共查询到20条相似文献，搜索用时 187 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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快速模拟退火地震反演 总被引：10，自引：3，他引：10
讨论了用模拟退火方法进行地震资料的参数反演，利用快速的降温方式实现模拟退火反演，从而形成了快速模拟退火算法。模拟退火反演的优点是可以突破反演过程中局部最优的限制，获得全局最优解。因此，ＳＡ方法适于解决地震反演中的非凸性目标函数的最优化问题。 相似文献
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用近震源波形资料拟合反演地震的震源破裂过程,所包含的一些不确定因素将对反演结果的精度及可靠性产生影响,文中的数值实验分析了所假定的反演断层模型参数的某些不确定性对反演结果的影响程度,并对观测波形的截取长度对反演精度的影响进行了讨论．结果表明：(1)近震源地震波形资料能较好地分辨断层浅部的破裂过程.然而对断层深部的位错分布的约束和反演能力较差．联合使用近、远场地震波资料进行反演,能反演出一个更为完全的整个断层破裂过程的图像.(2)用近震源地震波资料反演时,反演结果对所假定的反演断层的走向和倾角非常敏感.断层走向偏离真实值2°或倾角偏离真实值5°都会导致一个虚假的反演结果.(3)反演中所使用的介质速度结构模型的不确定性,也会对反演结果产生影响. 相似文献
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Seismic driven probabilistic classification of reservoir facies for static reservoir modelling: a case history in the Barents Sea 总被引：2，自引：0，他引：2
Dario Grana Enrico Paparozzi Silvia Mancini Cristiano Tarchiani 《Geophysical Prospecting》2013,61(3):613-629
In this paper we present a case history of seismic reservoir characterization where we estimate the probability of facies from seismic data and simulate a set of reservoir models honouring seismically‐derived probabilistic information. In appraisal and development phases, seismic data have a key role in reservoir characterization and static reservoir modelling, as in most of the cases seismic data are the only information available far away from the wells. However seismic data do not provide any direct measurements of reservoir properties, which have then to be estimated as a solution of a joint inverse problem. For this reason, we show the application of a complete workflow for static reservoir modelling where seismic data are integrated to derive probability volumes of facies and reservoir properties to condition reservoir geostatistical simulations. The studied case is a clastic reservoir in the Barents Sea, where a complete data set of well logs from five wells and a set of partial‐stacked seismic data are available. The multi‐property workflow is based on seismic inversion, petrophysics and rock physics modelling. In particular, log‐facies are defined on the basis of sedimentological information, petrophysical properties and also their elastic response. The link between petrophysical and elastic attributes is preserved by introducing a rock‐physics model in the inversion methodology. Finally, the uncertainty in the reservoir model is represented by multiple geostatistical realizations. The main result of this workflow is a set of facies realizations and associated rock properties that honour, within a fixed tolerance, seismic and well log data and assess the uncertainty associated with reservoir modelling. 相似文献
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Identification of rock boundaries and structural features from well log response is a fundamental problem in geological field studies. However, in a complex geologic situation, such as in the presence of crystalline rocks where metamorphisms lead to facies changes, it is not easy to discern accurate information from well log data using conventional artificial neural network (ANN) methods. Moreover inferences drawn by such methods are also found to be ambiguous because of the strong overlapping of well log signals, which are generally tainted with deceptive noise. Here, we have developed an alternative ANN approach based on Bayesian statistics using the concept of Hybrid Monte Carlo (HMC)/Markov Chain Monte Carlo (MCMC) inversion scheme for modeling the German Continental Deep Drilling Program (KTB) well log data. MCMC algorithm draws an independent and identically distributed (i.i.d) sample by Markov Chain simulation technique from posterior probability distribution using the principle of statistical mechanics in Hamiltonian dynamics. In this algorithm, each trajectory is updated by approximating the Hamiltonian differential equations through a leapfrog discrimination scheme. We examined the stability and efficiency of the HMC-based approach on “noisy” data assorted with different levels of colored noise. We also perform uncertainty analysis by estimating standard deviation (STD) error map of a posteriori covariance matrix at the network output of three types of lithofacies over the entire length of the litho section of KTB. Our analyses demonstrate that the HMC-based approach renders robust means for classification of complex lithofacies successions from the KTB borehole noisy signals, and hence may provide a useful guide for understanding the crustal inhomogeneity and structural discontinuity in many other tectonically critical and complex regions. 相似文献
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为实现测震台网日志产出的统一化和标准化,提高工作效率,减轻人员负担,基于Android系统,设计开发测震台网日志管理平台,用户可通过手机APP完成地震速报、编目及系统运维日志的增、删、改、查和统计操作。该平台的应用解决了日志管理散乱、查询不便等难题,可为测震台网统一化管理提供支持。 相似文献
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云南数字强震动台网硬件系统建设完成后,有效的强震动台站维护和强震动数据处理应用是面临解决的首要问题。该文实现了云南强震动台网中心应用系统一系列的强震动业务系统应用,使得云南强震动台站自动检查和实时扫描、震动图自动生成（ShakeMap）、强震动台站建设资料、台站维护日志等系统通过应用系统实现无逢对接和集成,系统具有安全认证、信息发布、业务信息处理、外部系统接口4大类功能,为仪器维护人员和数据开发处理提供了一个强有力的平台支撑。该系统经过实际地震考验,有效地提高了强震动数据的处理。 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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Norbert P. Szabó 《Acta Geophysica》2011,59(5):935-953
In the paper factor analysis is applied to well-logging data in order to extract petrophysical information about sedimentary
structures. Statistical processing of well logs used in hydrocarbon exploration results in a factor log, which correlates
with shale volume of the formations. The so-called factor index is defined analogously with natural gamma ray index for describing
a linear relationship between one special factor and shale content. Then a general formula valid for a longer depth interval
is introduced to express a nonlinear relationship between the above quantities. The method can be considered as an independent
source of shale volume estimation, which exploits information inherent in all types of well logs being sensitive to the presence
of shale. For demonstration, two wellbore data sets originated from different areas of the Pannonian Basin of Central Europe
are processed, after which the shale volume is computed and compared to estimations coming from independent inverse modeling. 相似文献
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Predicting reservoir parameters, such as porosity, lithology, and saturations, from geophysical parameters is a problem with non‐unique solutions. The variance in solutions can be extensive, especially for saturation and lithology. However, the reservoir parameters will typically vary smoothly within certain zones—in vertical and horizontal directions. In this work, we integrate spatial correlations in the predicted parameters to constrain the range of predicted solutions from a particular type of inverse rock physics modelling method. Our analysis is based on well‐log data from the Glitne field, where vertical correlations with depth are expected. It was found that the reservoir parameters with the shortest depth correlation (lithology and saturation) provided the strongest constraint to the set of solutions. In addition, due to the interdependence between the reservoir parameters, constraining the predictions by the spatial correlation of one parameter also reduced the number of predictions of the other two parameters. Moreover, the use of additional constraints such as measured log data at specific depth locations can further narrow the range of solutions. 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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Coupled inverse modelling of groundwater flow and mass transport and the worth of concentration data 总被引：1，自引：0，他引：1
Harrie-Jan Hendricks Franssen Jaime Gómez-Hernández Andrés Sahuquillo 《Journal of Hydrology》2003,281(4):281-295
This paper presents the extension of the self-calibrating method to the coupled inverse modelling of groundwater flow and mass transport. The method generates equally likely solutions to the inverse problem that display the variability as observed in the field and are not affected by a linearisation of the state equations. Conditioning to the state variables is measured by an objective function including, among others, the mismatch between the simulated and measured concentrations. Conditioning is achieved by minimising the objective function by gradient-based methods. The gradient contains the partial derivatives of the objective function with respect to: log conductivities, log storativities, prescribed heads at boundaries, retardation coefficients and mass sources. The derivatives of the objective function with respect to log conductivity are the most cumbersome and need the most CPU-time to be evaluated. For this reason, to compute this derivative only advective transport is considered. The gradient is calculated by the adjoint-state method. The method is demonstrated in a controlled, synthetic study, in which the worth of concentration data is analysed. It is shown that concentration data are essential to improve transport predictions and also help to improve aquifer characterisation and flow predictions, especially in the upstream part of the aquifer, even in the case that a considerable amount of other experimental data like conductivities and heads are available. Besides, conditioning to concentration data reduces the ensemble variances of estimated transmissivity, hydraulic head and concentration. 相似文献