共查询到20条相似文献，搜索用时 31 毫秒
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Neural networks offer a non-algorithmic approach to geostatistical simulation with the possibility of automatic recognition of correlation structure. The paper gives a brief overview of neural networks and describes a feedforward, back-propagation network for geostatistical simulation. The operation of the network is illustrated with two simple one-dimensional examples which can be followed through with hand calculations to give an insight into the operation of the network. The convergence of the network is described in terms of the variogram calculated from the values at each of the output nodes at each iteration. 相似文献
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Well-log correlation using a back-propagation neural network 总被引：1，自引：0，他引：1
We present a back-propagation neural network with an input layer in the form of a tapped delay line wich can be trained effectively
on one or several well logs to recognize a particular geological marker. Subsequently, the neural network proposes locations
of this marker on other wells in the field. Another neural network, similar in architecture to the first one, performs the
same task for secondary markers using, in addition to the well logs, a depth reference function to the first marker. This
method is shown to have better performance and better discrimination than standard cross-correlation techniques. It lends
itself well for an interactive implementation on a workstation. 相似文献
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滑坡变形监测与预测是滑坡预警预报中一种非常重要的途径。文章首先简单介绍了神经网络的基本原理和学习算法,然后利用某水库滑坡24期的GPS地表位移监测数据及其诱发因素即水库水位、降雨等资料,采用BP神经网络模型对该水库滑坡变形进行建模,最后将6期水库水位、降雨等资料输入模型进行滑坡变形预测,结果表明预测结果与实测数据符合性好,总体上能较好反映变形趋势。 相似文献
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神经网络方法识别测井曲线形态 总被引：1，自引：0，他引：1
随着油田勘探开发程度的不断提高,要找到有利的油气聚集带以及在开发阶段提高油田采收率,都必须进行储层沉积相分析。这里介绍一种利用自组织神经网络识别曲线形态的方法。采用将测井曲线网格化,再利用自组织神经网络识别曲线形态,进而去判别沉积相。此方法可以对测并曲线形态进行识别,且消除了测井曲线中的不确定因素,运用该方法对实际测井曲线形态的识别基本正确。 相似文献
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Inversion of residual gravity anomalies using neural network 总被引：1，自引：1，他引：0
Mansour A. Al-Garni 《Arabian Journal of Geosciences》2013,6(5):1509-1516
A new approach is presented in order to interpret residual gravity anomalies from simple geometrically shaped bodies such as horizontal cylinder, vertical cylinder, and sphere. This approach is mainly based on using modular neural network (MNN) inversion for estimating the shape factor, the depth, and the amplitude coefficient. The sigmoid function has been used as an activation function in the MNN inversion. The new approach has been tested first on synthetic data from different models using only one well-trained network. The results of this approach show that the parameter values estimated by the modular inversion are almost identical to the true parameters. Furthermore, the noise analysis has been examined where the results of the inversion produce satisfactory results up to 10% of white Gaussian noise. The reliability of this approach is demonstrated through two published real gravity field anomalies taken over a chromite deposit in Camaguey province, Cuba and over sulfide ore body, Nornada, Quebec, Canada. A comparable and acceptable agreement is obtained between the results derived by the MNN inversion method and those deduced by other interpretation methods. Furthermore, the depth obtained by the proposed technique is found to be very close to that obtained by drilling information. 相似文献
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Sajad Haghir Chehreghani Aref Alipour Mehdi Eskandarzade 《Journal of the Geological Society of India》2011,78(3):271-277
One important decision in design of surface mine is the selection of mine equipment and plant. Demand for mechanical excavation
is growing in mining industry because of its high productivity and excavation in large scale with lower costs. Several models
have been developed over the years to evaluate the ease of excavation and machine performance against rock mass properties.
Due to complexity of excavation process and large number of effective parameters, approaches made for this purpose are essentially
empirical. There are many uncertainties in results of these models. An attempt is made in this paper to revise the exisiting
models. Neural network models for estimation of rock mass excavatability and production rate of VASM-2D excavating machine
at Limestone quarry in Retznei, Austria, is presented. Input parameters of this model are Uniaxial compressive strength, tensile
strength and discontinuities spacing of rocks. Output is the specific excavation rate per power consumption (bcm/Kwh) as the
productivity indicator. Average of deviation between actual data and results estimated by neural network model was only 15%
which is in an acceptable range. 相似文献
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基于神经网络的混沌时间序列预测 总被引：8，自引：0，他引：8
应用混沌方法对时间序列观测数据进行处理,计算出最大lyapunov指数,得到最大可预报时间尺度。在此基础上,建立人工神经网络预测预报混沌时间序列的模型。结合实例,对该预测方法进行了计算验证。 相似文献
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BP神经网络方法在地下水动态监测网质量评价中的应用 总被引：3，自引：1，他引：3
本文运用BP神经网络方法构建了地下水动态监测网的质量评价模型，并以甘肃省武威盆地的地下水位监测网为例进行了实例研究。研究表明，在武威和清源附近地下水监测点密度大于0．09／km^2的三个区域，需要进一步调整地下水监测点结构。武威以东、双城以南的地下水位漏斗区和武威以西的山前地带，需要增加地下水监测点。其它地下水监测点密度小于0．03／km^2的地区，则需要根据实际情况而决定。 相似文献
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用遗传神经网络分析泥石流活动性 总被引：7，自引：0，他引：7
泥石流是我国山区的主要地质灾害之一。影响泥石流活动性的因素十分复杂，并且具有随机性和模糊性。遗传神经网络结合了神经网络和遗传算法的优点，可以模拟学习和进化之间的交互作用，很适合用于分析泥石流活动性。文章简要讨论了遗传神经网络的原理，建立了泥石流活动性分析的遗传神经网络模型，并将该模型用于川藏公路沿线30条泥石流沟的活动性分析。网络的拓扑结构为(9，6，4，3)，即输入节点(评价指标)、第l隐含层、第2隐含层和输出接点(分析结果)分别为9、6、4、3。首先以其中25条泥石流沟作为样本对网络进行训练，训练时网络的连接权采用遗传算法进行自适应演化，待模型稳定后将其余5条泥石流沟的数据输入模型，计算它们的活动性，计算结果与实际观测基本相符，证明模型是可行的，各个参数的选取也是合适的。 相似文献
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K. K. Kuok S. Harun Ph.D. S. M. Shamsuddin Ph.D. 《International Journal of Environmental Science and Technology》2010,7(1):67-78
The rainfall-runoff relationship is one of the most complex hydrological phenomena. In recent years, hydrologists have successfully applied backpropagation neural network as a tool to model various nonlinear hydrological processes because of its ability to generalize patterns in imprecise or noisy and ambiguous input and output data sets. However, the backpropagation neural network convergence rate is relatively slow and solutions can be trapped at local minima. Hence, in this study, a new evolutionary algorithm, namely, particle swarm optimization is proposed to train the feedforward neural network. This particle swarm optimization feedforward neural network is applied to model the daily rainfall-runoff relationship in Sungai Bedup Basin, Sarawak, Malaysia. The model performance is measured using the coefficient of correlation and the Nash-Sutcliffe coefficient. The input data to the model are current rainfall, antecedent rainfall and antecedent runoff, while the output is current runoff. Particle swarm optimization feedforward neural network simulated the current runoff accurately with R = 0.872 and E2 = 0.775 for the training data set and R = 0.900 and E2= 0.807 for testing data set. Thus, it can be concluded that the particle swarm optimization feedforward neural network method can be successfully used to model the rainfall-runoff relationship in Bedup Basin and it could be to be applied to other basins. 相似文献
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Use of artificial neural network for spatial rainfall analysis 总被引：1，自引：0，他引：1
TSANGARATOS PARASKEVAS ROZOS DIMITRIOS BENARDOS ANDREAS 《Journal of Earth System Science》2014,123(3):457-465
In the present study, the precipitation data measured at 23 rain gauge stations over the Achaia County, Greece, were used to estimate the spatial distribution of the mean annual precipitation values over a specific catchment area. The objective of this work was achieved by programming an Artificial Neural Network (ANN) that uses the feed-forward back-propagation algorithm as an alternative interpolating technique. A Geographic Information System (GIS) was utilized to process the data derived by the ANN and to create a continuous surface that represented the spatial mean annual precipitation distribution. The ANN introduced an optimization procedure that was implemented during training, adjusting the hidden number of neurons and the convergence of the ANN in order to select the best network architecture. The performance of the ANN was evaluated using three standard statistical evaluation criteria applied to the study area and showed good performance. The outcomes were also compared with the results obtained from a previous study in the area of research which used a linear regression analysis for the estimation of the mean annual precipitation values giving more accurate results. The information and knowledge gained from the present study could improve the accuracy of analysis concerning hydrology and hydrogeological models, ground water studies, flood related applications and climate analysis studies. 相似文献
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