基于IGA-BP的矿井构造复杂程度评价

薛喜成; 吕自豪; 倚江星; 霍高普

基于IGA-BP的矿井构造复杂程度评价

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出版历程
- 发布日期: 2023-03-19

Evaluation of mine structure complexity based on IGA-BP model

摘要

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。
- 矿井地质构造 /
- 地质勘查 /
- BP神经网络 /
- 免疫遗传算法 /
- 定量评价 /
- 黄陵一号煤矿
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.
- mine geological structure /
- gelolgical survey /
- BP neural network /
- immune genetic algorithm /
- quantitative evaluation /
- Huangling No. 1 coal mine

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