共查询到20条相似文献，搜索用时 140 毫秒
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介绍了神经网络的一些基本概念，ＢＰ神经网络及其算法，使用地震强度因子Ｍｆ值，地震空间集中度Ｃ值，地震危险度Ｄ值对华北地区１９７２￣１９９２年期间进行空间扫描的中期和短期异常资料，通过ＢＰ神经网络进行学习并进行地震短期预测。研究结果表明：利用这３类资料的多项因子进行短期预测的效果较为理想。文章还对使用ＢＰ神经网络的一些具体问题进行了讨论。 相似文献
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为解决建筑物震害信息提取自动化程度不高的问题，本文将全卷积神经网络应用于建筑物震害遥感信息提取。以玉树地震后获取的玉树县城区0.2m分辨率航空影像作为建筑物震害信息提取试验数据源，将试验区地物划分为倒塌建筑物、未倒塌建筑物和背景3类。对427个500×500像素的子影像进行人工分类与标注，选取393个组成训练样本集，34个用于验证。利用训练样本集对全卷积神经网络进行训练，采用训练后的网络对验证样本进行建筑物震害信息提取及精度评价。研究结果表明:建筑物震害遥感信息提取总体分类精度为82.3%，全卷积神经网络方法能提高信息提取自动化程度，具有较好的建筑物震害信息提取能力。 相似文献
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本文对影响结构计算仿真神经网络精度的训练样本选择方法进行了分析。以卫星天线模型(ANTENNA)和飞机模型(GARTEUR)为对象,以样本数量和网络模型的均方误差为指标,对多种样本选择方法进行了比较。结果表明,均匀设计方法可以用较少的训练样本,保证神经网络模型较高的精度,是结构计算仿真中神经网络训练样本的最优选取方法。 相似文献
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BP神经网络在新一代地震预报专家系统中的应用 总被引：3，自引：0，他引：3
简介了新一代地震预报专家系统ＮＧＥＳＥＰ，ＢＰ神经网络模型及其算法，同盱ＢＰ神经网络具有很强的非线怀映射功能，它可以很好地反映震前出现异常的种类和异常时间与未来地震震级之间的较强非线性关系，在ＮＧＥＳＥＰ系统中可以从实例库中提取典型震例并通过ＢＰ网络进行学习，实际震例检验表明系统对未来地震震级的预测取得较好理想的结果。 相似文献
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对地震层析成像非线性问题线性化处理之后,各种反演算法归纳成为对不适定方 程的求解．地震层析成像反演算法的解的物理意义是给出地质结构,因此对于解的可靠性及 分辨率研究非常重要．然而许多反演算法不能给出解的评价方法,因而对解的可信度产生怀 疑．本研究根据解估计的分辨率矩阵的原理,提出ＬＳＱＲ（Ｌｅａｓｔ Ｓｑｕａｒｅ ＱＲ）算法解协方差矩 阵的评价算法,用相关分析可以为那些在求解过程中得不到分辨率矩阵的反演方法提供解的 定量评价．并用本文提出的解的定量评价方法试评了一个实际地壳模型的地震层析成像的 速度重建结果． 相似文献
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给出了一种用地震折射波初至走时确定二维速度的迭代层析成象反演法。该反演法适用于源－接收器间距比常规剖面法密的折射剖面，它是以线性化问题的迭代解为依据，不仅能用于近水平界面的几何形态，而且也能用于连续速度变化的情况。每次迭代中用射击法进行两点间的射线追踪，来建立线性系统。速度场用速度梯度为常数的三角形单元定义，这样射线路径可解析计算。根据正问题的不同线性化公式对两种不同的反演方法作了研究。用线性化走时－速度雅可比行列式反演得出的结果比用更近似于一般级数展开法的慢度公式的结果好。分辨率的例子不仅揭示了源－接收器组合方式及速度结构对分辨率的影响，而且也揭示了射线的几何形态造成的水平向模糊不清和分辨率随深度降低。反演例子表明，使用较好的初始模型时，整体范数产生的解比用最小扰动法计算的解更接近真实模型。对不均匀射线覆盖产生的条纹作用及其排除作了实例说明。 相似文献
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3D inversion of DC data using artificial neural networks 总被引：2，自引：0，他引：2
Ahmad Neyamadpour W. A. T. Wan Abdullah Samsudin Taib Danesh Niamadpour 《Studia Geophysica et Geodaetica》2010,54(3):465-485
In this paper, we investigate the applicability of artificial neural networks in inverting three-dimensional DC resistivity
imaging data. The model used to produce synthetic data for training the artificial neural network (ANN) system was a homogeneous
medium of resistivity 100 Ωm with an embedded anomalous body of resistivity 1000 Ωm. The different sizes for anomalous body
were selected and their location was changed to different positions within the homogeneous model mesh elements. The 3D data
set was generated using a finite element forward modeling code through standard 3D modeling software. We investigated different
learning paradigms in the training process of the neural network. Resilient propagation was more efficient than any other
paradigm. We studied the effect of the data type used on neural network inversion and found that the use of location and the
apparent resistivity of data points as the input and corresponding true resistivity as the output of networks produces satisfactory
results. We also investigated the effect of the training data pool volume on the inversion properties. We created several
synthetic data sets to study the interpolation and extrapolation properties of the ANN. The range of 100–1000 Ωm was divided
into six resistivity values as the background resistivity and different resistivity values were also used for the anomalous
body. Results from numerous neural network tests indicate that the neural network possesses sufficient interpolation and extrapolation
abilities with the selected volume of training data. The trained network was also applied on a real field dataset, collected
by a pole-pole array using a square grid (8 ×8) with a 2-m electrode spacing. The inversion results demonstrate that the trained
network was able to invert three-dimensional electrical resistivity imaging data. The interpreted results of neural network
also agree with the known information about the investigation area. 相似文献
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Artificial neural networks for parameter estimation in geophysics 总被引：12，自引：0，他引：12
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Nonlinear inversion of electrical resistivity imaging using pruning Bayesian neural networks 总被引：1，自引：0，他引：1
Conventional artificial neural networks used to solve electrical resistivity imaging (ERI) inversion problem suffer from overfitting and local minima. To solve these problems, we propose to use a pruning Bayesian neural network (PBNN) nonlinear inversion method and a sample design method based on the K-medoids clustering algorithm. In the sample design method, the training samples of the neural network are designed according to the prior information provided by the K-medoids clustering results; thus, the training process of the neural network is well guided. The proposed PBNN, based on Bayesian regularization, is used to select the hidden layer structure by assessing the effect of each hidden neuron to the inversion results. Then, the hyperparameter α k , which is based on the generalized mean, is chosen to guide the pruning process according to the prior distribution of the training samples under the small-sample condition. The proposed algorithm is more efficient than other common adaptive regularization methods in geophysics. The inversion of synthetic data and field data suggests that the proposed method suppresses the noise in the neural network training stage and enhances the generalization. The inversion results with the proposed method are better than those of the BPNN, RBFNN, and RRBFNN inversion methods as well as the conventional least squares inversion. 相似文献
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The work develops the approximation approach to solving the inverse MTS problem with the use of neural networks. The inverse
problem is considered in model classes of parametrized geoelectric structures, whose electric conductivity is controlled by
a few hundreds of macroparameters (N ∼ 300). An approximate inverse operator of the problem is constructed for each model class as a neural network, whose coefficients
are determined in the process of training on a representative sample of standard examples of forward problem solutions. The
problem of determination of the model class of geolectric structures corresponding to the presented input MT data is solved
with the use of the neural network classifier constructed for the available set of model classes of structures. Regularizing
factors and errors of the neural network method are analyzed. The operation of the algorithm is illustrated by examples of
the 2-D inversion of synthetic MT data. 相似文献
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A systematic comparison of two basic types of neural network, static and dynamic, is presented in this study. Two back-propagation (BP) learning optimization algorithms, the standard BP and conjugate gradient (CG) method, are used for the static network, and the real-time recurrent learning (RTRL) algorithm is used for the dynamic-feedback network. Twenty-three storm-events, about 1632 rainfall and runoff data sets, of the Lan-Yang River in Taiwan are used to demonstrate the efficiency and practicability of the neural networks for one hour ahead streamflow forecasting. In a comparison of searching algorithms for a static network, the results show that the CG method is superior to the standard BP method in terms of the efficiency and effectiveness of the constructed network's performance. For a comparison of the static neural network using the CG algorithm with the dynamic neural network using RTRL, the results show that (1) the static-feedforward neural network could produce satisfactory results only when there is a sufficient and adequate training data set, (2) the dynamic neural network generally could produce better and more stable flow forecasting than the static network, and (3) the RTRL algorithm helps to continually update the dynamic network for learning—this feature is especially important for the extraordinary time-varying characteristics of rainfall–runoff processes. 相似文献
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针对随机地震反演中存在的两个主要问题,随机实现含有噪声和难以从大量随机实现中挖掘有效信息,提出了一种基于神经网络的随机地震反演方法.通过对多组随机实现及其正演地震数据的计算,构建了基于序贯高斯模拟的训练集.这也为应用神经网络求解地球物理反问题,提供了一种有效建立训练集的方法.较之传统的神经网络反演,这种训练集不仅保证了学习样本具有多样性,同时还引入了空间相关性.数值模拟结果表明,该方法只需要通过单层前馈神经网络,就可以比较有效的解决一个500个阻抗参数的反演问题. 相似文献
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Recognizing spatial distribution patterns of grassland insects: neural network approaches 总被引：2，自引：1，他引：1
WenJun Zhang XiaoQing Zhong GuangHua Liu 《Stochastic Environmental Research and Risk Assessment (SERRA)》2008,22(2):207-216
The main objective of this study was to fit and recognize spatial distribution patterns of grassland insects using various
neural networks, and to analyze the feasibility of neural networks for detecting spatial distribution patterns of grassland
insects. BP neural network, Learning vector quantization (LVQ) neural network, linear neural network and Fisher’s linear discriminant
analysis were used to fit and recognize spatial distribution patterns at different ecological scales. Various comparisons
and analysis were conducted. The results showed that BP, LVQ and linear neural networks were better algorithms for recognizing
spatial distribution patterns of grassland insects. BP neural network was the best algorithm to fit spatial distribution patterns.
BP network may be used to recognize the spatial details of distribution patterns, and the recognition performance of BP network
became better as the increase of the number of hidden layers and neurons. Performance of linear neural network for pattern
recognition was similar to linear discrimination method. Linear neural network would yield better performance in finding the
general trends of distribution patterns. Recognition performance of LVQ network was just between BP network and linear network.
It was found that recognition performance of neural networks depended upon not only the ecological scale but also the criterion
for classification. Under the uniform criterion, recognition efficiency of linear methods tended to be weak as ecological
scale became to be coarser. A joint use of neural networks was suggested in order to achieve both overall and detailed understanding
on spatial distribution patterns. 相似文献
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为满足地球物理资料反演解释的高精度、快速、稳定的要求,本文结合免疫遗传算法寻优速度快和BP神经网络反演不依赖初始模型等优点,设计了一种将BP神经网络和免疫遗传算法进行有机结合的全局优化反演策略,并将该策略成功地应用于二维高密度电法数据反演.利用免疫遗传算法(Immune Genetic Algorithm,简称IGA)对神经网络的反演参数进行同步优化,提高了电阻率反演的精度.仿真和实验结果验证设计的全局优化反演策略取得了较好的效果,通过与线性反演方法和BP法以及遗传神经网络法等反演方法进行比较,得出该方法具有反演精度更高,反演时间更短等显著优势的结论. 相似文献