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1.
采空塌陷危险性评价是编制地质灾害防治规划、开展地质灾害防治与监测预警工作的重要依据。本文采用层次分析法与频率比模型相结合构建了采空塌陷危险性评价模型（AHP-PF组合模型）。以沈阳市蒲河-清水矿区为例,考虑了地质条件、地表特征、开采条件等3方面影响因素,选取了第四纪覆盖类型、第四纪松散层厚度、地质构造复杂程度、可采煤层顶板强度指标、煤层倾角、地表沉陷速率、采深采厚比、采空区叠置层数等8个评价指标,利用AHP-PF组合模型计算各指标权重及频率比,最后进行采空塌陷危险性分区。评价结果表明,采空塌陷危险性高区主要集中在采深采厚比小、沉陷速率大及目前仍在开采的区域,该区域是地质灾害防治、搬迁避让的重点区域。 相似文献
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基于T-S模糊神经网络的采空塌陷危险性判别 总被引：1，自引：0，他引：1
采空区地面塌陷的危险性判别受地质因素、采矿因素等多重因素的影响,各因素往往影响程度不同且部分因素之间又相互联系。为了能够较准确地对采空塌陷危险性进行评估,引入了T-S模糊神经网络模型。以北京西山地区采空塌陷为例,根据塌陷特点,分别选取了地质构造复杂程度、覆盖层类型、第四系覆盖层厚度、覆岩强度、煤层倾角、采深采厚比、采空区埋深、采空区空间叠置层数8项影响因素作为评价指标,并建立了分级标准。将单因素评价指标均匀线性插值作为训练样本,建立了T-S模糊神经网络判别模型。利用训练好的神经网络模型对选取的8处采空区进行评估,结果分别为:Ⅰ、Ⅱ、Ⅲ、Ⅱ、Ⅲ、Ⅱ、Ⅲ、Ⅱ,结果与实际情况吻合。研究表明,利用T-S模糊神经网络研究采空塌陷危险性是可行的。 相似文献
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《岩土工程技术》2018,(6)
鉴于浅层采空区的复杂性、不确定性,目前缺乏针对浅层采空区塌陷危险性的有效评价方法。在对浅层采空区详尽客观调查探测基础上,提出了基于层次分析法的模糊综合评判定量评价方法评价采空塌陷危险性。首先,在研究区工程地质条件、采空区塌陷机理和塌陷影响因素分析基础上建立采空塌陷评价因子体系及标准;其次,针对离散型和连续型变量因子的不同,分别采用专家评定法和适宜的隶属度函数模型构建隶属度评价矩阵,同时采用层次分析法中的特征向量法确定因子权重;最后,通过权重矩阵和隶属度矩阵的合成运算得到评价结果向量,按贴近度原则确定危险性等级。研究结果表明,模糊综合评判方法在评价浅层采空区塌陷危险性方面是可行并有效的。 相似文献
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采空塌陷严重影响了人民群众的生命财产安全,准确评价采空区的稳定性,提高预警的时效性,采空塌陷的实时监测至关重要。本文以北京市门头沟区王平镇南港村采空塌陷区为工程背景,建立了以GPS、静力水准、深部测斜、多通道微震监测为主的动态监测系统,实现了多源信息融合技术的采空塌陷区位移场、应力场的全天候监测。基于CAD-Surfer-ANSYS方法建立了采空区三维模型,实现了采空塌陷区监测体系的三维可视化管理,并在监测数据初步分析的基础上,采用数值模拟技术对采空塌陷区稳定性进行了初步分析,为采空塌陷灾害防治提供了技术支持。 相似文献
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本文运用GIS的缓冲区、叠加、空间分析等功能，从地貌类型、碳酸盐岩类型、水文地质条件、人类活动及土地利用因素方面，对以岩溶塌陷和采空塌陷为代表的中国地面塌陷灾害危险性进行评价。其中模型中各影响因素的权重值主要是通过层次分析法来确定；而影响因素中的分类值则是地面塌陷点的分布概率。评估结果表明，地面塌陷极高危险区主要分布在中国的广西、贵州、云南，其次是湖北、湖南、重庆、四川、陕西。 相似文献
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《地质灾害与环境保护》2020,(2)
为了获得符合贵州西部山区煤矿采空区场地及地形特点的采空区场地稳定性评价方法,采用专家打分法及AHP法(层次分析法)归纳了主控因素,量化分析后提出贵州西部山区煤矿采空区场地稳定性主要受地形坡度、停采时长、采深采厚比等影响,并通过实例证明了其应用的可行性,指出了相关工作中应注意的主要问题,对类似采空区场地稳定性评价工作具有借鉴意义。 相似文献
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岩溶地面塌陷危险性模糊评价方法 总被引：1，自引：0，他引：1
岩溶地面塌陷危险性评价是一个复杂而亟待解决的问题,本文针对岩溶地面塌陷危险性评价的特点,在综合考虑影响岩溶地面稳定性的多种因素的基础上,引入系统层分析方法和多级模糊评价方法,并以正态函数为隶属函数,以实数的加乘运算作为合成运算规则,建立了岩溶地面塌陷危险性评价的二级模糊评价模型,并将评价结果分为:稳定、较稳定、较不稳定和不稳定4级。应用于广州市某建设场地地质灾害评估工程,结果表明该方法具有良好的可靠性。 相似文献
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准确有效地判别突水水源是解决矿井水害的前提条件。基于淮北袁店二矿各含水层共59个水样水质化验资料,利用主成分分析法,计算各水样的因子得分,并进行系统聚类,剔除错误样本。利用剩余水样作为学习样本,检验Bayes判别函数的判定准确性,得出准确率为92.5%,并进行交叉验证。利用该判别函数对某工作面底板下一富水区水样进行判别,结果与实际情况吻合。结果指示基于主成分分析与Bayes判别法较单一Bayes判别法更加准确,能够消除样本变量之间的相互影响,实现对突水水源的快速有效判别。 相似文献
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A. S. Ertürk F. Göde A. E. Bozdoğan 《International Journal of Environmental Science and Technology》2017,14(11):2459-2470
A chemometric approach coupled with capillary electrophoresis based on the hierarchical cluster analysis and principal component analysis has been applied for the investigation of the water quality in the Golcuk-Isparta region (Lake District of Turkey). In the research area, Egirdir Lake, Golcuk Lake and surrounding ground and domestic waters have been utilized as drinking water resources. Golcuk Lake is distinctive in terms of high fluoride content (3.50 ± 0.21 mg/mL) which is endemic in volcanic areas where the water flow through volcanic rocks and sediments. Based on the analysis of major anions chloride, sulfate, nitrate and fluoride with capillary electrophoresis, twenty-four drinking water sampling sites in the research area were classified into four classes using the hierarchical cluster and principal component analysis. Combining the research area investigation results of hierarchical cluster and principal component analysis, it was found that fluoride concentration is the major diagnostic variable to determine the quality of drinking waters, and all the other anions are the important classification factors to predict the resources of the drinking water samples, individually. To sum up, this study reveals the potential of the use of capillary electrophoresis in combination with chemometric techniques for the determination of the quality and origin of drinking waters. 相似文献
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V. R. S. Mani 《Journal of the Geological Society of India》2018,91(5):541-546
With the availability of multi sensor data in many fields, such as remote sensing, medical imaging or machine vision, sensor fusion has emerged as a new and promising research area. It is possible to have several images of the same scene providing different information although the scene is the same. This is because each image has been captured with a different sensor. A non-negative matrix factorization (NNMF) un mixing based fusion technique with vertex component analysis (VCA) based end member initialization and simple multiplicative update to improve the spatial resolution and to preserve the spectral resolution of the hyper spectral image is proposed. Its performance is analyzed with different number of iterations and end member initializations. A Constrained Non Negative Matrix Factorization unmixing based fusion technique is developed by adding a regularization term to the objective function to preserve the spectral resolution of the hyper spectral image, and its performance is analyzed with different number of iterations and end members. A rank two NNMF and hierarchical clustering based end member initialization and block principal pivoting algorithm based abundance estimation technique, for fusing hyper spectral image and simulated multispectral image is proposed and its performance is analyzed for different overlapping and non overlapping group of multispectral and hyper spectral bands. The performance of the above three methods are compared and analyzed. The obtained results show that the performance of rank two NNMF hierarchical clustering based fusion technique is better than the other two constrained and unconstrained NNMF un mixing based techniques. Also, the performance of these three proposed multi sensor image fusion techniques are compared with an existing image fusion technique. 相似文献
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为高效地进行砂土液化的预测,运用逐步判别法,从8个液化影响因子中选择平均粒径、烈度、震中距等3个判别能力显著的影响因子,建立判别函数,并利用工程实例进行验证。研究结果表明：逐步判别分析模型预测性能良好,且能有效地选择对砂土液化起主导作用的因子。相比距离判别分析,逐步判别分析建立的判别函数更加稳定,且所需测试因子较少,节省了因试验和现场调查所耗费的大量人力、物力和时间,因此逐步判别分析是一种值得推广的砂土液化预测方法。 相似文献
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July temperatures for the past 6000 yr at 11 sites in northern Canada have been predicted by transfer-function equations. Normalized departures from the mean of each time series at 250-yr intervals are analyzed by principal component (eigenvector) analysis. An initial analysis included 9 sites and the first three principal components accounted for 85.7% of the variance. Maps of the loadings on the principal components show broad spatial coherence on all three components. Temporal coefficients (principal component scores) illustrate major regional and local midsummer temperature variations. An additional 2 sites were then included but the spatial pattern of the loadings remained essentially unchanged. A further test of this approach, with a view toward predicting paleoclimates of northern regions, was to use the spatial coefficients (loadings) to estimate the July temperature departures at an “unknown” site (Long Lake, Keewatin). This reconstruction compares favorably with an independent transfer-function reconstruction (Kay, 1979). Power spectrum analysis of the significant principal component scores (temperature departures) over the 6000 yr showed that the temporal fluctuations associated with the first three principal components follow a “red noise” spectrum, indicative of strong persistence in the reconstructed climatic records. The scores on the fourth principal component approximate a “white noise” spectrum. A peak in power between 2000 and 3000 yr occurs in the variance spectrum of the second principal component (significance 10%). We conclude that eigenvector analysis of Holocene paleoclimatic data has considerable power and may be useful for identifying regional and local climatic variations. 相似文献
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Y. Zee Ma 《Mathematical Geosciences》2011,43(4):401-419
Both statistical methods and artificial neural network (ANN) have been used for lithology or facies clustering. ANN, in particular,
has increasingly gained popularity for clustering of categorical variables as well as for predictions of continuous variables.
In this article, we discuss several counter examples that show deficiencies of these techniques when used for automatic lithofacies
clustering. Our examples show that the lithofacies clustered by ANN alone or ANN in combination with principal component analysis
(PCA), as commonly used, are highly inconsistent with the benchmark charts based on laboratory results. We propose several
techniques to overcome these problems and improve the clustering of lithofacies, including (1) classification of lithofacies
using the minor or intermediate principal component(s), (2) rotation of a principal component before using ANN for clustering,
(3) cascading two or more PCAs and ANNs for clustering lithofacies or electrofacies, and (4) classifying lithofacies with
demarcated stratigraphic reference classes. 相似文献
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影响砂土液化的因素有很多,建立多指标的液化预测模型非常有必要。目前所有的多指标砂土液化预测模型,均默认选取的判别因子之间相互独立,不存在相关性,可能导致各判别因子之间存在信息叠加而发生误判。以唐山地震砂土液化的25个案例为样本,选取8个影响因素作为砂土液化预测的初始判别指标,首先采用主成分分析（PCA）对各判别指标进行分析,对存在相关性比较高的指标进行了降维处理。基于降维后的4个主成分换算得到新的样本数据,以18个案例为学习样本,建立主成分分析与距离判别分析（DDA）相结合的砂土液化预测模型。利用建立的预测模型对18个案例进行回判,结果全部正确。对其他7个案例的液化情况进行了预测,并与规范法、Seed方法、BP法、DDA法的判别结果进行分析比较,结果表明基于主成分分析与距离判别方法的砂土液化判别模型预测准确率为100%。将模型应用于工程实例,判别结果也与实际情况一致,表明该模型具有良好的预测功能,可在实际工程中应用。 相似文献
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基于主成分分析的岩石质量综合评价模型与应用 总被引：2，自引：0，他引：2
主成分分析法能够在保证原始数据信息损失最小的情况下,以少数的综合变量取代原有的多维变量,使数据结构大为简化。试验选取了能间接反映岩石质量的5项主要指标,分别为岩石的干密度、变形模量、饱和吸水率、干抗压强度和纵波传播波速。采用主成分分析法对这些间接指标进行相关的数值处理,得出了评价岩石质量的数学模型。在对16组岩石样品的5项指标进行室内测试后,运用该模型对岩石样品的岩石质量进行了评价,并对该16组岩石样品的岩石质量进行了排序,将评价结果与实际样品进行对比,结果表明,该模型的评价结果与岩石的实际质量有很好的符合性。主成分分析法用于评价岩石质量有较高的可信度,并能够客观、准确、迅速地评价某种岩石的岩石质量。 相似文献
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岩体质量等级分类在实际的工程中有着很重要的作用。基于主成分分析（PCA）法和与Fisher判别分析法相结合建立岩体质量等级判别模型,选取单轴抗压强度、岩体体积节理数、声波纵波速度、节理面风化变异系数、节理面粗糙度系数和透水性系数6种指标作为岩体质量分级判别的判别因子。以永平铜矿露天矿区工程岩体特征资料中的20个样本为训练样本,10个为待判样本,对该模型进行检验和应用,并与传统的RMR法、Fisher判别分析模型的结果进行比较,相应正确率分别为87%、70%、77%,判断结果表明利用主成分分析法和Fisher判别分析法建立的模型判别能力更高。 相似文献
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基于距离判别分析方法的深基坑支护方案优选研究 总被引：2，自引：1，他引：1
基于马氏距离判别分析理论,分析了影响深基坑支护方案的因素,从安全性、经济性、可行性3个方面选取了10个实测指标作为影响深基坑支护方案选型的判别指标,利用国内大量的深基坑支护实例作为学习样本进行训练,建立距离判别分析模型,对深基坑支护方案进行优选,并利用回代估计法对距离判别分析模型进行检验。研究结果表明,经过训练后的支护方案优选模型误判率很低,判别优选能力很高。预测样本与工程实例的检验表明,距离判别分析模型优选性能良好,验证了该模型的高效性和实用性。说明距离判别分析理论是解决深基坑支护方案优选问题的有效方法之一,可以在实际工程中进行推广。 相似文献