基于MapObjects与ANN耦合技术的底板突水危险性评价

发布时间：2018-05-01 06:10

本文选题：底板突水 + MapObjects　；参考：《太原理工大学》2013年硕士论文

【摘要】：长期以来,受煤层底板突水的困扰,导致许多矿井的生产能力达不到设计标准,严重影响了煤矿的正常生产。因此,查清矿井水文地质条件、研究矿井突水机理与底板突水危险性评价,对防治煤层底板突水实现煤矿安全生产具有非常重要的理论与现实意义。本文通过分析底板突水影响因素,使用底板突水理论,获得影响新河煤矿煤层底板突水的主要地质因素,包括：断裂强度、断裂密度、隔水层厚度、奥灰水压以及奥灰顶部富水性等,为数值计算提供了基础资料。其中断裂在空间分布上具有不连续性的特点,故断裂密度和断裂强度为不连续性指标,隔水层厚度、奥灰水压以及奥灰顶部富水性在空间分布上具有连续性的特点,称为连续性指标,作为煤层底板突水危险性评价指标体系的指标参数。利用MapObjects强大的空间分析功能与人工神经网络(ANN)高度的非线性特性的耦合进行空间信息处理。本文为实现MapObjects与ANN的耦合,特研发了MGSPA2008,该系统具有强大的空间分析功能和显示功能。首先利用该软件画出矿区地图,并生成评价单元,把断层文件与评价单元输入并与评价单元叠加,将隔水层厚度、奥灰水压以及奥灰顶部富水性指数导入系统数据层,通过空间分析,统计分析出每一个评价单元的断裂强度、含水层水压、阻水层的厚度、断裂密度和含水层富水性指数。将上述指标参数导入人工神经网络(ANN),利用训练好的BP网络对煤层底板突水进行危险性评价,提高了突水危险性评价的准确性。根据评价结果按照突水危险性程度将评价区域划分为危险区、较危险区、过渡区和安全区。实践证明评价结果符合实际情况,为煤矿合理的安排生产工作和预防煤矿突水提供了依据。
[Abstract]:For a long time, because of the water inrush from coal seam floor, the production capacity of many coal mines can not reach the design standard, which seriously affects the normal production of coal mine. Therefore, it is of great theoretical and practical significance to investigate the hydrogeological conditions of coal mines and study the mechanism of water inrush and the assessment of the risk of water inrush from the floor of the coal seam in order to prevent and control the water inrush from the floor of coal seam to realize the safe production of coal mine. By analyzing the influencing factors of floor water inrush and using the theory of floor water inrush, the main geological factors influencing water inrush of coal seam floor in Xinhe coal mine are obtained, including fracture strength, fracture density, thickness of water barrier layer. The water pressure of the Ordovician ash and the wateriness of the top of the Ordovician ash provide the basic data for the numerical calculation. Among them, faults have discontinuity in spatial distribution, so fracture density and fracture strength are discontinuity indexes, the thickness of water-barrier layer, the water pressure of Ordovician ash and the water-rich water of the top of Ordovician ash have the characteristics of continuity in spatial distribution. It is called continuity index, which is the index parameter of water inrush risk evaluation index system of coal seam floor. The spatial information processing is based on the coupling of the strong spatial analysis function of MapObjects and the high nonlinearity of Ann. In order to realize the coupling between MapObjects and ANN, this paper develops MGSPA2008.This system has powerful spatial analysis function and display function. Firstly, the map of mining area is drawn out by the software, and the evaluation unit is generated. The fault file is input with the evaluation unit and superimposed with the evaluation unit. The thickness of the water-barrier layer, the water pressure of the Ordovician ash and the water-rich index of the top of the Ordovician ash are introduced into the data layer of the system. Through spatial analysis, the fracture strength, aquifer water pressure, thickness of water barrier layer, fracture density and water-rich index of each evaluation unit are analyzed statistically. The above parameters are introduced into the artificial neural network and the trained BP network is used to evaluate the risk of water inrush from coal seam floor. The accuracy of water inrush assessment is improved. According to the evaluation results, the evaluation area is divided into dangerous area, relatively dangerous area, transitional area and security zone according to the degree of water inrush. It is proved by practice that the evaluation result accords with the actual situation and provides the basis for rational arrangement of production work and prevention of water inrush in coal mines.
【学位授予单位】：太原理工大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TD745

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