基于IGA-BP的矿井构造复杂程度评价
Evaluation of mine structure complexity based on IGA-BP model
-
摘要: 为了准确评价矿井地质构造复杂程度，以黄陵一号煤矿为研究对象，在矿井地质构造发育特征与规律分析的基础上，选取了能够反映和影响该矿地质构造复杂程度的11个评价指标，按照1 km×1 km规格将井田剖分为185个评价单元，计算每个评价单元的评价指标值，借助有序地质量最优分割分析将每个评价指标值分割为4类，分别对应地质构造的简单、中等、复杂、极复杂4种类型，利用段内插值法获得BP神经网络的训练样本；为了克服单纯BP神经网络程序缺乏隐层神经元结构全局优化、收敛速度慢和易陷入局部最小值之缺陷，尝试采用基于免疫遗传算法（IGA）进行优化的BP神经网络算法（即IGA-BP）对矿井地质构造复杂程度进行综合评价；借助既定的训练样本，成功实现了BP网络隐层结构的全局优化和BP神经网络训练，最终利用训练好的IGA-BP网络对未知评价单元的地质构造复杂程度进行了综合评价，并绘制了矿井构造复杂程度分区图。结果显示：构造简单区位于研究区北部、东北部和南部，构造复杂区位于研究区中部偏西，构造中等区分布于研究区中部构造复杂区的南北两侧；与GA-BP、BP神经网络方法对比，基于IGA-BP的评价结果与矿井实际情况更为吻合，且IGA-BP评价方法无需考虑评价指标之间的相关性及权重，为矿井构造评价提供了1种新的评价方法，评价结果可以指导矿井合理的采掘部署。Abstract: In order to accurately evaluate the complexity of the mine geological structure, taking Huangling No.1 coal mine as the research object, based on the analysis of the development characteristics and laws of the mine geological structure, 11 evaluation indicators that can reflect and affect the complexity of the mine geological structure are selected. The well field is divided into 185 evaluation units according to the specification of 1 km×1 km, the evaluation index value of each evaluation unit is calculated, and each evaluation index value is divided into 4 categories according to the orderly quality optimal segmentation analysis, corresponding to the geological structure of simple, medium, complex, and extremely complex. The training samples of BP neural network are obtained by using the segment interpolation method. In order to overcome the defects of the simple BP neural network program, which lacks the global optimization of the hidden layer neuron structure, slows the convergence speed and is easy to fall into the local minimum value, this paper tries to use the BP neural network algorithm（ IGA-BP） based on the immune geneticalgorithm （IGA） for optimization to comprehensively evaluate the complexity of the mine geological structure. With the help of the established training samples, the global optimization of the hidden layer structure of the BP network and the training of the BP neural network were successfully realized. Finally, the trained IGA-BP network was used to comprehensively evaluate the complexity of the geological structure of the unknown evaluation unit, and the complex degree zoning map of mine structure is drawn. The results show that the structurally simple area is located in the north, northeast and south of the study area, the structurally complex area is located in the west of the middle of the study area, and the structurally moderate area is distributed on the north and south sides of the tectonic complex area in the middle of the study area. Compared with the GA-BP and BP neural network methods, the evaluation results based on IGA-BP are more consistent with the actual situation of the mine, and the IGA-BP evaluation method does not need to consider the correlation and weight between the evaluation indicators, which provides a useful tool for mine structure evaluation. A new evaluation method, the evaluation results can guide the reasonable mining deployment of the mine.
-
-
[1] 姜波，李明，程国玺，等.矿井构造预测及其在瓦斯突出评价中的意义[J].煤炭学报，2019，44（8）：2306-2317. JIANG Bo, LI Ming, CHENG Guoxi, et al. Mine geological structure prediction and its significance for gas outburst hazard evaluation[J]. Journal of China Coal Society, 2019, 44（8）: 2306-2317.
[2] 刘义生，赵少磊.开平向斜地质构造特征及其对瓦斯赋存的控制[J].煤炭学报，2015，40（S1）：164-169. LIU Yisheng, ZHAO Shaolei. Geological structural characteristics and their control action on gas occurrence in Kaiping syncline[J]. Journal of China Coal Society, 2015, 40（S1）: 164-169.
[3] 贾建称，陈健，柴宏有，等.矿井构造研究现状与发展趋势[J].煤炭科学技术，2008（10）：72-77. JIA Jiancheng, CHEN Jian, CHAI Hongyou, et al. Research status and development tendency of mine coalfield tectonics[J]. Coal Science and Technology, 2008（10）: 72-77.
[4] 詹才高，范念寒，陆汝纶，等.应用等性块段指数法定量划分华北煤矿勘探类型[J].煤田地质与勘探，1985（5）：16-24. [5] 徐凤银，龙荣生，夏玉成，等.矿井地质构造定量评价及其预测[J].煤炭学报，1991，16（4）：93-102. XU Fengyin, LONG Rongsheng, XIA Yucheng, et al. Quantitative assessment and prediction of geological structures in coal mine[J]. Journal of China Coal Society, 1991, 16（4）: 93-102.
[6] 徐凤银，龙荣生.煤矿构造复杂程度评价指标的优选途径[J].煤田地质与勘探，1991，19（1）：20-23. XU Fengyin, LONG Rongsheng. Optimization of parameters for evaluating structure complexity in coal mining[J]. Coal Geology & Exploration, 1991, 19（1）: 20-23.
[7] 夏玉成，徐凤银.灰色关联分析构造复杂程度评价在模糊综合评判中的应用[J].西安矿业学院学报，1991（1）：44-50. XIA Yucheng, XU Fengyin. Applicationof grey correlation analysis in fuzzy comprehensive assessment[J]. Journal of Xi'an Mining Institute, 1991（1）: 44-50.
[8] 朱宝龙，夏玉成.人工神经网络在矿井构造定量评价中的应用[J].煤田地质与勘探，2001，29（6）：15-17. ZHU Baolong, XIA Yucheng. Quantitative evaluation of mining structure based on the artificial neural network[J]. Coal Geology & Exploration, 2001, 29（6）: 15-17.
[9] 舒建生，贾建称，王跃忠，等.地质构造复杂程度定量化评价-以涡北煤矿为例[J].煤田地质与勘探，2010， 38（6）：22-26. SHU Jiansheng, JIA Jiancheng, WANG Yuezhong, et al. Quantitative evaluation of geological structures complexity: with Guobei Coal Mine as example[J]. Coal Geology & Exploration, 2010, 38（6）: 22-26.
[10] 邱梅，施龙青，滕超，等.构造预测与定量评价模型的构建及应用[J].煤矿安全，2013，44（9）：207-210. QIU Mei, SHI Longqing, TENG Chao, et al. Construction and application of structure forecast of quantitative evaluation model[J]. Safety in Coal Mines, 2013, 44（9）: 207-210.
[11] 方家虎，李志，张洋，等.芦岭煤矿8煤层地质构造复杂程度综合评价[J].煤田地质与勘探，2016，44（1）：22-26. FANG Jiahu, LI Zhi, ZHANG Yang, et al. Comprehensive evaluation of geological structure complexity of 8th seam in Luling mine[J]. Coal Geology & Exploration, 2016, 44（1）: 22-26.
[12] 国家安全生产监督管理总局，国家煤矿安全监察局．煤矿地质工作规定[Ｍ]．北京：煤炭工业出版社，2014. [13] 李家宏，朱炎铭，唐鑫，等.唐山矿西南区构造复杂程度的熵函数评价[J].煤田地质与勘探，2015，43（3）：6-10. LI Jiahong, ZHU Yanming, TANG Xin, et al. Entropy function evaluation of geological structure complexity of southwest section in Tangshan Mine[J]. Coal Geology & Exploration, 2015, 43（3）: 6-10.
[14] 施龙青，滕超，韩进，等.基于层次分析法-系统聚类分析的井田构造复杂程度评价[J].中国科技论文，2015，10（21）：2550-2554. SHI Longqing, TENG Chao, HAN Jin, et al. Evaluation of mine field structure complexity based on analytical hierarchy process and hierarchical cluster analysis[J]. China Sciencepaper, 2015, 10（21）: 2550-2554.
[15] 尹尚先，吴志远.钱家营井田构造复杂程度定量评价[J].煤矿安全，2019，50（5）：218-221. YIN Shangxian, WU Zhiyuan. Quantitative evaluation of structure complexity of Qianjiaying mine field[J]. Safety in Coal Mines, 2019, 50（5）: 218-221.
[16] 左林霄，高鹏，冯栋，等.基于AHP-熵权法耦合方法的地质构造复杂程度定量评价[J/OL].煤炭科学技术，2021，1-9.http://kns.cnki.net/kcms/detail/11.2402.td.20210508.0945.002.html. ZUO Linxiao, GAO Peng, FENG Dong, et al. Quantitative evaluation of geological structure complexity based on the coupling method of AHP-entropy weight method[J]. CoalScience and Technology, 2021, 1-9.http://kns.cnki.net/kcms/detail/11.2402.td.20210508.0945.002.html.
[17] 李鹏.邢东煤矿地质构造分析与评价[D].西安：西安科技大学，2010：10-15. [18] 夏玉成，胡明显，陈练武.矿井构造的GMDH-BP评价预测方法及其应用[J].煤炭学报，1997，22（5）：466-470. XIA Yucheng, HU Mingxian, CHEN Lianwu. GMDH-BP method and its application in evaluation and prediction of mine structure[J]. Journal of China Coal Society, 1997, 22（5）: 466-470.
[19] 张帆，钟灏.基于BP神经网络的矿井热动力灾害监测研究[J].煤矿安全，2020，51（11）：216-220. ZHANG Fan, ZHONG Hao. Research on mine thermodynamic disaster monitoring based on BP neural network[J]. Safety in Coal Mines, 2020, 51（11）: 216-220.
[20] 薛喜成.煤矿地质构造评价指标系统的建立与优选[J].煤炭技术，2010，29（2）：130-134. XUE Xicheng. Establishment and optimization of comprehensive evaluation indicator system for geological structure of coal mine[J]. Coal Technology, 2010, 29（2）: 130-134.
[21] 施龙青，邱梅，韩进，等.矿井地质构造定量化预测[M].北京：煤炭工业出版社，2014：45-50. [22] 张立志，施式亮.基于生物免疫系统的瓦斯异常涌出风险防控体系研究[J].煤矿安全，2021，52（12）：251-255. ZHANG Lizhi, SHI Shiliang. Study on risk control system of abnormal gas emission based on biological immune system[J]. Safety in Coal Mines, 2021, 52（12）: 251-255.
[23] 朱玉，张虹，苏成.基于免疫遗传算法的煤与瓦斯突出预测研究[J].中国矿业大学学报，2009，38（1）：125-130. ZHU Yu, ZHANG Hong, SU Cheng. Coal and gas outburst forecasting based on immune genetic algorithm[J]. Journal of China University of Mining & Technology, 2009, 38（1）: 125-130.
[24] 莫宏伟.人工免疫系统原理与应用[M].哈尔滨：哈尔滨工业大学出版社，2003：151-157. [25] 张旭东，高茂庭.基于IGA-BP网络的水质预测方法[J].环境工程学报，2016，10（3）:1566-1571. ZHANG Xudong, GAO Maoting. Water quality prediction method based on IGA-BP[J]. Chinese Journal of Environmental Engineering, 2016, 10（3）: 1566-1571.
[26] 赵伟，陈培红，曹阳.基于ACSOA-BP神经网络的瓦斯含量预测模型[J].煤矿安全，2022，53（1）：174-180. ZHAO Wei, CHEN Peihong, CAO Yang. Prediction model of coal seam gas content based on ACSOA optimized BP neural network[J]. Safety in Coal Mines, 2022, 53（1）: 174-180.
[27] 孙瑞.基于IGA-BP神经网络的地铁施工地表沉降预测研究及信息系统的实现[D].石家庄：石家庄铁道大学，2020：20-30. [28] 高宗帅，郗涛，徐伟雄，等.基于改进遗传算法-反向传播神经网络的升降机健康评价研究[J].机电工程,2021，38（3）：313-318. GAO Zongshuai, XI Tao, XU Weixiong, et al. Elevator safety assessment based on IGA-BPNN[J]. Journal of Mechanical & Electrical Engineering, 2021, 38（3）: 313-318.
-
期刊类型引用(3)
1. 樊潇飞. 通风机风量测试系统设计及试验验证. 机械管理开发. 2024(01): 223-224+254 . 
百度学术
2. 王毅. 煤矿主通风装置设计与实现. 机械管理开发. 2023(08): 105-107 . 百度学术
3. 李祥荣. 煤矿局部通风管控系统优化设计. 山西煤炭. 2023(04): 89-93+128 . 百度学术
其他类型引用(1)
计量
- 文章访问数: 18
- HTML全文浏览量: 2
- PDF下载量: 27
- 被引次数: 4
下载:
