共查询到20条相似文献，搜索用时 546 毫秒
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人工神经网络是通过从大量训练数据中学习来拟合复杂非线性函数的有效方法,属于一种数据驱动的机器学习方法.人工神经网络应用于地震反演时可以得到更高分辨率和精度的结果,有着优于传统反演方法的泛化能力和非线性拟合能力.本文对人工神经网络的发展脉络进行了回顾,梳理了基于梯度的学习过程中代价函数的作用,反向传播学习算法的思路,激活函数的不同类型,以及万能近似定理等.特别是对热门的深度神经网络,按照时间先后顺序总结了带卷积核的LeNet-5、AlexNet、VGGNet、GoogLeNet、ResNet、UNet、自编码器和GANs等经典模型.在此基础上,本文分析了深度神经网络在反射系数和子波反演、速度反演、波阻抗反演和地震结构反演中不同网络的拓扑结构、学习算法、激活函数和训练样本等.最后,本文归纳和讨论了用于地震反演的有监督端到端学习网络的流程和关键影响因素等,展望了融入物理规律、基于反演目标函数展开的专用地震反演网络. 相似文献
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提出了一种用小波变换方法反演接收函数的新方法. 通过对接收函数作离散小波变换，将接收函数展开到不同分辨尺度，从一给定的初始模型出发，分别在不同分辨尺度上用广义线性反演方法，对展开后的接收函数进行反演. 并将低阶接收函数的反演结果作为高阶接收函数的初始模型，在大尺度空间找到包含全局极小值的一个邻域，并逐步缩小该邻域.渐进地获取介质结构的跳变信息，从而保证反演结果稳定地收敛到全局极小点，降低接收函数波形反演对初始模型的依赖，尽可能克服波形反演的非唯一性，得到比较可靠的高分辨率的地壳上地幔速度结构． 相似文献
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通过合成地震图的计算,本文系统地分析研究了S波接收函数的运动学及动力学特征,并在接收函数非线性复谱比反演方法的基础上,进一步发展了基于贝叶斯理论的P波和S波接收函数的非线性联合反演方法.
利用本文发展的P波和S波接收函数的非线性联合反演方法和沿独库公路布设的由51个宽频带流动地震台站组成的地震台阵记录的远震体波波形数据得到了中国境内天山300 km深度范围地壳上地幔的P波和S波速度结构,得到了中国境内天山的岩石圈结构和造山动力学模型的一些新认识. 相似文献
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约束变密度界面反演方法 总被引：3，自引：0，他引：3
三维密度界面反演具有严重的多解性,已有方法较少利用已知信息约束反演过程,导致界面反演结果可信度降低.本文在反演过程中,利用已有地质信息作为约束条件,有效提高了三维界面反演结果的准确性.该方法具有如下特点:1)利用指数变密度模型,通过已知密度分布计算模型参数,使其更加接近实际密度分布;2)引入已知深度点约束,提高了反演结果的准确性;3)引入深度加权函数,纠正界面畸变,使其适用于界面起伏较大的情况;4)频率域正演与空间域迭代反演相结合,在提高计算速度的同时保证反演收敛.通过模型检验,证实了方法的有效性,并将该方法应用于中蒙边境地区东段莫霍面深度反演中,效果良好. 相似文献
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提出了针对轴对称二维井间模型的一维、二维联合反演方法.该方法基于纵向成层背景地层的Green函数,以一维反演结果作为联合反演的迭代初始值,同时反演一维背景地层和二维异常剖面的电导率.采用递推技术计算Green函数的系数,可以很方便地同时得到Green函数对空间坐标的积分和对背景层电导率的微分,大大加快了计算雅可比矩阵〖WTHX〗M〖WTBZ〗的速度并使计算方便、准确.反演实例说明了该方法的有效性.反演实例还显示,背景层电导率较二维成像剖面的电导率收敛速度要快,测量数据的精度对二维剖面电导率成像质量的影响大于对背景层电导率分布的影响. 相似文献
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重力反演方法是研究地壳结构和物性界面起伏的有效地球物理手段之一.本文收集了南北地震带南段67个已有的固定台站接收函数反演的Moho面深度结果,并使用基于EGM2008重力异常模型计算的布格重力异常,验证了本文提出的重震联合密度界面反演方法的有效性.利用接收函数对台站下方Moho面深度估计作为先验约束,定义了一类评价函数,通过对重力反演算法中尺度因子,平移因子和稳定性因子的最优选择,最小化重力反演结果与接收函数模型之间的差异.结果表明,本文提出的方法,可以有效地同化不同地球物理方法获得的反演模型,且通过重震联合反演可以改进由于对空间分布不均匀的接收函数结果插值可能而引起的误差.本文还通过引入Crust1.0的Moho面深度为初值,同时考虑地壳密度的横向不均匀分布,通过模型之间的联合反演有效改善了地球物理反演模型间的不一致性问题.本文反演得到的最优化Moho面深度模型与已知67个台站位置接收函数模型之间的标准差约1.9km,小于Crust1.0与接收函数结果模型之间标准差为3.73km的统计结果.本文研究结果对于同化重震反演结果、精化地壳密度界面模型,都具有十分重要的参考意义. 相似文献
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常规非线性反演方法虽然对初始模型的依赖大为减弱，但局部收敛现象和计算速度慢仍然是瓶颈. 本文提出了一种新的反演方法——量子路径积分算法(Quantum Path Integral Algorithm,简称QPIA). 该方法引入量子力学的横向场、传播子等概念，并充分利用量子隧穿效应，大大提高反演的效率，具体是通过对反演目标函数的构建，并以Feynman的传播子来构成模型的接收概率来实现. 在对一维大地电磁模型和实际数据进行试验后，表明该方法比常规反演方法更能够精确、稳定和快速地逼近真实模型. 相似文献
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地壳不同深度介质的地震各向异性是研究地壳不同深度范围变形方式的重要依据.鉴于地壳介质的复杂性,如何从远震体波接收函数中提取不同深度的各向异性参数仍是一个有待深入研究的课题.在已有研究的基础上,本文利用广义反射-透射系数矩阵方法计算的合成地震图,研究了复杂地壳分层各向异性介质的接收函数随反方位角(back azimuth)变化及不同层位各向异性参数对接收函数波场的影响,为各向异性介质接收函数的解释提供了新的理论依据.通过引入粒子群优化理论,发展了分层各向异性介质接收函数全局反演算法.数值及观测数据的验证结果表明,在各向同性速度模型确定的前提下,我们的方法能够可靠地提取地壳分层各向异性参数;在反演中引入曲波变换去噪技术,对于正确解析不同层位的各向异性参数具有重要价值. 相似文献
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A method of approximate magnetotelluric sounding (MTS) data inversion is developed on the basis of the representation of the inverse operator by an artificial neural network in classes of geoelectric structures. A methodology of the neural network inversion of magnetotelluric data is proposed for a family of classes of geoelectric structures and the uncertainty of the inferred results is estimated. A neural network algorithm of MTS data inversion is tested using synthetic 2-D data. 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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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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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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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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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