岩体结构与岩体水力耦合计算模型

水力耦合作用是岩体中力学过程与渗流过程相互作用的物理过程。水力耦合机理的理解是水力耦合分析的关键问题,其耦合机理是由岩体结构特性决定的。在分析岩体水力耦合过程基础上,根据岩体的基本结构及代表性单元体（REV）是否存在提出了建立水力耦合模型的方法。当裂隙岩体中不存在代表性单元体（REV）时,提出了裂隙岩体多重介质流固耦合分析的全耦合数学模型,给出流固耦合模型数值方法求解的数学模型及有限元计算表达式。程序编制和验证工作正在进行中。     

Hydromechanical modelling of the SEALEX experiments

Numerical modelling of coupled physical processes in bentonite–sand mixtures under the geological conditions is significant for designing and constructing sealing systems in deep underground repositories for highly radioactive nuclear waste. Within the framework of DECOVALEX 2015, Task A, this work presents the model validation of OpenGeoSys by numerical modelling of coupled hydromechanical (HM) processes in bentonite–sand mixtures. Parameters used in the HM model were determined by modelling the laboratory tests of the sealing experiment (SEALEX). Afterwards these parameters were applied for the modelling of a small-scale mock-up test considering the influence of technological gap and incidental fail of the seal in the sealing system. In order to investigate the availability of employing these HM parameters and numerical models directly to field predictions, the modelling results and measured data of an in situ SEALEX experiment were analysed comparatively. The modelling results reproduced well the main features in HM behaviour of the compacted bentonite–sand mixture, which denotes that the adopted HM models and parameters are adequate for describing the HM processes in the sealing system. It is necessary to take the elastoplastic behaviour and evolution of the permeability of bentonite–sand mixtures into account when using the adopted models to reproduce the HM processes of a sealing system.     

基于蚁群算法的含水层参数识别方法

根据渗流场的水头和流量观测数据,建立了基于蚁群算法的地下含水层参数识别方法,含水层参数识别反问题的不适定性由解的不唯一性和不稳定性所表征。与传统的基于梯度的优化方法相比较,对于参数识别反问题蚁群算法能够收敛到全局最优解。为了将蚁群算法引入到参数识别反问题,介绍了一些数值算例,并且将参数识别结果与数值模拟结果进行了比较。研究表明,所提出的参数识别方法具有鲁棒性、全局收敛性和抗观测噪音的能力。     

岩浆分离结晶作用过程微量元素反演

介绍岩浆分离结晶作用中微量元素系统反演的基本原理和方法,反演计算中极小化问题的求解,反演所需要的优选模式参数估计方法,进行反演的计算机程序,利用模拟数据和实测数据检验了方法和程序的有效性.     

Comparing the Gradual Deformation with the Probability Perturbation Method for Solving Inverse Problems

Inverse problems are ubiquitous in the Earth Sciences. Many such problems are ill-posed in the sense that multiple solutions can be found that match the data to be inverted. To impose restrictions on these solutions, a prior distribution of the model parameters is required. In a spatial context this prior model can be as simple as a Multi-Gaussian law with prior covariance matrix, or could come in the form of a complex training image describing the prior statistics of the model parameters. In this paper, two methods for generating inverse solutions constrained to such prior model are compared. The gradual deformation method treats the problem of finding inverse solution as an optimization problem. Using a perturbation mechanism, the gradual deformation method searches (optimizes) in the prior model space for those solutions that match the data to be inverted. The perturbation mechanism guarantees that the prior model statistics are honored. However, it is shown with a simple example that this perturbation method does not necessarily draw accurately samples from a given posterior distribution when the inverse problem is framed within a Bayesian context. On the other hand, the probability perturbation method approaches the inverse problem as a data integration problem. This method explicitly deals with the problem of combining prior probabilities with pre-posterior probabilities derived from the data. It is shown that the sampling properties of the probability perturbation method approach the accuracy of well-known Markov chain Monte Carlo samplers such as the rejection sampler. The paper uses simple examples to illustrate the clear differences between these two methods     

土壤和沉积物中重金属生物可利用性的评估

土壤和沉积物中重金属被植物吸收利用可通过单一化学萃取方法评估。几种重金属生物可利用态的萃取方法被用于土壤和沉积物中重金属的提取。根据实验萃取结果和文献报道,对中性盐、弱酸、螯合剂和联合萃取法进行评价,对影响生物可利用态萃取方法的因素进行讨论,并对土壤和沉积物中重金属生物可利用性评价方法的建立和有效统一使用提出建议。     

土壤腐殖物质特征及其对有机碳长时间尺度稳定性的指示：以陕西洛川黄土剖面为例

在长时间尺度上研究土壤中腐殖物质的组成和腐殖化特征对于正确评价土壤有机质的稳定性具有重要意义。文中对洛川黄土/古土壤剖面S8以上的17个黄土和古土壤样品中的胡敏酸（HA）、富里酸（FA）和胡敏素（HM）进行定量研究,并对HA和FA的E465、E665值等进行了测定。结合该课题组对矿物组成、总有机碳（TOC）及其各组分含...     

混沌人工鱼群算法在重力坝材料参数反演中的应用

首先介绍了一种随机搜索优化方法-人工鱼群算法（AFSA）,同时根据混沌（CHAOS）的遍历性和随机性等特点,将混沌系统和人工鱼群算法相结合形成了一种新的融合优化算法-混沌人工鱼群算法（CAFSA）。将混沌人工鱼群算法应用到混凝土大坝材料参数反演中。经算例分析表明,与人工鱼群算法相比较,混沌人工鱼群算法具有收敛快、效率高和结果精度高等优点。从而为解决类似的系统优化和参数识别问题提供了一种新的方法。     

基于微粒群算法的大坝材料参数反分析研究

将微粒群算法应用于大坝参数反分析，同时分析了群体规模对算法的搜索效率和搜索质量的影响以及微粒群反分析算法的数值稳定性。对算例的分析结果表明，基于微粒群算法的大坝参数反分析方法简便易行，收敛精度高，且具有很好的抗噪音能力，是一种新的有效、可靠的参数反分析方法。     

基于优化的BP神经网络地层可钻性预测模型

提出了一种粒子群算法（PSO）优化的BP网络模型预测地层可钻性的新方法。利用粒子群算法优化BP网络模型的参数,避免了BP网络陷入局部极小值的缺点,提高了模型的预测速度和精度。结合钻探实例,利用测井资料和地层可钻性级别的关系建立了可钻性级别实时预测模型,并将该模型与传统的BP网络进行对比,结果表明,该模型优于BP网络,具有较高的精度和较快的收敛速度,有一定的适用性。     

The Hydro-Mechanically Coupled Response of Rock Fractures

Summary. In a fractured rock mass, variations in stress and fluid pressure induced by engineering activities can significantly affect the hydrogeological properties. A significant change in fracture transmissivities can also be experienced in the far-field. The simulation of this kind of change requires a Hydro-Mechanical (HM) coupled model. The purpose of this paper is to show how such a model can be used to analyse the evolution of deformation and pressure in a fracture subjected to fluid injection. A 2D BEM-FEM code is used to solve the non-linear system of equations that describe the dependency of transmissivity on local fracture closure. The results of a sensitivity analysis of the essential fracture parameters allow one to gain insight into the importance of the HM models in the framework of the hydrogeology of fractured rock masses. Results obtained from a system of two impervious blocks and a saturated fracture are reported, in order to show the possibilities offered by this technique.     

神经网络在岩体力学参数和地应力场反演中的应用

BP神经网络已广泛地应用于岩体力学参数和初始应力场的反演分析,但在实际应用中,BP网络存在着网络训练易于过度、收敛速度慢、易陷入局部极小以及隐层节点数难于确定等缺点。采用RBF网络和改进的BP网络,利用基于有限差分格式的快速拉格朗日算法进行正分析计算,依据若干测点的正应力数据,反演了计算区域的岩体力学参数以及初始应力场。算例表明,RBF神经网络与快速拉格朗日算法相结合,在样本容量相同的情况下,反演分析的精度、网络的拓扑结构以及学习、收敛速度,均优于采用BP网络的反演算法。     

Single‐and multi‐objective genetic algorithm optimization for identifying soil parameters

This paper discusses the quality of the procedure employed in identifying soil parameters by inverse analysis. This procedure includes a FEM‐simulation for which two constitutive models—a linear elastic perfectly plastic Mohr–Coulomb model and a strain‐hardening elasto‐plastic model—are successively considered. Two kinds of optimization algorithms have been used: a deterministic simplex method and a stochastic genetic method. The soil data come from the results of two pressuremeter tests, complemented by triaxial and resonant column testing. First, the inverse analysis has been performed separately on each pressuremeter test. The genetic method presents the advantage of providing a collection of satisfactory solutions, among which a geotechnical engineer has to choose the optimal one based on his scientific background and/or additional analyses based on further experimental test results. This advantage is enhanced when all the constitutive parameters sensitive to the considered problem have to be identified without restrictions in the search space. Second, the experimental values of the two pressuremeter tests have been processed simultaneously, so that the inverse analysis becomes a multi‐objective optimization problem. The genetic method allows the user to choose the most suitable parameter set according to the Pareto frontier and to guarantee the coherence between the tests. The sets of optimized parameters obtained from inverse analyses are then used to calculate the response of a spread footing, which is part of a predictive benchmark. The numerical results with respect to both the constitutive models and the inverse analysis procedure are discussed. Copyright © 2011 John Wiley & Sons, Ltd.     

Identifying parameters controlling soil delayed behaviour from laboratory and in situ pressuremeter testing

The aim of this paper is to present a methodology for identifying the soil parameters controlling the delayed behaviour from laboratory and in situ pressuremeter tests by using an elasto‐viscoplastic model (EVP‐MCC) based on Perzyna's overstress theory and on the elasto‐plastic Modified Cam Clay model. The influence of both the model parameters and the soil permeability was studied under the loading condition of pressuremeter tests by coupling the proposed model equations with Biot's consolidation theory. On the basis of the parametric study, a methodology for identifying model parameters and soil permeability by inverse analysis from three levels of constant strain rate pressuremeter tests was then proposed and applied on tests performed on natural Saint‐Herblain clay. The methodology was validated by comparing the optimized values of soil parameters and the values of the same parameters obtained from laboratory test results, and also by using the identified parameters to simulate other tests on the same samples. The analysis of the drainage condition and the strain rate effect during a pressuremeter test demonstrated the coupled influence of consolidation and viscous effects on the test results. The numerical results also showed that the inverse analysis procedure could successfully determine the parameters controlling the time‐dependent soil behaviour. Copyright © 2008 John Wiley & Sons, Ltd.     

Impact assessment of anthropogenic activities on air quality, using lichen Remototrachyna awasthii as biomonitor

The study was carried out with an aim to assess the heavy metal (HM) and polycyclic aromatic hydrocarbons (PAHs) in the air of a biodiversity as well as tourist-rich area of Western Ghats by applying a most frequent growing lichen Remototrachyna awasthii (Hale and Patw.) Divakar and A. Crespo, as biomonitor. Thalli of R. awasthii were collected from eight sites of Mahabaleshwar area located in Western Ghats. Samples were prepared for HM and PAHs quantification by ICP-MS and HPLC, respectively. Total metal concentration (HM) ranged from 644 to 2,277.5 μg g^?1 while PAHs concentration between 0.193 and 54.78 μg g^?1. HM and PAHs concentrations were the highest at Bus Stand while control site (Lingmala Fall) exhibited the lowest concentration of HM as well as PAHs followed by samples from Wilson point (both these sites are having trekking route). It was also evident from this study that vehicular emission played a significant role in the release of HM and PAHs as pollutants in the environment. The effectiveness of R. awasthii as biomonitor could be further investigated by comparing this species with other biomonitors.     

水文地质参数反演的Hooke-Jeeves粒子群混合算法

水文地质参数寻优结果的好坏会直接影响到地下水数值模拟的精度,而参数寻优结果很大程度上取决于寻优方法的选择。粒子群算法是一种基于群智能的随机全局寻优方法,算法的缺陷是后期搜索效率低劣。基于随机寻优算法的混合策略,引入有效的约束处理手段和粒子群算法惯性因子的动态非线性调整技术,有机融合粒子群算法与Hooke-Jeeves方法,提出一种适用于水文地质参数反演的HJPSO混合算法。应用研究表明,HJPSO混合算法在参数反演计算中求解精度高、收敛速度快、寻优性能强,是一种值得推广的水文地质参数识别方法。     

The Probability Perturbation Method: A New Look at Bayesian Inverse Modeling

Building of models in the Earth Sciences often requires the solution of an inverse problem: some unknown model parameters need to be calibrated with actual measurements. In most cases, the set of measurements cannot completely and uniquely determine the model parameters; hence multiple models can describe the same data set. Bayesian inverse theory provides a framework for solving this problem. Bayesian methods rely on the fact that the conditional probability of the model parameters given the data (the posterior) is proportional to the likelihood of observing the data and a prior belief expressed as a prior distribution of the model parameters. In case the prior distribution is not Gaussian and the relation between data and parameters (forward model) is strongly non-linear, one has to resort to iterative samplers, often Markov chain Monte Carlo methods, for generating samples that fit the data likelihood and reflect the prior model statistics. While theoretically sound, such methods can be slow to converge, and are often impractical when the forward model is CPU demanding. In this paper, we propose a new sampling method that allows to sample from a variety of priors and condition model parameters to a variety of data types. The method does not rely on the traditional Bayesian decomposition of posterior into likelihood and prior, instead it uses so-called pre-posterior distributions, i.e. the probability of the model parameters given some subset of the data. The use of pre-posterior allows to decompose the data into so-called, “easy data” (or linear data) and “difficult data” (or nonlinear data). The method relies on fast non-iterative sequential simulation to generate model realizations. The difficult data is matched by perturbing an initial realization using a perturbation mechanism termed “probability perturbation.” The probability perturbation method moves the initial guess closer to matching the difficult data, while maintaining the prior model statistics and the conditioning to the linear data. Several examples are used to illustrate the properties of this method.     

Efficient reservoir history matching using subspace vectors

In this paper, we describe a method of history matching in which changes to the reservoir model are constructed from a limited set of basis vectors. The purpose of this reparameterization is to reduce the cost of a Newton iteration, without altering the final estimate of model parameters and without substantially slowing the rate of convergence. The utility of a subspace method depends on several factors, including the choice and number of the subspace vectors to be used. Computational gains in efficiency result partly from a reduction in the size of the matrix system that must be solved in a Newton iteration. More important contributions, however, result from a reduction in the number of sensitivity coefficients that must be computed, reduction in the dimensions of the matrices that must be multiplied, and elimination of matrix products involving the inverse of the prior model covariance matrix. These factors affect the efficiency of each Newton iteration. Although computation of the optimal set of subspace vectors may be expensive, we show that the rate of convergence and the final results are somewhat insensitive to the choice of subspace vectors. We also show that it is desirable to start with a small number of subspace vectors and gradually increase the number at each Newton iteration until an acceptable level of data mismatch is obtained.     

基于共轭梯度法的全波形反演

全波形反演是一种全新的地震成像方法,主要利用全波形信息反演地下介质参数,通过非线性优化波场理论值和观测值的残差实现波形反演。基于时间域声波方程,建立了时间域波场残差目标函数,以分层模型为例,分别从波场精度、目标函数收敛性和运行时间3个方面,比较了共轭梯度（Conjugate Gradient,CG）算法和拟牛顿算法（Broyden-Fletcher-Goldfarb-Shanno,BFGS）反演的效果。同时,应用共轭梯度法对正、逆断层模型和Marmousi模型进行了速度结构反演。反演结果表明:共轭梯度法计算效率较高,反演得到的速度模型精度更高,反演效果较好,是一种有效的波形反演方法。      

遗传算法在反演三维地下水流模型参数中的应用

本文以非均质各向同性承压三维非稳定流为理想模型，结合有限元法讨论了用遗传算法反演水文地质参数问题，计算结果表明，本文在瘴简单遗传算法（SGA）的基础上提出的优体克隆＋子体优生遗传算法（BCC－YGCD－GA）具有收敛速度快，解的精度高和避免出现早期等优点，在水资源评价和矿床疏干计算中有广阔的应用前景。