Liu Weijun, Liu Zida, Liu Zhixiang
School of Resources and Safety Engineering, Central South University, Changsha, 410083, China.
Sci Rep. 2024 Nov 18;14(1):28401. doi: 10.1038/s41598-024-80025-w.
Undersea mining encounters challenges due to the presence of seawater. An influx of seawater into stop in undersea can result in enormous disaster. Predicting strata settlement is a crucial measure to ensure the safety of undersea mining. This study proposed an intelligent model based on deep forest (DF) to evaluate the strata settlement during undersea mining. Initially, the strata displacement was monitored in the Xishan mining area of Sanshandao gold mine, China. Comprehensive datasets encompassing roof displacement and twelve influencing factors were compiled from 120 observations. Then, these datasets were statistically analyzed and used to train the DF model. The developed DF model achieved a training R of 0.971 and a testing R of 0.936. Compared with other machine learning models, the DF model has superior performance in the prediction of strata settlement. Moreover, a graphical user interface was designed to facilitate the application of the DF model. Finally, to validate model feasibility, displacement monitoring was conducted in the Xinli mining area of Sanshandao gold mine. Additional datasets were collected to validate the capability of the DF model. The results suggested that the DF model can be used to predict strata subsidence in undersea mining effectively.
由于海水的存在，海底采矿面临挑战。海水涌入海底采场可能会导致巨大灾难。预测地层沉降是确保海底采矿安全的关键措施。本研究提出了一种基于深度森林（DF）的智能模型，用于评估海底采矿过程中的地层沉降。首先，在中国三山岛金矿的西山矿区对地层位移进行了监测。从120次观测中编制了包含顶板位移和12个影响因素的综合数据集。然后，对这些数据集进行统计分析，并用于训练DF模型。所开发的DF模型训练R值为0.971，测试R值为0.936。与其他机器学习模型相比，DF模型在预测地层沉降方面具有卓越性能。此外，设计了一个图形用户界面以方便DF模型的应用。最后，为验证模型的可行性，在三山岛金矿的新立矿区进行了位移监测。收集了额外的数据集以验证DF模型的能力。结果表明，DF模型可有效用于预测海底采矿中的地层沉降。
