共查询到20条相似文献，搜索用时 31 毫秒
1.
Hamid Mahmoudabadi Mohammad Izadi Mohammad Bagher Menhaj 《Computational Geosciences》2009,13(1):91-101
In the present paper, a new hybrid method is proposed for grade estimation. In this method, the multilayer perceptron (MLP)
network is trained using the combination of the Levenberg–Marquardt (LM) method and genetic algorithm (GA). Having a few samples
for grade estimation, it is difficult to get a proper result using some function approximation methods like neural networks
or geostatistical methods. The neural network training methods are very sensitive to initial weight values when there are
a few samples as a training dataset. The main objective of the proposed method is to resolve this problem. Here, our method
finds the optimal initial weights by combining GA and LM method. Having the optimal initial values for weights, the local
minima are avoided in the training phase and subsequently the neural network sustainability is trained optimally. Furthermore,
the hybrid method is applied for grade estimation of Gol-e-Gohar iron ore in south Iran. The proposed method shows significant
improvements compared to both conventional MLP and Kriging method. The efficiency of the proposed method gets more highlighted
when the training data set is small. 相似文献
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Ashok Kumar Patel Snehamoy Chatterjee Amit Kumar Gorai 《Arabian Journal of Geosciences》2017,10(5):107
The product of the mining industry (ore) is considered to be the raw material for the metal industry. The destination policy of the raw materials of iron mine is highly dependent on the class of iron ores. Thus, regular monitoring of iron ore class is the urgent need at the mine for accurately assigning the destination policy of raw materials. In most of the iron ore mines, decisions on ore class are made based on either visual inspection by the geologist or laboratory analyses of the ores. This process of ore class estimation is time consuming and also challenging for continuous monitoring. Thus, the present study attempts to develop an online vision-based technology for classification of iron ores. A laboratory-scale transportation system is designed using conveyor belt for online image acquisition. A multiclass support vector machine (SVM) model was developed to classify the iron ores. A total of 2200 images were captured for developing the ore classification model. A set of 18 features (9-histogram-based colour features in red, green and blue (RGB) colour space and 9-texture features based on intensity (I) component of hue, saturation and intensity (HSI) colour space) were extracted from each image. The performance of the SVM model was evaluated using four confusion matrix parameters (sensitivity, accuracy, misclassification and specificity). The SVM model performance was also compared with the other methods like K-nearest neighbour, classification discriminant, Naïve Bayes, classification tree and probabilistic neural network. It was observed that the SVM classification model performs better than the other classification methods. 相似文献
4.
矿床数学—经济模型及其在元江金矿的应用 总被引：11，自引：3，他引：11
利用地质统计学的原理和方法，编制出矿床数学－经济模型软件包，它作为一种储量计算和矿床经济评价方法。介绍了该软件包各模块的功能，并以元江金矿应用为例，展示了该软件包的功能及效果。 相似文献
5.
We show the presence of fractal ordering of copper grade in bore core data at short range in the Cadia Ridgeway porphyry deposit and measure its persistence after mining by monitoring the output of the... 相似文献
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This study deals with reservoir characterization based on well log data using an unsupervised self-organizing map (SOM) and supervised neural network algorithms with the aim of clustering log responses into reservoir facies of an oil field located in southwest of Iran. In order to promote and justify the quality control and quantify spatial relationships for petrophysical properties, some of neural network-based approaches were introduced such as the SOMs as the intelligent clustering method compared with other hybrid methods, principal component analysis networks (PCANs) and multilayer perceptron (MLP) and statistical clustering (CA) methods. The results obtained from all the abovementioned methods are compared to each other, and the best option is selected based on accuracy and capabilities of clustering and estimation of the petrophysical data, concluding that for predicting any characteristic of the reservoirs, the appropriate network should be chosen and a unique network cannot be convenient for all of them. Accordingly, the SOM clustering technique was employed to classify the reservoir rocks. Based on the SOM visualization, the reservoir rocks were classified into six facies associated with specific petrophysical properties; among them, F6 expressed the best reservoir quality which is characterized by the low amount of density, highest DT, high amount of neutron porosity (NPHI), and lowest GR response. Ultimately, the performance of all the methods was compared to estimate the porosity and permeability within each facies. The results revealed the preference and reliability of PCAN in predicting porosity and confirmed the capability of MLP in permeability prediction. This study also indicates that neuro-prediction of formation properties using well log data is a feasible methodology for optimization of exploration programs and reduction of expenditure by delineating potentially oil-bearing strata with higher accuracy and lower expenses. The resulting neural net-based model can be used as a powerful and distributive system to reduce the high impact of risk in similar fields. 相似文献
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The mineral resource estimation requires accurate prediction of the grade at location from limited borehole information. It plays the dominant role in the decision-making process for investment and development of various mining projects and hence become an important and crucial stage. This paper evaluvates the use of two distinct artificial neural network (ANN)-based models, general regression neural network (GRNN) and multilayer perceptron neural network (MLP NN), to improve the grade estimation from Koira iron ore region in Sundargarh district, Odisha. ANN-based models capture the inherent complex structure of mineral deposits and provide a reliable generalization of the iron grade. The ANN-based approach does not require any preliminary geological study and is free from any statistical assumption on the raw data before its application. The GRNN is a one-pass learning algorithm and does not require any iterative procedure for training less complex structure and requires only one learning parameter for optimization. In this investigation, the spatial coordinates and multiple lithological units were taken as input variables and the iron grade was taken as the output variable. The comparative analysis of these models has been carried out and the results obtained were validated with traditional geostatistical method ordinary kriging (OK). The GRNN model outperforms the other methods, i.e. MLP and OK, with respect to generalization and predictability of the grades at an un-sampled location. 相似文献
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Grade estimation using fuzzy- set algorithms 总被引：1，自引：0，他引：1
T. D. Pham 《Mathematical Geology》1997,29(2):291-305
This paper presents a new approach for estimating unknown ore grades within a mining deposit in a fuzzy environment using
fuzzy c- means clustering and a fuzzy inference system. Based on a collection of cluster centers obtained from fuzzy c- means,
a fuzzy rule base and fuzzy search domains are established to compute grades at these cluster centers. These cluter center-
grade pairs act as control information in the fuzzy space- grade system in order to infer unknown grades on the basis of fuzzy
interpolation, fuzzy extrapolation, and a defuzzification process of fuzzy control. 相似文献
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为提高多种典型人工神经网络应用于降水预报的精度与稳定性并做出优选,对太湖流域湖西区丹徒、丹阳、金坛、溧阳、宜兴5站的年降水量时间序列建立基于组成成分分析的人工神经网络模型,并通过平均相对误差、平均绝对误差、均方根误差及合格率4项评价指标对比分析预报效果。该模型采用Mann-Kendall法、秩和检验法、谱分析法进行组成成分分析;建立BP网络、小波神经网络、RBF网络、GRNN网络及Elman网络模拟并预测随机成分,与确定性成分叠加得年降水量预报结果。在湖西区的研究结果表明,基于组成成分分析的人工神经网络模型的拟合及预测精度高于原始人工神经网络和线性自回归模型,GRNN网络的预测精度与稳定性高于其他4类神经网络。 相似文献
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矿井涌水量的准确预测对预防矿山透水事故的发生至关重要,提出利用GA优化的SVM模型（GA-SVM）来实现矿井涌水量的短期准确预测。该方法利用GA的自动寻优功能寻找SVM的最佳参数,提高了预测的准确率。首先,利用微熵率法求矿井涌水量时间序列的最佳嵌入维数和延迟时间,进行相空间重构。其次,采集义煤集团千秋煤矿2011—2015年实际涌水量的时间序列,利用GA-SVM模型对最后12组数据进行预测,其预测平均绝对百分比误差仅为0.92%,最大相对误差为2.62%。最后,与PSO-SVM和BP神经网络预测进行对比,结果表明GA-SVM优化模型适用于矿井涌水量的预测并且预测精度较高。 相似文献
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巷道掘进前方采用钻探或地球物理方法进行超前探测含水层的位置及富水性,提前做好防治水工作对煤矿安全生产至关重要。利用钻孔瞬变电磁法(BTEM)进行超前探测优势明显,目前解释方法是根据计算的电阻率进行岩层富水性的定性分析,还无法实现对含水层富水性等级进行预测。提出利用钻孔瞬变电磁法探测,采用全卷积神经网络(FCNN)方法进行钻孔外围含水层富水性等级的准确预测。首先,依据阿尔奇公式、Kozeny–Carman公式、导水系数公式和单位涌水量公式,建立砂岩含水层单位厚度的电阻率与按钻孔单位涌水量划分的含水层富水性4个等级的对应关系;其次,建立全空间条件下不同富水性岩层的地质–地球物理模型,采用三维时域有限差分法(FDTD)进行全空间瞬变电磁场数值模拟;为了接近实际情况,在正演结果中加入了5%～15%的随机噪声,提取与岩层富水性等级关联的特征参数,采用全卷积神经网络(FCNN)进行了岩层富水性等级预测的训练和仿真测试,测试集预测的富水性等级平均准确率为91.8%;最后,利用某矿煤层水力压裂后的钻孔瞬变电磁法实测数据进行煤岩层富水性等级预测,检验FCNN方法预测效果。研究结果表明:采用全卷积神经网络... 相似文献
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针对BP人工神经网络具有易陷入局部极小等缺陷,提出了将遗传算法与神经网络结合,同时优化网络结构的权值与阈值的思想,建立了基于遗传算法的混凝土坝抗震可靠度预测的神经网络模型。该模型分别对混凝土坝抗滑稳定可靠度、抗压可靠度和抗拉可靠度进行了预测,并与BP神经网络预测结果进行比较。结果表明,遗传神经网络模型可靠,预测精度高,在岩土工程中利用该方法进行可靠性问题预测是有效及可行的。 相似文献
13.
C.
zgen Karacan 《International Journal of Coal Geology》2007,72(3-4):221-239
In longwall development mining of coal seams, planning, optimizing and providing adequate ventilation are very important steps to eliminate the accumulation of explosive methane–air mixtures in the working environment. Mine operators usually try to supply maximum ventilation air based on the capacity of the system and the predicted need underground. This approach is neither economical nor safer as ventilation capacity may decrease in time depending on various mining and coalbed parameters. Thus, it is important to develop better engineered approaches to optimize mine ventilation effectiveness and, therefore, to ensure a safer work environment.This study presents an approach using coalbed methane reservoir modeling and an artificial neural network (ANN) design for prediction and optimization of methane inflows and ventilation air requirements to maintain methane concentrations below statutory limits. A coalbed reservoir model of a three-entry development section, which is typical of Pittsburgh Coalbed mines in the Southwestern Pennsylvania section of Northern Appalachian Basin, was developed taking into account the presence and absence of shielding boreholes around the entries against methane inflow. In the model, grids were dynamically controlled to simulate the advance of mining for parametric simulations.Development and application of artificial neural networks as an optimization tool for ventilation requirements are introduced. Model predictions are used to develop, train, and test artificial neural networks to optimize ventilation requirements. The sensitivity and applications of proposed networks for predicting simulator data are presented and discussed. Results show that reservoir simulations and integrated ANN models can be practical and powerful tools for predicting methane emissions and optimization of ventilation air requirements. 相似文献
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Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach 总被引：5，自引：1，他引：4
An ideally performed blasting operation enormously influences the mining overall cost. This aim can be achieved by proper prediction and attenuation of flyrock and backbreak. Poor performance of the empirical models has urged the application of new approaches. In this paper, an attempt has been made to develop a new neuro-genetic model for predicting flyrock and backbreak in Sungun copper mine, Iran. Recognition of the optimum model with this method as compared with the classic neural networks is faster and convenient. Genetic algorithm was utilized to optimize neural network parameters. Parameters such as number of neurons in hidden layer, learning rate, and momentum were considered in the model construction. The performance of the model was examined by statistical method in which absolutely higher efficiency of neuro-genetic modeling was proved. Sensitivity analysis showed that the most influential parameters on flyrock are stemming and powder factor, whereas for backbreak, stemming and charge per delay are the most effective parameters. 相似文献
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基于BP神经网络的降雨充水矿井涌水量预测 总被引：1，自引：0，他引：1
长沟峪煤矿矿井涌水量受降雨影响显著,曾经因降雨造成淹井事故。文章分析了长沟峪煤矿矿井充水因素及其影响程度,建立了矿井涌水量预测的BP网络模型,通过对2006年和2007年＋141水平和＋20水平矿井最大涌水量预测验证了该模型的可行性,并据此对不同降雨条件下的矿井涌水量进行了预测。 相似文献
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Geostatistical estimation techniques were customized to allow forecasting of production figures at the Silver Bell uranium mine (Uravan District).Surface drill hole data were used to provide a block model of kriged estimators of average uranium grades. Figures for recoverable ore grade and the ore-waste ratio are then deduced from regressive curves previously obtained from underground information and production data. Cross-validations of the entire model were performed and were found positive. 相似文献
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基于DIMINE软件的易门铜厂矿床Cu品位分布规律研究 总被引：2，自引：0，他引：2
在三维可视化环境下可快速实现矿体品位的高精度计算,直观体现矿体规模及矿体(矿化)的空间分布规律。本文基于DIMINE软件,利用易门铜厂矿区的坑道及钻孔资料建立地质数据库,运用地质统计学理论和块段构模方法创建铜厂矿体沿走向、倾向、厚度三个方向上的矿化数学模型。并用块段模型对Cu元素进行了品位推估,得出Cu品位空间分布模型,从三维角度更直观、准确地揭示矿体中铜品位在空间上的分布规律,为认识矿化分布规律提供了新依据。 相似文献
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An intelligent neural network model for evaluating performance of immobilized cell biofilter treating hydrogen sulphide vapors 总被引：1，自引：1，他引：0
E. R. Rene Ph.D. J. H. Kim Ph.D. H. S. Park Ph.D. 《International Journal of Environmental Science and Technology》2008,5(3):287-296
Biofiltration has shown to be a promising technique for handling malodours arising from process industries. The present investigation pertains to the removal of hydrogen sulphide in a lab scale biofilter packed with biomedia, encapsulated by sodium alginate and poly vinyl alcohol. The experimental data obtained under both steady state and shock loaded conditions were modelled using the basic principles of artificial neural networks. Artificial neural networks are powerful data driven modelling tools which has the potential to approximate and interpret complex input/output relationships based on the given sets of data matrix. A predictive computerised approach has been proposed to predict the performance parameters namely, removal efficiency and elimination capacity using inlet concentration, loading rate, flow rate and pressure drop as the input parameters to the artificial neural network model. Earlier, experiments from continuous operation in the biofilter showed removal efficiencies from 50 to 100 % at inlet loading rates varying up to 13 g H2S/m3h. The internal network parameter of the artificial neural network model during simulation was selected using the 2k factorial design and the best network topology for the model was thus estimated. The results showed that a multilayer network (4-4-2) with a back propagation algorithm was able to predict biofilter performance effectively with R2 values of 0.9157 and 0.9965 for removal efficiency and elimination capacity in the test data. The proposed artificial neural network model for biofilter operation could be used as a potential alternative for knowledge based models through proper training and testing of the state variables. 相似文献