岩体裂隙网络随机生成及连通性研究

基于裂隙几何参数分布的统计特性,应用Monte Carlo模拟技术生成二维裂隙网络.利用无向图方法实现裂隙网络连通图的绘制,并比较了走向服从均匀分布和正态分布的裂隙连通概率.在此基础上,对Balberg等人提出的渗流临界裂隙数(即主干裂隙数)和临界裂隙长度估算方法进行了验证.结果表明,无向图方法可以方便、快捷地实现裂隙网络连通图的绘制;同等条件下走向服从均匀分布比走向服从正态分布的裂隙连通概率大20％左右;当裂隙连通概率大于90％,Balberg等人提出的渗流的临界裂隙数和临界裂隙长度估计值与实际参数值吻合较好,研究成果为裂隙地下水渗流计算提供了分析方法.     

3D裂隙网络随机模拟及其工程应用研究

岩体中存在的大量随机分布的裂隙对岩体的工程性质及稳定性有着极其重要的影响。传统的工程地质研究方法难以客观地描述其空间分布特征。应用概率与统计学的理论 ,通过统计分析得到裂隙的分布规律性 ,采用随机模拟的方法实现与统计分布相适应的裂隙网络是研究随机裂隙宏观特征的有效途径。通过模拟所得的 3D裂隙网络 ,进一步研究了裂隙的连通特征及工程开挖面上的稳定分析与加固措施优化的方法     

随机与确定耦合的裂隙岩体结构面三维网络模拟

离散裂隙随机网络模型中结构面的分布特征仅代表其统计规律,并非地质成因、结构模式、构造形迹的客观反映,以致后续力学计算及渗流模拟结果可信度偏低。针对国家高放废物处置库甘肃北山预选区出露良好的花岗岩体,基于实测结构面统计规律、内在成生关系及水力联系,在前期纯随机模型关键部位修正特征参数,并融入人工辨识的确定结构模式而构建“随机-确定”耦合模型。图形和渗流模拟两种定量检验结果表明：耦合模型结构面数量更接近客观实际,模型准确度提升约48.8%;耦合模型渗流路径与流量较之前更显客观真实,模拟结果与试验数据的接近程度比随机模型大约1/3。另外,不同孔位渗流结果显示：相比于随机结构面,确定性结构面对区域渗流控制作用更加明显,在渗流模拟中扮演更为重要角色。此耦合模型有益于拓宽结构面网络模拟的发展方向。     

裂隙网络模拟与REV尺度研究

研究了利用平面四边形模拟节理岩体三维裂隙网络的方法。在生成裂隙网络时,同时考虑结构面几何参数和力学参数的随机性。利用裂隙网络研究了确定岩体REV尺度的指标。在此基础上,编制了岩体裂隙网络模拟程序、裂隙网络图形输出程序和岩体REV尺度指标分析程序。通过算例分析,验证了研究成果及程序的合理性,并得出了岩体REV尺度约为各组裂隙中最大迹长期望值3~4倍的结论。研究成果为后续计算岩体等效力学参数奠定了基础。     

三峡水库某库段岩体裂隙网络模拟研究

为合理评价三峡水库某库岸岩体结构特征及工程整治提供依据,对区内岩体进行了裂隙调查及网络模拟研究。研究基于Monte-Carlo模拟方法,在技术上进行了探讨和一定改进。如采用了聚类分析法确定裂隙优势产状、用直接法产生随机数、广义RQD t及裂隙线连通率的计算等。根据野外调查及计算机模拟研究,研究区发育有3组优势裂隙、平均迹长1.45 m左右。间距一般服从对数正态或负指数分布,而走向和迹长基本上不服从常规分布。裂隙线密度在3－6条/m、t = 0.1 m时RQD t值在90 %左右、t = 0.7 m时RQD t值为10 %－20 %、RQD t值变化可能影响库岸岩体在不同方向的稳定性。     

岩体裂隙中渗流场有限元随机模拟分析

采用有限元方法数值,模拟了开度随机分布裂隙中的渗流场问题。根据给定的裂隙开度均值和标准差,随机分布生成有限元模型中的单元裂隙开度,模型中的材料参数和单元属性用ANSYS中的APDL参数化语言赋值。根据有限元随机模拟断面的流量分布和稳态渗流问题的达西定律,计算在不同裂隙开度标准差条件下的等效导水系数,研究等效导水系数与裂隙粗糙度之间的关系。数值模拟结果表明,与光滑平直的裂隙相对比,在裂隙平均开度为常数、开度随机正态分布的条件下,表面粗糙裂隙的等效导水系数略有降低;对于裂隙平均开度为0.5 mm的裂隙模型,裂隙开度标准差从0.01 mm变化到 0.2 mm,其等效导水系数变化小于5 %。传统的渗流控制方程无法精确描述地下水在粗糙裂隙中的流动特征,因为在压力水头的定义中忽略了速度项。     

非连续裂隙网络水流计算机模拟

本文以湖北某地区为例,对工作区二迭及三迭系地层的基岩裂隙进行了统计分析,并对其裂隙网络及水流进行了计算机模拟。利用非连续介质方法就非连续裂隙网络的连通性、裂隙长度对网络导水能力的影响、裂隙网络的方向导水系数、网络内水头分布特征等问题进行了初步的探讨。     

裂隙网络多标度分形计算机模拟

岩体裂隙网络是不均匀，非对称的复杂分形，用简单分数维不能刻画其特征，必须采用多标度分形来研究，才能全面客观地描述其本质。文中介质了隙网络多标度分形计算机模拟原理与方法。     

地质系统热-水-力耦合作用的随机建模初步研究

热-水-力(THM)耦合作用是岩石力学与环境地质中的重要基础理论问题,核废料地质处置库周围的缓冲材料和围岩中的热-水-力耦合现象将影响其力学稳定性、热传导性和渗透性,进而影响放射性核素在裂隙岩体中的迁移规律。核废料或放射性废料的地下深埋处置是国际上正在研究的永久性隔离的有效方法之一。因此,对核废料地质处置法安全性评估的一个重要内容就是对裂隙岩体中力学稳定性与构造应力、地下水渗流及热载荷等的耦合作用之数值模拟和评估。这已成为当前刻不容缓的重要的环境影响评价课题。笔者研究了温度场-渗流场-应力场中热传导系数和渗透率以及岩体力学参数的空间变异性,用实验方法研究三场耦合效应及裂隙岩体的场性能等效处理,试图建立热-水-力耦合作用的随机性数学模型及可视化数值模拟方法,为核废料地质处置安全性评估提供直观的新方法。     

A comparative study for the estimation of geodetic point velocity by artificial neural networks

Space geodesy era provides velocity information which results in the positioning of geodetic points by considering the time evolution. The geodetic point positions on the Earth’s surface change over time due to plate tectonics, and these changes have to be accounted for geodetic purposes. The velocity field of geodetic network is determined from GPS sessions. Velocities of the new structured geodetic points within the geodetic network are estimated from this velocity field by the interpolation methods. In this study, the utility of Artificial Neural Networks (ANN) widely applied in diverse fields of science is investigated in order to estimate the geodetic point velocities. Back Propagation Artificial Neural Network (BPANN) and Radial Basis Function Neural Network (RBFNN) are used to estimate the geodetic point velocities. In order to evaluate the performance of ANNs, the velocities are also interpolated by Kriging (KRIG) method. The results are compared in terms of the root mean square error (RMSE) over five different geodetic networks. It was concluded that the estimation of geodetic point velocity by BPANN is more effective and accurate than by KRIG when the points to be estimated are more than the points known.     

A comparative study of artificial neural networks,Bayesian neural networks and adaptive neuro-fuzzy inference system in groundwater level prediction

Predictive modeling of hydrological time series is essential for groundwater resource development and management. Here, we examined the comparative merits and demerits of three modern soft computing techniques, namely, artificial neural networks (ANN) optimized by scaled conjugate gradient (SCG) (ANN.SCG), Bayesian neural networks (BNN) optimized by SCG (BNN.SCG) with evidence approximation and adaptive neuro-fuzzy inference system (ANFIS) in the predictive modeling of groundwater level fluctuations. As a first step of our analysis, a sensitivity analysis was carried out using automatic relevance determination scheme to examine the relative influence of each of the hydro-meteorological attributes on groundwater level fluctuations. Secondly, the result of stability analysis was studied by perturbing the underlying data sets with different levels of correlated red noise. Finally, guided by the ensuing theoretical experiments, the above techniques were applied to model the groundwater level fluctuation time series of six wells from a hard rock area of Dindigul in Southern India. We used four standard quantitative statistical measures to compare the robustness of the different models. These measures are (1) root mean square error, (2) reduction of error, (3) index of agreement (IA), and (4) Pearson’s correlation coefficient (R). Based on the above analyses, it is found that the ANFIS model performed better in modeling noise-free data than the BNN.SCG and ANN.SCG models. However, modeling of hydrological time series correlated with significant amount of red noise, the BNN.SCG models performed better than both the ANFIS and ANN.SCG models. Hence, appropriate care should be taken for selecting suitable methodology for modeling the complex and noisy hydrological time series. These results may be used to constrain the model of groundwater level fluctuations, which would in turn, facilitate the development and implementation of more effective sustainable groundwater management and planning strategies in semi-arid hard rock area of Dindigul, Southern India and alike.     

储层随机建模方法研究进展

油气储层随机建模方法是近年来新发展起来的一门油气预测技术,它在综合了地质、地球物理信息的基础上,充分利用已知点的储层信息,根据所要预测的储层属性来选择不同的随机模型,从而实现了对油气储层的定量表征及对各种尺度非均质性的定量刻画,其最终模拟结果可以有任意多个以供油藏描述选择。这里简要介绍了目前比较常用的储层随机建模方法、原理,各方法的优缺点,以及不同随机模型的地质适用性,并针对我国储层的特点,指出储层随机建模技术的发展方向。     

Groundwater resources evaluation case study via discrete fracture flow modeling

The Arizona Department of Transportation (ADOT) is preparing to upgrade State Route 260 between Payson and Heber. It is estimated that a total of about one million cubic meters of water will be required for embankment construction during a period of about 84 months to upgrade the first 33.8 km of the highway. ADOT is investigating various sources of construction water for use in the highway improvement project, including groundwater resources along the highway corridor. A region known as the RV site, underlain by fractured granite, is located 12.9 km east of Payson. The site includes three springs, a creek and several wells. Several boreholes and observation wells were made to a maximum depth of 157 m to obtain fracture data and to conduct pumping tests with monitoring. Fracture data recorded by acoustic televiewer logs were used to build a fracture network model for the rock mass. Results of a 24-hour and a 7-day pumping tests were used to calibrate hydraulic parameters of a finite element discrete fracture fluid flow model considering the region as a heterogeneous, anisotropic, fractured medium. A 38-day multi-well pumping test was used to validate the calibrated numerical model. The calibrated model showed the capability to provide reasonably accurate predictions for new pumping tests conducted in the same well field. The validated model was then used to simulate pumping exceeding a 7 year period under different scenarios incorporating different sets of boundary conditions and different pumping rates at multi-wells, with and without recharge, to evaluate the yield of the aquifer and to assess the effect of long-term pumping on the environment. The results indicated that (a) the combined yield of the wells in the RV site is sufficient to meet the water demand for the ADOT highway project and (b) the water levels in the well field would decline between 3.0 and 7.6 m after one year of pumping and by 12.2 to over 30.5 m during the life of the project.     

基于逆Broyden秩1拟牛顿迭代法的岩体裂隙网络渗流特性研究

岩体裂隙粗糙度和流体惯性效应是影响岩体裂隙网络等效渗透性的重要因素。首先回顾了单裂隙中裂隙粗糙度和流体惯性效应对其过流能力的影响;然后基于逆Broyden秩1拟牛顿迭代法求解控制裂隙内流体流动的非线性方程组,研究岩体裂隙粗糙度和流体惯性效应对离散裂隙网络(DFN)等效渗透性的影响;最后,基于两种不同边界条件,对离散裂隙网络模型内流体的流动特性进行了探讨。结果表明,当水力梯度较小(0.5)时,裂隙粗糙度和流体惯性效应对离散裂隙网络渗透性的影响很小;当水力梯度较大(0.5)时,裂隙粗糙度和流体惯性效应对离散裂隙网络渗透性的影响随水力梯度的变化而显著变化;在两种边界条件下,当水力梯度在0.1~10.0之间时,裂隙粗糙度和流体惯性效应对离散裂隙网络过流能力的最大影响分别为18.1%和27.5%。所以,当水力梯度较大时,需要在离散裂隙网络模型的渗流计算中考虑裂隙粗糙度和流体惯性效应的影响。     

A comparative study between sequential Gaussian simulation and kriging method grade modeling in open-pit mining

Grade estimation is very important in designing open pits. In the process of grade estimation, underestimation can result in loss of economic ore, whereas overestimation would unnecessarily increase stripping ratio. Normally, kriging method, which suffers from underestimation and/or overestimation due to smoothing effect, is used for grade estimation. To overcome drawbacks of the kriging method, more efficient techniques such as conditional simulation can be applied. In this paper, utilizing sequential Gaussian conditional simulation, grade models were constructed for Sungun copper deposit situated in the North West of Iran. According to the obtained results, it was observed that conditional simulation can effectively cope with the weakness of kriging method. Also, it was observed that as compared to the kriging method, grade distribution, resulted from the conditional simulation, is almost identical to that of the real exploration data. Accordingly, using conditional simulation, the amount of mineable ore was significantly increased, and also, average net present value as the mines’ most important economic indicator was improved by 40%.