矿区39个煤矿现场的实测数据，建立了多元回归分析模型来预测深部矿山的底板破坏深度。建立了布里渊（Brillouin）光学时域反射仪系统，该系统在煤层的底板中嵌入了分布式光纤传感器，以测量矿井底板的实际故障深度。测量结果和预测结果吻合良好。该研究为预防和控制华北煤田深部矿井底板突水提供了有效的科学依据。