共查询到19条相似文献,搜索用时 108 毫秒
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基于汉南-奎因信息准则的电阻率层析成像径向基神经网络反演 总被引:5,自引:4,他引:1
径向基神经网络(RBFNN)具有结构简单、学习速度快、不易陷入局部极小等优点,能够有效地提高电阻率层析成像反演的收敛速度和求解质量.本文针对电阻率层析成像反演的非线性特征,提出了一种基于汉南-奎因信息准则(HQC)的正交最小二乘法(OLS)学习算法(HQOLS).该算法通过计算HQC的最优值来自动选择RBFNN的网络结构,避免了传统OLS学习算法中阈值参数的设定,保证了网络的泛化性能.通过比较聚类法、梯度法、OLS和HQOLS等学习算法的反演性能,构建了基于RBFNN的电阻率层析成像反演模型.数值仿真和模型反演的结果表明,该方法实现简单,在准确性上优于BP反演,成像质量优于传统最小二乘法反演. 相似文献
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为进一步提高大地电磁非线性反演的稳定性、运算效率及准确度,将遗传神经网络算法引入大地电磁反演.首先针对大地电磁二维地电模型建立BP(Back Propagation)神经网络基本框架进行学习训练,网络输入为已知地电模型的视电阻率参数,输出为该地电模型参数;再利用遗传算法对神经网络学习训练过程进行优化,计算出多种地电模型网络连接权值和阈值的最优解;最后将最优连接权值和阈值对未知模型进行反演测试,网络输入为未知地电模型的视电阻率参数,输出为该地电模型参数.模型实验表明:遗传神经网络算法充分结合了遗传算法的全局寻优性和神经网络的局部寻优性,相比单一神经网络算法,在网络学习训练中提高了解的收敛成功率和计算速度,在反演测试中能更准确地逼近真实模型.将遗传神经网络算法与最小二乘正则化反演进行对比,理论模型和实测数据都验证了遗传神经网络算法在大地电磁反演中的可行性和有效性. 相似文献
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作为全局非线性优化的新方法之一的遗传算法,近年来已从生物工程流行到大地电磁测深资料解释中.然而,大地电磁反演问题具有不适定性,解的非唯一性.通过结合求解不适定问题的Tikhonov正则化方法,本文采用实数编码遗传算法求解大地电磁二维反演问题.此算法在构建目标函数时引入正则化的思想,利用遗传算法求解最优化问题.常规的基于局部线性化的最优化反演方法易使解陷入局部极小值,而且严重的依赖初始模型的选择.与传统线性化的迭代反演方法相比,实数编码遗传算法能够克服传统方法的不足且能获得更好的反演结果.通过对大地电磁测深理论模型进行计算,结果表明:该算法具有收敛速度快、解的精度高和避免出现早熟等优点,可用于大地电磁资料解释. 相似文献
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《地球物理学进展》2016,(4)
使用Zelt和Barton的方法,通过一个计算效率高的有限差分求解eikonal方程,正演计算走时和射线路径.使用最小二乘QR分解法,求解稀疏线性系统方程组.使用正则化层析反演,结合用户给定的最小的、最平坦和最平滑的扰动限制,每一个加权因子随深度变化.结合数据残差和模型粗糙度的最小化,为数据残差提供一个最平滑的近似模型.该反演方法为非线性反演,需要一个初始模型,在每一次迭代时,需要计算新的射线路径.使用二维初至走时数据,对某油田二维井间地震实际资料进行无限频率初至走时层析反演.将反演所得到的速度与井的测井速度曲线相比较,二者吻合程度较高,表明该反演方法所得速度的分辨率比较高.证实了二维无限频率初至走时层析反演可以为全波形反演提供一个分辨率较高的长波长速度模型,从而为全波形反演井间地震实际资料提供了一个比较可靠的初始速度模型. 相似文献
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《应用地球物理》2016,(2)
针对传统神经网络在电阻率成像反演中存在的过拟合和易陷入局部极值等问题,提出了一种基于剪枝贝叶斯神经网络(PBNN)的非线性反演算法和一种基于K-medoids聚类的样本设计方法。在基于K-medoids聚类的样本设计方法中,利用观测数据的聚类结果提供先验信息构造神经网络的训练样本,从而有针对性地指导神经网络的训练过程;剪枝贝叶斯神经网络是在贝叶斯正则化的基础上,通过评估各隐节点对反演结果的影响来自适应确定神经网络的隐层结构,根据小样本条件下训练样本的先验分布特征,选择了基于广义平均的超参数αk来引导剪枝过程。通过与地球物理领域内其它常用的自适应正则化方法相比较,验证了本文算法的有效性。理论数据和实测数据反演的结果表明:该方法能够较好地抑制神经网络训练过程中噪声的影响,提高网络的泛化能力,其反演结果优于BPNN反演、RBFNN反演和RRBFNN反演以及传统的最小二乘反演。 相似文献
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Inversion of DC resistivity data using neural networks 总被引:9,自引:0,他引:9
The inversion of geoelectrical resistivity data is a difficult task due to its non-linear nature. In this work, the neural network (NN) approach is studied to solve both 1D and 2D resistivity inverse problems. The efficiency of a widespread, supervised training network, the back-propagation technique and its applicability to the resistivity problem, is investigated. Several NN paradigms have been tried on a basis of trial-and-error for two types of data set. In the 1D problem, the batch back-propagation paradigm was efficient while another paradigm, called resilient propagation, was used in the 2D problem. The network was trained with synthetic examples and tested on another set of synthetic data as well as on the field data. The neural network gave a result highly correlated with that of conventional serial algorithms. It proved to be a fast, accurate and objective method for depth and resistivity estimation of both 1D and 2D DC resistivity data. The main advantage of using NN for resistivity inversion is that once the network has been trained it can perform the inversion of any vertical electrical sounding data set very rapidly. 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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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
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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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Andreas Ahl 《Geophysical Prospecting》2003,51(2):89-98
Artificial neural networks were used to implement an automatic inversion of frequency‐domain airborne electromagnetic (AEM) data that do not require a priori information about the survey area. Two classes of model, i.e. homogeneous half‐space models and horizontally layered half‐space models with two layers, are used in this 1D inversion, and for each data point the selection of the class of 1D model is performed prior to the inversion, also using an artificial neural network. The proposed inversion method was tested in a survey area situated in Austria, northwest of Vienna in the Bohemian Massif. The results of the inversion were compared with the geological setting, logging results, and seismic and gravimetric measurements. This comparison shows a good correlation between the AEM models and the known geological and geophysical data. 相似文献
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G.S. O'Brien 《Geophysical Prospecting》2020,68(6):1758-1770
Seismically derived amplitude-versus-angle attributes along with well constraints are the base inputs into inverting seismic into subsurface properties. Conditioning the common image gathers is a common workflow in quantitative inversion and leads to a more accurate inversion product due to the removal of post-migration artefacts. Here, we apply a neural network to condition the post-migration gathers. The network is a cycle generative adversarial network, CycleGAN, which was designed for image-to-image translation. This can be considered the same problem as translating an artefact rich seismic gather to an artefact free seismic gather. To assess the feasibility of applying the network to pre-stack conditioning, synthetic data sets were generated to train different networks for different tasks. The networks were trained to remove white noise, residual de-multiples, gather flattening and a combination of the above for conditioning. The results show that a trained network was able remove white noise providing a more robust amplitude-versus-offset calculation. Another network trained using synthetic gathers with and without multiples assisted in multiple removal. However, instability around primary preservation has been observed so the network works better as a residual de-multiple method. For gather conditioning, a network was trained with the unpaired artefact-rich and artefact-free training data where the artefacts included complex moveout, noise and multiples. When applied to the test data sets, the networks cleaned the artefact-rich test data and translated complex moveout into flat gathers whilst preserving the amplitude response. Finally, two networks are applied to real data where a gather based on the well logs is used to quantify the match between the conditioned gathers and the raw gathers. The first network used synthetic data to train the network and, when applied to real data, provided a better tie with the well. The second network was trained with synthetic gathers whose properties were constrained by real seismic gathers from near the well. As anticipated, the network trained on the representative training data outperforms the network trained using the unconstrained data. However, the ability of the first network to condition the gather indicates that a sweep of networks can be trained without the need for real data and applied in a manner analogous to the way parameters are adjusted in traditional geophysical methods. The results show that the different neural networks can offer an alternative or augmentation to the existing geophysical workflow for conditioning pre-stack seismic gathers. 相似文献
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从地球物理反演的基本概念出发,认为地球物理反演是 对实测数据的地球物理属性的理解或解释,多方法的地球物理数据联合反演是一种多传感器 的数据融合. 本文分析了地球物理数据的模糊特性,采用基于语义的模糊化方法,使不同物 理意义和尺度的特征数据及测区的地质和地球物理背景成为一体,结合地球物理专家解释的 方法,利用基于模糊逻辑系统的神经网络实现了融合. 该方法充分利用了各种地球物理探测 数据的全部信息,避免了线性反演的复杂计算;其数据融合的观点,为解决地球物理联合反 演问题提供了新的思路. 通过模拟实验和应用实例验证,该方法是有效的. 相似文献
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