基于空间统计的采煤工作面内断层识别方法

冯雅杰; 文广超; 吴冰洁; 胡祖栋

doi:10.12363/issn.1001-1986.23.03.0145

基于空间统计的采煤工作面内断层识别方法

doi: 10.12363/issn.1001-1986.23.03.0145

河南理工大学资源环境学院，河南焦作 454003

基金项目: 河南省高等学校重点科研项目(15A170007)；河南省科技攻关计划项目(212102310389)

详细信息

第一作者:
冯雅杰，1998年生，女，陕西宝鸡人，硕士，从事三维地质方向的研究. E-mail：fengyj395@163.com

通信作者:
文广超，1979年生，男，河南驻马店人，博士，教授，硕士生导师，从事地学信息技术方面的教学与研究. E-mail：wengc366@163.com

中图分类号: TD712
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出版历程
- 收稿日期: 2023-03-18
- 修回日期: 2023-06-28
- 刊出日期: 2023-10-25
- 网络出版日期: 2023-07-20

A method for identifying faults within mining faces based on spatial statistics

School of Resources & Environment, Henan Polytechnic University, Jiaozuo 454003, China

摘要

摘要: 采煤工作面内小断层严重影响瓦斯抽采及煤层回采工作，准确识别位置、落差、产状等参数对保障煤矿安全生产意义重大。为有效降低瓦斯涌出量、防止瓦斯爆炸、开发利用瓦斯资源，煤矿施工了大量的瓦斯抽采孔，这为识别煤层内小断层提供了良好的工程条件。相较于传统依赖地质人员专业基础的断层识别方法，基于数学统计和空间拟合的识别模型具有自动化程度高的特点。为此，依据断层两盘高程相异特性和煤层错断前埋深相似性特征，提出了基于瓦斯抽采孔数据，采用聚类分析方法，识别采煤工作面内小断层的思路。对比分析了不同聚类算法的原理和结构，建立了基于K-Means聚类算法的煤层小断层识别模型；设计了小断层识别的关键技术流程：采用手肘法求解最佳聚类簇数，以戴维森堡丁指数和相关系数作为识别精度评价标准，通过异常点识别、断层参数(走向、倾角、落差)计算、断层面拟合、三维可视化等技术手段，实现煤层小断层识别；利用现场采煤工作面底抽巷的部分瓦斯抽采孔数据，识别出落差为3 m和1 m的断层，结合断层实际揭露情况和工作面可视化结果分析。结果表明，现场揭露情况与模型计算结果基本一致，识别方法可用于煤层工作面内断层的识别。
- 采煤工作面 /
- 断层识别 /
- 聚类分析 /
- 瓦斯抽采 /
- 空间拟合
Abstract: Minor faults within mining faces of coal mines severely affect gas drainage and coal seam mining. Accurately identifying parameters such as positions, throws, and attitudes of these faults is of great significance for the safe production of coal mines. To effectively reduce gas emissions, prevent gas explosions, and exploit and utilize gas resources, many gas drainage holes have been drilled during the construction of coal mines, providing favorable engineering conditions for identifying minor faults within coal seams. Compared with the conventional fault identification methods, which rely on the expertise of geologists, the identification model based on mathematical statistics and space fitting enjoys a high degree of automation. Therefore, based on the characteristics that two walls of a fault show different elevations and that coal seams have similar burial depths before being offset, as well as the data from gas drainage holes, this study proposed a philosophy for identifying minor faults within mining faces using the cluster analysis method. By comparing principles and structures of different clustering algorithms, this study built a model for identifying minor-faults in coal seams based on the K-Means clustering algorithm. The key technical process is as follows: the optimal number of clusters was determined first using the elbow method; with the Davies-Bouldin index and the correlation coefficients as the criteria for the evaluation of identification accuracy, minor faults in coal seams were finally identified using technological means such as anomalous point identification, the calculation of fault parameters (strikes, dip angles, and throws), fault plane fitting, and 3D visualization were employed. Using the identification model proposed in this study, faults with throws of 3 m and 1 m were identified on site using the data from partial gas drainage holes in the bottom drainage roadway of mining faces. As indicated by the comparative analysis of the faults revealed on site and the visualization results of the mining faces, the faults revealed on site are consistent with the results calculated using the model. Therefore, the identification method proposed in this study can be employed to identify faults within the mining faces of coal seams.
- mining face /
- fault identification /
- cluster analysis /
- gas drainage /
- space fitting

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图 1 钻孔平面分布

Fig. 1 Planar distribution of boreholes

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图 2 钻孔终孔高程计算剖面

Fig. 2 Calculation profile for the final hole elevation of a borehole

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图 3 钻孔分布示意

Fig. 3 Schematic showing borehole distribution

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图 4 小断层识别技术流程

Fig. 4 Technical flow chart of minor fault identification

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图 5 异常点识别结果

Fig. 5 Anomalous point identification results

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图 6 异常点分类

Fig. 6 Anomalous point classification

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图 7 拟合走向线

Fig. 7 Fitted fault strikes

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图 8 拟合断层面

Fig. 8 Fitted fault planes

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图 9 断层三维可视化

Fig. 9 3D visualization of faults

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图 10 等高线法验证识别断层识别结果

Fig. 10 Verification of fault identification results using contours

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表 1 聚类算法优缺点对比

Table 1 Comparison of clustering algorithms

类型	算法	优点	缺点
聚类算法	K-Means	原理简单，聚类效果好，适用于大多数聚类场景	最佳聚类簇数k值影响算法的准确度，不好把握
聚类算法	凝聚层次聚类	相似度定义灵活限制少，不需要预先制定聚类数	对于高维数据，运算深度和复杂度太高
最佳聚类簇数求解	手肘法	适用于高维数据样本，数据变化单一，易观察拐点	斜率变化有波动时，会出现误判拐点，需要人工解译
最佳聚类簇数求解	轮廓系数法	对数据的分布没有假设	凸型的类上，结果存在虚高

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表 2 聚类精度评价指标对比

Table 2 Comparison of clustering accuracy evaluation indices

评价指标	优点	缺点
CH分数	计算复杂度低，运行速度快，当簇类密集且分离较好时，分数较高	凸型类评价结果偏高，不适合基于密度的聚类评价
戴维森堡丁指数	综合性评价指标，容易计算	采用欧氏距离作为评价因子时，不适合环状聚类评价
轮廓系数	原理简单	计算复杂度太高，不适合基于密度的聚类评价

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表 3 断层识别评价指标

Table 3 Fault identification evaluation indices

评价参数	计算公式
走向误差F_T	$ {F_{\text{T}}} = {T_{{\text{iden}}}} - {T_{{\text{real}}}} $
倾角误差F_P	$ {F_{\text{p}}} = {P_{{\text{iden}}}} - {P_{{\text{real}}}} $
落差误差F_D	$ {F_{\text{D}}} = {D_{{\text{iden}}}} - {D_{{\text{real}}}} $
注：T为走向；P为倾角；D为落差；以iden为下标的参数为拟合断层的参数；以real为下标的参数为真实揭露断层的参数；F_T、F_P、F_D分别为走向、倾角及落差的误差。

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表 4 断层参数对比

Table 4 Comparison of fault parameters

分类	A研究区			B研究区
分类	走向/(°)	倾角/(°)	落差/m	走向/(°)	倾角/(°)	落差/m
真实断层	340.0	45.0	1.0	137.0	50.0	3.0
拟合断层	338.5	41.7	0.9	135.3	47.3	2.8
误差	1.5	3.3	0.1	1.7	2.7	0.2

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