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1.
Prediction of Floor Water Inrush: The Application of GIS-Based AHP Vulnerable Index Method to Donghuantuo Coal Mine,China 总被引：6，自引：0，他引：6
Qiang Wu Yuanzhang Liu Donghai Liu Wanfang Zhou 《Rock Mechanics and Rock Engineering》2011,44(5):591-600
Floor water inrush represents a geohazard that can pose significant threat to safe operations for instance in coal mines in
China and elsewhere. Its occurrence is controlled by many factors, and the processes are often not amenable to mathematical
expressions. To evaluate the water inrush risk, the paper proposes the vulnerability index approach by coupling the analytic
hierarchy process (AHP) and geographic information system (GIS). The detailed procedures of using this innovative approach
are shown in a case study in China (Donghuantuo Coal Mine). The powerful spatial data analysis functions of GIS was used to
establish the thematic layer of each of the six factors that control the water inrush, and the contribution weights of each
factor was determined with the AHP method. The established AHP evaluation model was used to determine the threshold value
for each risk level with a histogram of the water inrush vulnerability index. As a result, the mine area was divided into
five regions with different vulnerability levels which served as general guidelines for the mine operations. The prediction
results were further corroborated with the actual mining data, and the evaluation result is satisfactory. 相似文献
2.
AHP法是煤层底板突水预测预报的关键技术之一,但传统基于“1~9”标度的AHP法往往存在一致性效果不够理想等问题.通过对AHP法的改进研究,提出了基于“10/10~18/2”标度的改进AHP法型脆弱性指数法评价技术.以成庄矿3#、9#和15#煤层底板奥灰突水脆弱性评价为例,在建立各主控因素专题层图基础上,应用基于“10/10~18/2”新标度的改进AHP法,确定了各主控因素的权重;进一步建立了煤层底板奥灰突水的脆弱性评价模型,得出了各煤层脆弱性评价分区.研究表明,改进的AHP法构建的判断矩阵具有较好的一致性;通过与传统突水系数法评价结果对比可知,基于GIS的改进AHP型脆弱性指数法评价能够真实反映多因素影响下煤层底板突水的非线性动力过程,评价结果更为吻合实际. 相似文献
3.
The prediction and prevention of floor water inrush is directly related to the safety of the coal mine production. The previous evaluation method of floor water inrush was more one-sided and lacked main control factors related to mining conditions. In order to evaluate the floor water inrush more accurately, under the project background of geological data of Wanglou coal mine, stope width, mining depth, fault scale index, water pressure, water abundance and thickness of aquifer were selected as main controlling factors of floor water inrush. Combined with the subjective weight analytical hierarchy process and the objective weight variation coefficient method, the weight coefficients corresponding to the main controlling factors were obtained respectively. The thematic map of the risk assessment of coal seam floor water inrush was drawn by combining the constructed comprehensive weight vulnerability index model and geographic information system. The results show that: ① according to the actual geological data of mine, two fault related factors were removed. And stope width and mining depth were increased as the main controlling factors to evaluate floor water inrush. It is easier to compare and calculate the weight of evaluation factors. ② The constructed comprehensive weight vulnerability index model can comprehensively evaluate the risk of floor water inrush. And the results of the evaluation are more accurate. ③ The related thematic maps can directly reflect the risk of floor water inrush, which is of guiding significance for the prediction and prevention of coal seam floor water inrush. 相似文献
4.
The no. 11 coal seam in the deep area of Hancheng mining area is mining in recent years, which is threatened by the water inrush from the Ordovician limestone aquifer. Coal-floor water inrush is governed by the water abundance of coal-floor aquifer, the water-resisting performance of coal-floor aquitard, and the pathway connecting the water source and the working face. To make an accuracy risk assessment of water inrush from the no. 11 coal seam floor, a GIS-based vulnerability index method (VIM) is adopted for its superior comprehensive consideration of more controlling factors, powerful spatial analysis, and intuitively display functions. This study firstly established an index system including the water pressure of the coal-floor aquifer, the unit water inflow, the thickness, the core recovery percentage, the thickness ratio of brittle rocks to ductile rocks, the thickness of effective aquitard, and the accumulated length of faults and folds, of which the former six indexes governed the water abundance of the coal-floor aquifer which was combined with the last two factors to determine the risk of coal-floor water inrush. Secondly, the thematic map of each controlling factor is established by GIS using the geological prospecting data, and the weight of each factor is determined by the analytic hierarchy process (AHP) after consulting the expert review panel. At last, a vulnerability index is obtained and used to assess the risk of coal-floor water inrush of the no. 11 coal seam. The risk of water inrush of the no. 11 coal seam of the study area was ranked to three zones: the southeastern shallow area in red color is the dangerous zone, the wide northwestern area in green color is the safe zone, and the transition area in yellow color is the moderate-risk zone. Compared with the actual water-inrush incidents, the risk assessment result was verified to achieve an accuracy of 82.35%, which is proved to be a dependable reference for the prevention and controlling of coal-floor water inrush of the no. 11 coal seam in Hancheng mining area. 相似文献
5.
In order to improve the accuracy of floor water inrush assessment, the risk prediction model of floor water inrush was established by combining the principal component logistic regression analysis (PCLRA) and GIS spatial geographic analysis. In this paper, the geological data of Pandao coal mine was taken as the engineering background. First of all, main controlling factors of floor water inrush were determined and quantified. Next, PCLRA was used to determine the weight of each factor and establish the mathematical model for predicting the floor water inrush. And then, GIS’s spatial analysis and data processing function was used to draw related single factor thematic maps. Related thematic maps were weighted superposed to draw a floor water inrush zoning map based on PCLRA mathematical model. The study areas were divided into five levels by Jenks optimization method and vulnerability index initial model. And the corresponding threshold range was determined. The results show that (1) the high sensitivity factors in floor failure depth were added to evaluate the water inrush, and the fault fractal dimension was used to replace the fault structure related factors, and the main controlling factors of floor water inrush are more comprehensive; (2) the fitting degree of PCLRA model is high and the test accuracy is 83.3%; (3) the prediction results were well fitted to the actual position of water inrush (three water inrush points are located in the dangerous area, and two water inrush points are located in the relatively dangerous area). 相似文献
6.
针对传统的AHP型脆弱性指数法在权重确定方面的不足,采用熵权法对其改进,将熵权法计算的客观权重与AHP法计算的主观权重进行加权平均,综合确定各主控因素诱发突水的权重比例。在综合分析长平煤矿水文地质资料的基础上,应用GIS建立主控因素专题图并进行归一化处理,运用基于GIS的改进AHP型脆弱性指数法确定各主控因素权重,建立井田西部3号煤底板奥灰突水危险性评价模型。利用自然间断点分级法确定了分区阈值,将研究区按突水危险性的相对大小分为5个区域。评价结果符合实际,对煤矿安全生产具有重要的现实意义。 相似文献
7.
Evaluation and zoning of groundwater hazards in Pingshuo No. 1 underground coal mine,Shanxi Province,China 总被引：4，自引：1，他引：3
Coal mining safety has been compromised with water inrushes from aquifers either overlying or underlying the coal seams. Detailed studies of the associated hydrogeological conditions in China have led to different approaches to mitigate the water inrush risks from these two types of aquifers—the ‘three diagram method’ for overlying-aquifer water inrushes and the ‘vulnerability index method’ for underlying-aquifer water inrushes. The ‘three diagram method’ consists of: (1) aquifer water-abundance distribution charts derived from a geographic information system and analytic hierarchy process based water-abundance index model; (2) a fracture height map showing mining-induced fractures above the coal seam, established with stratified numerical simulations; and (3) a comprehensive partition map identifying the overlying-aquifer water inrush risk. The ‘vulnerability index method’ uses site-specific data to establish thematic maps for major factors that affect the underlying-aquifer water inrushes, whereas the weight of each control factor is determined by the analytic hierarchy process. The calculated vulnerability index is indicative of water inrush risks. The effectiveness of these methods is illustrated with a case study at the Pingshuo No. 1 underground coal mine, Shanxi Province, China. 相似文献
8.
从地质构造、含水层、隔水层、开采条件等方面详细分析了赵官井田10煤层底板突水的影响因素,确定了断层强度指数、褶皱分维值、"底板充水含水层组"水压、"底板充水含水层组"富水性、奥灰富水性、隔水层厚度、泥岩比率、底板破坏深度8个主控因素作为10煤层底板突水危险性评价的决策指标,并建立了各主控因素专题图;运用层次分析法(AHP)确定了各主控因素的权重系数,建立了基于"脆弱性指数法"的底板突水危险性评价模型,对10煤层底板突水危险性进行了定量评价,结果表明:在井田的南部煤层露头处脆弱性指数小,突水可能性较小;在井田的北部,特别是在井田东北部,脆弱性指数大,突水危险性较大。 相似文献
9.
10.
Groundwater inrush is a geohazard that can significantly impact safe operations of the coal mines in China. Its occurrence
is controlled by many factors and processes are often not amenable to mathematical expressions. To evaluate the water inrush
risk, Professor Wu and his colleagues have proposed the vulnerability index approach by coupling the artificial neural network
(ANN) and geographic information system (GIS). The detailed procedures of using this innovative approach are shown in a case
study. Firstly, the powerful spatial data analysis functions of GIS was used to establish the thematic layer of each of the
main factors that control the water inrush, and then to choose the training sample on the thematic layer with the ANN-BP Arithmetic.
Secondly, the ANN evaluation model of the water inrush was established to determine the threshold value for each risk level
with a histogram of the water inrush vulnerability index. As a result, the mine area was divided into four regions with different
vulnerability levels and they served as the general guidelines for the mine operations. 相似文献
11.
12.
针对当前煤层底板突水预测存在的问题,在突水概率指数法预测预报系统的基础上,用matlab开发出了一套新型煤层底板突水预测系统软件。通过对地质、水文地质等信息数据进行分析处理,从而确定导致煤层底板突水的主控因素及次级影响因素,并分别赋予其相应的权重值,将各因素在底板突水中所起的作用定量化。特别是对于不同矿区不同控制因素的影响,其相应权重值的大小可以灵活改变。建立赋权求和数学模型,绘制出各个主要控制因素的专题图,并根据各个主要控制因素的不同权重值,叠合绘制出底板突水概率指数法突水分区图。同时计算出煤层底板突水概率指数。将系统软件应用于工程实际,预测效果与实际情况相吻合。 相似文献
13.
Groundwater inrushes often occur in the coal mines of China. One of the water sources is the aquifers underlying the coal
seams. Because such a water hazard is affected by many factors, data collected from various sources need to be evaluated to
predict its occurrence. This paper introduces an innovative approach in which the water inrush risk is represented by the
vulnerability index. This method combines the geographic information system and the artificial neural network. The artificial
neural network is used to estimate the weight of each factor. Unlike the traditional prediction method in which two controlling
factors are often evaluated without regard to their relative importance, this new approach incorporates multi-factors and
describes the non-linear dynamical processes. 相似文献
14.
为解决煤层顶板突水预测预报评价难题,在提出了富水性指数和突水危险性指数的基础上,以鄂尔多斯盆地西缘新上海一号煤矿为研究对象,应用富水性指数和突水危险性指数的双图,对三个采煤工作面顶板突水危险性进行评价,结果表明,采煤活动位于富水区又位于突水危险区是顶板突（涌）水的充要条件。 相似文献
15.
为克服层次分析法主观确权的弊端,同时避免熵权客观确权与主控因素实际重要程度相悖的错误,以山西长治三元煤矿3号煤层底板奥灰突水危险性评价为例,将熵权与层次分析耦合确定煤层底板突水主控因素权重并建立脆弱性指数模型,采用K均值聚类法对脆弱性指数值进行聚类分区,确定阈值3个,按照突水危险程度将研究区分为安全区、相对安全区、过渡区和危险区4个区域。经对比验证评价结果符合实际情况。 相似文献
16.
针对煤层底板突水预测问题,在总结现有突水预测方法和理论的基础上,通过特征选择实验得出水压、距工作面距离、砂岩段厚度、煤层厚度、煤层倾角、断层落差、裂隙带、开采面积、采高、走向长度是影响突水发生的主要因素,这些因素具有复杂、非线性的特点。提出基于长短时记忆（LSTM）神经网络构建的突水预测模型,将煤矿突水实例的数据作为样本数据对模型进行训练。最后,将LSTM神经网络模型与遗传算法-反向传播（GA-BP）神经网络模型和反向传播（BP）神经网络模型进行对比实验。实验结果表明,LSTM神经网络模型在测试集上的预测正确率更高,稳定性更好,更适用于煤层底板突水预测。 相似文献
17.
煤层深部开采使得煤矿底板水害事故频发,传统突水危险性评价方法评价指标单一、评价结果偏离实际的弊端逐渐显露,造成众多新型评价方法涌现。以河北省华北型煤田东欢坨矿为研究对象,选取含水层性能、隔水层性能、地质条件、煤层条件的评价因素集,综合考虑10个评价因素,建立适用于东欢坨矿的底板突水危险性评价指标体系;利用层次分析法确定各指标主观权重,利用CRITIC法确定各指标客观权重,将2者耦合得到综合权重,兼顾专家主观经验与数据客观信息,保证权重确定的全面性;引入加权秩和比法,构建评价矩阵,依据指标对评价对象所产生的优劣性影响将其分为高优型指标和低优型指标,编秩计算WRSR值,对数据进行分档排序,确定安全、较安全、较危险、危险4个评价等级区间,形成评价模型;利用GIS强大的空间管理及信息处理功能,完成结果的信息展示;将评价结果与实际工程出水位置相比较,发现突水位置都在底板突水较危险区域,并与传统评价方法突水系数法相对比,证明评价模型有效。研究成果形成了煤层底板突水危险性评价新方法,丰富煤层底板突水危险性评价方法的种类,为煤矿防治水工作者提供新思路。 相似文献
18.
准确圈定煤矿工作面底板突水预警重点监测区域,实现监测位置和潜在突水点位置在空间上的匹配,是突水灾害预警急需解决的问题之一。为研究煤矿工作面底板突水灾害预警重点监测区域评价技术,采用水文地质分析、GIS空间分析及ANN预测等技术手段,建立了底板突水灾害预警重点监测区域评价指标体系,提出了将不连续指标转化为连续指标的方法,建立了评价模型,研发了重点监测区域评价GIS系统,实现了煤矿底板突水灾害预警重点监测区域GIS与ANN耦合评价技术,最后以赵庄煤矿5303回采工作面底板突水监测预警为例,利用研发的系统圈定了该工作面重点监测区域。研究表明,确定预警重点监测区域的影响因素主要有含水层水压、含水层富水性、含水层防（隔）水煤岩柱厚度、老空区危险性指数、断层危险性指数、陷落柱危险性指数和封闭不良钻孔危险性指数,利用分段函数可以有效将不连续指标转化为连续指标,研发的评价系统可以实现煤矿突水灾害预警监测位置自动评价,评价结果与现场揭露及水害预警系统监测结果一致。 相似文献
19.
为更好地解决支持向量机(SVM)核参数和惩罚因子的取值对煤层底板突水量等级预测精度的影响问题,提出利用全局搜索能力较强的粒子群优化(PSO)算法优化支持向量机参数。选取含水层水压、隔水层厚度、岩溶发育程度、断层规模等作为影响煤层底板突水量等级的因素,利用华北聚煤区煤层底板突水的实测数据进行训练,建立了煤层底板突水量等级预测的粒子群-支持向量机(PSO-SVM)模型,并将其应用于其他样本的预测。应用表明:模型能够较好地解决煤层底板突水量等级预测中存在的小样本、非线性等问题,预测结果与实际情况吻合程度高,具有较强的实用性和有效性。 相似文献
20.
Mining-induced water inrush is a sudden and destructive underground disaster caused by a mining disturbance. This disaster occurs frequently in the northern region of Shaanxi province in China due to overburden fractures in shallow seam mining, which pose a great threat to residents’ safety. It is therefore essential to construct an accurate prediction model. This study first applies selection hierarchy analysis to the main controlling factors of roof water inrush to study their weights using an analytic hierarchy process (AHP) including five factors: surface water catchment features, wateriness of the aquifer, water-resistant characteristics of aquiclude, combined influence of overburden, and mining disturbance characteristics. The grey relational analysis (GRA) method is used to calculate the correlation degree of each water inrush. The AHP-GRA method presents a comprehensive evaluative model combining the advantages of both approaches to analyze mining safety. Qualitative and quantitative indicators of the roof water inrush prediction model in shallow seam mining are established. Secondly, risk prediction of roof water inrush points and comprehensive water inrush is determined using engineering examples from the Hanjiawan coal mine. Results indicate that during safety mining, water inflow data are consistent with our prediction, thereby substantiating the model’s accuracy and providing a new method for predicting roof water inrush in shallow seam mining. 相似文献