基于二项logistic回归模型与CART树的煤层底板突水预测

为定量评价煤层底板突水信息对突水过程的影响程度,获得煤层底板突水规则,采用二项logistic回归与CART树相结合的方法进行煤层底板突水预测。在煤层底板突水信息分析的基础上,建立了包含全因素的煤层底板突水预测概率模型,基于向后逐步回归分析方法获得了包含6项主要突水信息的精简煤层底板突水预测概率模型。通过CART树算法获得了煤层底板突水规则,分类测试结果表明,所获得的突水规则分类准确率达到91.67%。     

Assessment of water inrush and factor sensitivity analysis in an amalgamated coal mine in China

Hydrogeological data are generally incomplete and inaccurate in amalgamated coal mines in China, which results in inaccuracy in water inrush forecasts. To enhance the precision of the prediction of water inrush from coal floor in an amalgamated coal mine, the vulnerability index method was developed using an analytic hierarchy process (AHP) to analyze the water inrush hazard. Six factors related to water inrush were selected and the corresponding single factor thematic map was established through geographic information system (GIS). The AHP model was built to calculate the weight of each factor. The final forecast map based on vulnerability index was acquired by superposing the six thematic maps. The forecast map was consistent with the real water inrush position. The sensitivity of the six factors was analyzed and the water-resisting layer played a significant role in controlling water inrush. Several suggestions about water inrush prevention were put forward based on the prediction results.     

基于PCA-SVR的煤层底板突水量预测

提出了一种基于主成分分析支持向量机回归(PCA-SVR)的煤层底板突水预测方法,用主成分分析来解决输入变量的选择问题。主成分以较少的维数包含了高维变量所携带的大部分信息,这不仅避免了过多的输入导致训练速度慢,同时也保证了预测准确度。实例表明,所提方法可有效消除众多影响因素间的相关性,减少输入变量个数,提高预测效率和精度。     

数值模拟在回坡底煤矿底板突水防治中的应用

随着煤矿开采深度的不断增加,将面临高承压水的严重威胁,带压开采已成为深部煤炭资源开采的主要方式。应用RFPA2D-Flow系统对煤层底板破坏深度进行了数值模拟,得出了回坡底煤矿采煤工作面煤层底板岩层的破坏深度约为12 m;同时,当回采到110 m处时,自切眼向掘进方向50～80 m处出现漏斗状底板破坏区,该破坏区将可能导通底部奥灰水,使煤层底板发生突水。     

基于粗糙集的Logistic回归模型在矿井突水模式识别中的应用

矿井突水模式识别是一个非正态、非线性和高维数据处理问题,也是二分类问题。使用粗糙集属性约简算法对样本数据降维,建立Logistic回归模型,并利用粒子群算法对模型参数优化。该模型对建模样本突水模式识别正确率为90%,对测试样本突水模式识别正确率为100%,效果好于数据不降维的Logistic回归模型。该模型克服了线性回归分析解决二分类问题存在的不足,为矿井突水模式识别提供了一种新思路、新方法。     

矿井突水水源类型判别模型及其系统实现

系统介绍了矿井突水水源快速识别系统的组成,阐述了煤矿突水水源判别的3种数学模型及其适用条件,从应用的角度介绍了该软件的使用步骤。采用该系统软件可高效准确地完成未知样品的水源类型判别,为煤矿安全生产提供决策依据。     

Assessment of regional drought vulnerability and risk using principal component analysis and a Gaussian mixture model

Due to the complex characteristics of drought, drought risk needs to be quantified by combining drought vulnerability and drought hazard. Recently, the major focus in drought vulnerability has been on how to calculate the weights of indicators to comprehensively quantify drought risk. In this study, principal component analysis (PCA), a Gaussian mixture model (GMM), and the equal-weighting method (EWM) were applied to objectively determine the weights for drought vulnerability assessment in Chungcheong Province, located in the west-central part of South Korea. The PCA provided larger weights for agricultural and industrial factors, whereas the GMM computed larger weights for agricultural factors than did the EWM. The drought risk was assessed by combining the drought vulnerability index (DVI) and the drought hazard index (DHI). Based on the DVI, the most vulnerable region was CCN9 in the northwestern part of the province, whereas the most drought-prone region based on the DHI was CCN12 in the southwest. Considering both DVI and DHI, the regions with the highest risk were CCN12 and CCN10 in the southern part of the province. Using the proposed PCA and GMM, we validated drought vulnerability using objective weighting methods and assessed comprehensive drought risk considering both meteorological hazard and socioeconomic vulnerability.

     

赵官井田10煤层底板突水危险性评价

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、\     

利用水化学特征识别桑树坪煤矿突水水源

及时准确地找到突水水源,是解决矿井突水问题的关键。通过对桑树坪煤矿主要含水层水样进行常规水质分析,并通过Piper三线图揭示了矿区不同地下水含水层的水化学特征,并通过出水点与背景值的水文地球化学特征对比,正确地判断出了该矿区突水水源为奥灰岩溶水。研究认为,水化学特征分析是一种快速判别突水水源的有效方法。     

因子分析法在矿井涌水来源判别中的应用

结合恒源煤矿六八采区实例,采用因子分析方法研究矿井涌水水源。取各充水含水层样品与采区涌水样品的主要离子含量指标为分析对象,经过系列处理得到正交因子载荷矩阵。用因子1和因子2分别代表太灰水和煤系砂岩水,在因子载荷坐标系中,六八采区涌水水样则分布在因子1和因子2之间,并同时具有很高的共同度,充分证明了六八采区涌水属于太灰水与煤系砂岩裂隙水之间的过渡类型。     

矿井煤层底板突水预测新方法研究

本文针对煤矿矿井煤层底板突水系统为一非线性系统的特性,提出采用对非线性问题具有良好适用性的人工神经网络系统（以下简称神经网络）,进行煤层底板突水预测。以作者们研制,使用神经网络的实践为基础,阐述系统、建模方法、适用条件和应用问题,并在焦作矿务局演马庄矿、焦作金科尔集团方庄煤矿对所建立的煤层底板突水预测神经网络进行生产性检验,取得良好的结果,说明该系统应用于煤层底板突水预测的可靠性。     

深埋特长隧道的突涌水危险性评价研究

为评价隧道突涌水风险,确保施工安全,本文结合工程实例,利用层次分析法和模糊理论构建了隧道突涌水危险性评价模型,并利用P×C分级法确定隧道突涌水危险性等级。实例检验表明：该模型的权值求解方法具操作简单、准确性高等优点,能有效通过一致性检验,且隶属度求解过程有效综合了定性分析与定量评价,保证了分析结果的准确性;同时,该评价模型可定量评价隧道突涌水的危险性等级,且判别结果与现场实测值的判别结果具较好的一致性,验证了该评价模型的可靠性和准确性,为隧道突涌水危险性评估提供了一种有效途径。     

滇东南高原岩溶盆地区普阳煤矿的深层涌水趋势分析

普阳煤矿位于一断陷盆地的浅部新近系地层中,矿区被3种类型的水文地质边界圈闭。煤层分布标高低于暗河出口100～230 m,煤田四周及盆地基底均为岩溶含水层,其承压水头高于煤层底板67～268 m,预计最低开采标高的平均承压水头约206 m。为解决矿区地下水突水威胁,监测普阳河水流入和流出的水量,根据水均衡原理及矿坑充水要素,制定中长期排水方案。研究结果表明,矿区岩溶发育垂向分带特征清楚,煤层底板以下岩溶含水层以弱岩溶带为主,单元内93%的地下水通过普阳暗河集中排泄,加之煤层以下有一定厚度的隔水层阻隔,故深层开采时可能发生局部突水危害,但水量不大,最大涌水量仅限于自然状态下补给普阳河的地下水径流量。结合矿坑充水控制因素及地下水动力学分析,采用水均衡法评价突水量,方便可行。     

铜川玉华煤矿顶板离层水突水机理与防治

陕西铜川玉华煤矿一盘区1418工作面,回采期间发生多次突水事故,对煤矿的正常生产和人员的生命安全构成严重威胁。从本井田地质和水文地质资料入手,分析了工作面突水的背景、水源、通道等,得出洛河组下段砂岩裂隙含水层水为离层空间充水的主要水源,煤层采动后形成的导水裂隙带为主要的导水通道。通过理论分析和模拟研究覆岩移动破坏规律,分析确定导水裂隙带发育过程和离层空间可能发育的位置,离层水形成、发展及突水过程,从而总结出玉华井田1418工作面顶板离层水突水机理,为矿井后期实现安全高效生产提出切实有效的防治水措施打好基础。     

Landslide hazard mapping using logistic regression model in Mackenzie Valley,Canada

A logistic regression model is developed within the framework of a Geographic Information System (GIS) to map landslide hazards in a mountainous environment. A case study is conducted in the mountainous southern Mackenzie Valley, Northwest Territories, Canada. To determine the factors influencing landslides, data layers of geology, surface materials, land cover, and topography were analyzed by logistic regression analysis, and the results are used for landslide hazard mapping. In this study, bedrock, surface materials, slope, and difference between surface aspect and dip direction of the sedimentary rock were found to be the most important factors affecting landslide occurrence. The influence on landslides by interactions among geologic and geomorphic conditions is also analyzed, and used to develop a logistic regression model for landslide hazard mapping. The comparison of the results from the model including the interaction terms and the model not including the interaction terms indicate that interactions among the variables were found to be significant for predicting future landslide probability and locating high hazard areas. The results from this study demonstrate that the use of a logistic regression model within a GIS framework is useful and suitable for landslide hazard mapping in large mountainous geographic areas such as the southern Mackenzie Valley.     

Assessment of groundwater water quality in central and southern Gulf Coast aquifer,TX using principal component analysis

Principal component analysis has been applied for source identification and to assess factors affecting concentration variations. In particular, this study utilizes principal component analysis (PCA) to understand groundwater geochemical characteristics in the central and southern portions of the Gulf Coast aquifer in Texas. PCA, along with exploratory data analysis and correlation analysis is applied to a spatially extensive multivariate dataset in an exploratory mode to conceptualize the geochemical evolution of groundwater. A general trend was observed in all formations of the target aquifers with over 75 % of the observed variance explained by the first four factors identified by the PCA. The first factor consisted of older water subjected to weathering reactions and was named the ionic strength index. The second factor, named the alkalinity index explained greater variance in the younger formations rather than in the older formations. The third group represented younger waters entering the aquifers from the land surface and was labeled the recharge index. The fourth group which varied between aquifers was either the hardness index or the acidity index depending on whether it represented the influences of carbonate minerals or parameters affecting the dissolution of fluoride minerals, respectively. The PCA approach was also extended to the well scale to determine and identify the geographic influences on geochemical evolution. It was found that wells located in outcrop areas and near rivers and streams had a larger influence on the factors suggesting the importance of surface water–groundwater interactions.     

Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model

A remote sensing and Geographic Information System-based study has been carried out for landslide susceptibility zonation in the Chamoli region, part of Garhwal Himalayas. Logistic regression has been applied to correlate the presence of landslides with independent physical factors including slope, aspect, relative relief, land use/cover, lithology, lineament, and drainage density. Coefficients of the categories of each factor have been obtained and used to assess the landslide probability value to ultimately categorize the area into various landslide susceptibility zones; very low, low, moderate, high, and very high. The results show that 71.13% of observed landslides fall in 21.96% of predicted very high and high susceptibility zone, which in fact should be the case. Furthermore, lineament first buffer category (0–500 m) and the east and south aspects are the most influential in causing landslides in the region.