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1.
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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Mansour A. Al-Garni 《Arabian Journal of Geosciences》2016,9(2):156
A new approach is proposed to interpret magnetic anomalies caused by 2D fault structures. This approach is based on the artificial neural network inversion, utilizing particularly modular neural network algorithm. The inversion process is implemented to estimate the parameters of 2D fault structures where it has been verified first on synthetic models. The results of the inversion show that the parameters derived from the inversion agree well with the true ones. The analysis of noise has been studied in order to investigate the stability of the approach where it has been tested for contaminated anomalies with 5 and 10 % of white Gaussian noise. The results of the inversion provide satisfactory results even with contaminated signals.The validity of the approach has been demonstrated through real data taken from New South Wales, Australia. A comparable and satisfactory agreement is shown between the inversion results of the neural network and those from techniques published in literatures. 相似文献
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Mansour A. Al-Garni 《Arabian Journal of Geosciences》2009,2(2):175-184
Magnetic surveys have been used for mineral exploration where different data processing techniques were used to derive the parameters of causative targets. In this respect, the neural network (NN) technique was used to estimate the magnetic causative target parameters. Examples of NN inversion have been tested on synthetic examples where the NN was trained well using forward models of the vertical magnetic effect of a vertical sheet and a horizontal circular cylinder. Specifically, modular neural network (MNN) inversion has been used for the parameter estimation of the causative targets, where the sigmoid function was used as the activation function. The effect of random noise and the error estimation of the horizontal location have been analyzed. When NN is applied to real data, it estimates successfully the parameters of the causative targets such as burial depths, magnetic constants, and angle of polarization. Hilbert transform has been used to locate the source origin, which is important for the NN inversion. This approach has more advantages than the conventional data inversions in terms of its efficiency and flexibility. It also gives fast solutions. The MNN approach has been applied to the Kursk and Manjampalli anomalies, where the results were shown to be in good agreement with the other techniques published in the literature. 相似文献
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高密度电法技术在煤矿地质灾害勘探中发挥着重要的作用。近年来,以BP（Backpropagation）神经网络为代表的一类非线性反演方法被广泛运用到高密度电法的反演中。针对BP神经网络方法在高密度电法反演中存在的易陷入局部极小、收敛缓慢、反演精度差等问题,将BP神经网络算法与遗传算法（Genetic Algorithm,简称GA算法）联合演算,实现高密度电法的二维非线性反演。通过典型地电模型对该方法进行验证,结果表明遗传算法能有效优化BP神经网络的权值和阈值,提高了算法的全局寻优性。 相似文献
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Evaluation of Interpolation Accuracy of Neural Kriging with Application to Temperature-Distribution Analysis 总被引：8，自引：0，他引：8
An interpolation method based on a multilayer neural network (MNN), has been examined and tested for the data of irregular sample locations. The main advantage of MNN is in that it can deal with geoscience data with nonlinear behavior and extract characteristics from complex and noisy images. The training of MNN is used to modify connection weights between nodes located in different layers by a simulated annealing algorithm (one of the optimization algorithms of the network). In this process, three types of errors are considered: differences in values, semivariograms, and gradients between sample data and outputs from the trained network. The training is continued until the summation of these errors converges to an acceptably small value. Because the MNN trained by this learning criterion can estimate a value at an arbitrary location, this method is a form of kriging and termed Neural Kriging (NK). In order to evaluate the effectiveness of NK, a problem on restoration ability of a defined reference surface from randomly chosen discrete data was prepared. Two types of surfaces, whose semivariograms are expressed by isotropic spherical and geometric anisotropic gaussian models, were examined in this problem. Though the interpolation accuracy depended on the arrangement pattern of the sample locations for the same number of data, the interpolation errors of NK were shown to be smaller than both those of ordinary MNN and ordinal kriging. NK can also produce a contour map in consideration of gradient constraints. Furthermore, NK was applied to distribution analysis of subsurface temperatures using geothermal investigation loggings of the Hohi area in southwest Japan. In spite of the restricted quantity of sample data, the interpolation results revealed high temperature zones and convection patterns of hydrothermal fluids. NK is regarded as an interpolation method with high accuracy that can be used for regionalized variables with any structure of spatial correlation. 相似文献
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地铁深基坑支护的遗传神经网络位移反分析 总被引：2，自引：0，他引：2
针对目前已有的各种位移反分析方法存在的缺陷,利用神经网络具有的非线性映射能力和遗传算法具有的全局随机搜索能力,提出了一种基于遗传神经网络进行深基坑支护的位移反分析方法。该方法改变了BP算法依赖梯度信息的指导来调整网络权值的方法,而是利用遗传算法全局性搜索的特点,寻找最合适的网络连接权和网络结构等来达到优化的目的。结合地铁深基坑支护位移计算,应用该方法对某一地铁深基坑土体的力学参数进行了反演。结果表明：将位移观测值作为网络输入数据,土体力学参数作为输出数据,在较大的解空间内,该位移反分析方法收敛速度快、解的稳定性好、反演结果精度高,是一种理想的位移反分析方法。最后,采用该软件结合一个工程实例实现了应用遗传神经网络进行的基坑支护位移反分析。 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
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本文提出了三度体重磁异常的人机联作校正－迭代反演方法。该方法用二度半组合多边形棱柱体来逼近三度体，从而把三度体重磁异常反演问题转化为二度半体的反演问题；为了消除组合体迭加场的影响，该方法采用了一种校正－迭代技术。理论模型反演计算表明，该方法实际可行。 相似文献
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A new approach is developed to determine the model parameters of a two-dimensional inclined sheet from self-potential anomaly. In this method, the numerical horizontal self-potential gradient obtained from self-potential anomaly is convolved using Hilbert transform to obtain the vertical self-potential gradient. The complex gradient is the sum of horizontal and vertical gradient anomalies. The horizontal and vertical gradients are plotted in one graph to form the complex gradient graph. By defining few characteristic points and distances along the complex gradient profile, procedures are then formulated using the analytical functions of the complex gradients to obtain the model parameters of sheet-like structures. The validity of the new proposed method has been tested on synthetic data with and without random noise. The obtained parameters are in congruence with the model parameters when using noise-free synthetic data. After adding 10% random error in the synthetic data, the maximum error in model parameters is 11.8%. Moreover, the method have been applied to analyze and interpret the self-potential anomaly measured on a graphite ore body at southern Bavarian woods, Germany to prove its efficiency where an acceptable agreement has been noticed between the obtained results and the other published results. 相似文献
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本文提出了三度体重磁异常的人机联作校正－迭代反演方法。该方法用二度半组合多边形棱柱体来逼近三度体，从而把三度体重磁异常反演问题转化为二度半体的反演问题；为了消除组合体迭加场的影响，该方法采用了一种校正－迭代技术。理论模型反演计算表明，该方法实际可行。 相似文献
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BP神经网络方法在二维密度界面的反演中取得了较好的效果,但在反演三维界面时,由于模型更复杂、参数更多,BP神经网络的收敛速度和反演精度都有一定程度的下降。为了改善反演效果,本文利用遗传算法对BP神经网络的权值、阈值选择过程进行优化,获得了更好的网络模型;并将此模型应用于密度界面模型的反演中,预测误差从上百米减小到数十米,同时迭代计算步数减少了近2/3,有效减少了计算时间,反演结果更准确。利用基于遗传算法优化的BP神经网络反演了法国某地区莫霍面深度,预测相对误差仅为1.8%,取得了较好的应用效果。基于遗传算法优化的BP神经网络在密度界面的反演中具有良好的应用价值和研究前景。 相似文献
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The decrease of density contrast with depth in sedimentary basins is approximated by an exponential function. The anomaly
equation, in frequency domain, of a prismatic model with an exponential density function is derived. The method has been extended
to derive the Fourier transforms of the gravity anomalies of the sedimentary basin, wherein the basin is viewed as vertical
prisms placed in juxtaposition. The gravity anomalies of the sedimentary basin are obtained by taking the inverse Fourier
transforms. Filon’s method has been extended for calculating accurate inverse Fourier transforms. The accuracy of the method
has been tested using a synthetic example. A combination of space and frequency domain methods have been developed for inversion
of gravity anomalies over the sedimentary basin. The method has been applied to interpret one synthetic profile and one field
profile over the Godavari basin. The method developed in this paper to calculate the inverse Fourier transforms yields continuous
spectrum with accurate values. The maximum depth deduced from the gravity anomalies is of the same order as the depth encountered
to the basement at the Aswaraopeta borewell. 相似文献
18.
V. A. Kulikov A. A. Bobachev I. N. Modin A. Yu. Palenov I. D. Sterligova 《Moscow University Geology Bulletin》2014,69(3):171-177
A paleovalley in the vicinity of the village of Aleksandrovka (Kaluga oblast) has been investigated within the framework of a geophysical survey by Moscow State University for 5 years. A local magnetic anomaly in the region of the paleovalley (a grid of 300 × 1000 m and a scale of 1: 2500) has been confirmed according to the results of electrical-resistivity tomography (ERT) and the spectral induced polarization (IP) method for a series of four profiles. IP measurements at several frequencies have permitted some additional conclusions about the nature of the anomaly. Based on the ERT 2D inversion and IP results, a clear correlation has been revealed between the magnetic anomalies and the high-frequency IP anomalies. 相似文献
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Ahmad Neyamadpour W. A. T. Wan Abdullah Samsudin Taib 《Journal of Earth System Science》2010,119(1):27-40
The objective of this paper is to investigate the applicability of artificial neural networks in inverting quasi-3D DC resistivity
imaging data. An electrical resistivity imaging survey was carried out along seven parallel lines using a dipole-dipole array
to confirm the validation of the results of an inversion using an artificial neural network technique. The model used to produce
synthetic data to train the artificial neural network was a homogeneous medium of 100Ωm resistivity with an embedded anomalous
body of 1000Ωm resistivity. The network was trained using 21 datasets (comprising 12159 data points) and tested on another
11 synthetic datasets (comprising 6369 data points) and on real field data. Another 24 test datasets (comprising 13896 data
points) consisting of different resistivities for the background and the anomalous bodies were used in order to test the interpolation
and extrapolation of network properties. Different learning paradigms were tried in the training process of the neural network,
with the resilient propagation paradigm being the most efficient. The number of nodes, hidden layers, and efficient values
for learning rate and momentum coefficient have been studied. Although a significant correlation between results of the neural
network and the conventional robust inversion technique was found, the ANN results show more details of the subsurface structure,
and the RMS misfits for the results of the neural network are less than seen with conventional methods. The interpreted results
show that the trained network was able to invert quasi-3D electrical resistivity imaging data obtained by dipole-dipole configuration
both rapidly and accurately. 相似文献