基于Chebyshev走时逼近的三维多次反射射线计算

在利用有限差分等基于网格的数值分析方法解地震波走时所满足的程函方程时,由于速度模型的网格化离散等原因,会使走时在各网格节点之间不具有计算射线路径所要求的光滑性,即走时在邻近网格节点之间不具有连续的一阶导数。因此,直接利用网格节点走时计算射线路径会使最终的射线路径不光滑。为解决这个问题,已有研究者提出了基于B样条插值的逆向梯度方案（法）。然而,在速度发生突变时,B样条逆向梯度法所计算出的射线路径会具有较大的误差。针对这个问题,首先采用适合于解最小零偏差逼近及最佳平方逼近问题的Chebyshev多项式取代B样条对来自于分区多级计算方案的网格节点走时进行最佳逼近,得到在最小平方意义下的最优走时公式;然后采用与B样条逆向梯度法类似的计算过程得到光滑的射线路径。数值实验表明,利用Chebyshev多项式逼近走时可以得到具有很高精度的多次反射射线路径,在多次波偏移成像研究中具有潜在的价值。     

Hybrid Gaussian-cubic radial basis functions for scattered data interpolation

Scattered data interpolation schemes using kriging and radial basis functions (RBFs) have the advantage of being meshless and dimensional independent; however, for the datasets having insufficient observations, RBFs have the advantage over geostatistical methods as the latter requires variogram study and statistical expertise. Moreover, RBFs can be used for scattered data interpolation with very good convergence, which makes them desirable for shape function interpolation in meshless methods for numerical solution of partial differential equations. For interpolation of large datasets, however, RBFs in their usual form, lead to solving an ill-conditioned system of equations, for which, a small error in the data can cause a significantly large error in the interpolated solution. In order to reduce this limitation, we propose a hybrid kernel by using the conventional Gaussian and a shape parameter independent cubic kernel. Global particle swarm optimization method has been used to analyze the optimal values of the shape parameter as well as the weight coefficients controlling the Gaussian and the cubic part in the hybridization. Through a series of numerical tests, we demonstrate that such hybridization stabilizes the interpolation scheme by yielding a far superior implementation compared to those obtained by using only the Gaussian or cubic kernels. The proposed kernel maintains the accuracy and stability at small shape parameter as well as relatively large degrees of freedom, which exhibit its potential for scattered data interpolation and intrigues its application in global as well as local meshless methods for numerical solution of PDEs.     

Reduced order models for many-query subsurface flow applications

Inverse modeling involves repeated evaluations of forward models, which can be computationally prohibitive for large numerical models. To reduce the overall computational burden of these simulations, we study the use of reduced order models (ROMs) as numerical surrogates. These ROMs usually involve using solutions to high-fidelity models at different sample points within the parameter space to construct an approximate solution at any point within the parameter space. This paper examines an input–output relational approach based on Gaussian process regression (GPR). We show that these ROMs are more accurate than the linear lookup tables with the same number of high-fidelity simulations. We describe an adaptive sampling procedure that automatically selects optimal sample points and demonstrate the use of GPR to a smooth response surface and a response surface with abrupt changes. We also describe how GPR can be used to construct ROMs for models with heterogeneous material properties. Finally, we demonstrate how the use of a GPR-based ROM in two many-query applications—uncertainty quantification and global sensitivity analysis—significantly reduces the total computational effort.     

基于单位分解的扩展径向点插值无网格法

针对线弹性断裂力学问题,提出扩展径向点插值无网格法（X-RPIM）。该方法基于单位分解思想,在传统径向点插值无网格法的位移模式中加入扩展项来描述裂纹两侧的不连续位移场和裂尖奇异场。由于其形函数具有Kronecker ? 函数性质,易于施加本质边界条件。详细描述了X-RPIM不连续位移模式的建立,支配方程的离散形式以及J积分计算混合模式裂纹的应力强度因子的实现过程,讨论了不同积分区域对应力强度因子的影响。数值算例分析证明了该方法在求解断裂问题时的可行性和有效性,同时说明扩展径向点插值无网格法在模拟裂纹扩展问题时具有良好的前景。     

Ensemble clustering for efficient robust optimization of naturally fractured reservoirs

In the development of naturally fractured reservoirs (NFRs), the existence of natural fractures induces severe fingering and breakthrough. To manage the flooding process and improve the ultimate recovery, we propose a numerical workflow to generate optimal production schedules for smart wells, in which the inflow control valve (ICV) settings can be controlled individually. To properly consider the uncertainty introduced by randomly distributed natural fractures, the robust optimization would require a large ensemble size and it would be computationally demanding. In this work, a hierarchical clustering method is proposed to select representative models for the robust optimization in order to avoid redundant simulation runs and improve the efficiency of the robust optimization. By reducing the full ensemble of models into a small subset ensemble, the efficiency of the robust optimization algorithm is significantly improved. The robust optimization is performed using the StoSAG scheme to find the optimal well controls that maximize the net-present-value (NPV) of the NFR’s development. Due to the discrete property of a natural fracture field, traditional feature extraction methods such as model-parameter-based clustering may not be directly applicable. Therefore, two different kinds of clustering-based optimization methods, a state-based (e.g., s _w profiles) clustering and a response-based (e.g., production rates) clustering, are proposed and compared. The computational results show that the robust clustering optimization could increase the computational efficiency significantly without sacrificing much expected NPV of the robust optimization. Moreover, the performance of different clustering algorithms varies widely in correspondence to different selections of clustering features. By properly extracting model features, the clustered subset could adequately represent the uncertainty of the full ensemble.     

基于二维降阶Hermite插值的铁路路基动力响应2.5D有限元模拟

2.5D有限元方法在铁路路基动力响应研究领域中的应用渐趋广泛。针对其在求解随机不平顺条件下路基动力响应时计算效率显著下降的问题,构建了基于二维降阶Hermite插值的2.5D有限元路基动力响应快速计算框架。以路基在频率-波数域动力响应的基本特征为依据确定了插值原则,讨论了插值点分布和数量对插值精度的影响。研究表明：采用二维降阶Hermite插值方法可以实现随机不平顺条件下路基动力响应的快速计算。相比插值点非均匀分布,插值点均匀分布可以兼顾幅值和相位的插值精度,适应性更好。此外,该方法的计算效率仅与插值点数量相关,不受随机不平顺谐波数量的影响,在模拟随机不平顺条件下路基动力响应方面具备显著的优势。     

Comparison of surrogate models with different methods in groundwater remediation process

Surrogate modelling is an effective tool for reducing computational burden of simulation optimization. In this article, polynomial regression (PR), radial basis function artificial neural network (RBFANN), and kriging methods were compared for building surrogate models of a multiphase flow simulation model in a simplified nitrobenzene contaminated aquifer remediation problem. In the model accuracy analysis process, a 10-fold cross validation method was adopted to evaluate the approximation accuracy of the three surrogate models. The results demonstrated that: RBFANN surrogate model and kriging surrogate model had acceptable approximation accuracy, and further that kriging model’s approximation accuracy was slightly higher than RBFANN model. However, the PR model demonstrated unacceptably poor approximation accuracy. Therefore, the RBFANN and kriging surrogates were selected and used in the optimization process to identify the most cost-effective remediation strategy at a nitrobenzene-contaminated site. The optimal remediation costs obtained with the two surrogate-based optimization models were similar, and had similar computational burden. These two surrogate-based optimization models are efficient tools for optimal groundwater remediation strategy identification.     

A nonlinear multiparameter prestack seismic inversion method based on hybrid optimization approach

Multiparameter prestack seismic inversion is one of the most powerful techniques in quantitatively estimating subsurface petrophysical properties. However, it remains a challenging problem due to the nonlinearity and ill-posedness of the inversion process. Traditional regularization approach can stabilize the solution but at the cost of smoothing valuable geological boundaries. In addition, compared with linearized optimization methods, global optimization techniques can obtain better results regardless of initial models, especially for multiparameter prestack inversion. However, when solving multiparameter prestack inversion problems, the application of standard global optimization algorithms maybe limited due to the issue of high computational cost (e.g., simulating annealing) or premature convergence (e.g., particle swarm optimization). In this paper, we propose a hybrid optimization-based multiparameter prestack inversion method. In this method, we introduce a prior constraint term featured by multiple regularization functions, intended to preserve layered boundaries of geological formations; in particular, to address the problem of premature convergence existing in standard particle swarm optimization algorithm, we propose a hybrid optimization strategy by hybridizing particle swarm optimization and very fast simulating annealing to solve the nonlinear optimization problem. We demonstrate the effectiveness of the proposed inversion method by conducting synthetic test and field data application, both of which show encouraging results.     

应用自然邻接点插值法的块体非连续变形分析

传统的非连续变形分析法(DDA)采用线性位移模式存在诸多缺陷。为准确计算块体应力场,传统上一般直接增加位移函数的多项式阶次,或进行子块体划分或耦合有限元等改进措施,但应用上仍不够方便有效。建议引进无网格节点位移插值模式,采用自然单元法中的自然邻接点插值(NNI)法,具有插值特性,易于准确实施边界条件或材料连续性条件,且具有无网格特征和良好的计算精度,计算更快效。可在此基础上进一步分析大块体弯曲、裂纹扩展破坏形式等,以解决线性位移模式等的不足。     

Interpolation for geochemical surface reconstruction incorporating topographic catchment definitions

Geochemical surfaces are reconstructed by interpolating geochemical measurements obtained from stream-water and stream-sediment samples. The geographical region that influences (and therefore is represented by) the value of a geochemial sample is its topographic catchment area. However, standard convention is to treat and to record the stream sample in the database as a point location, and to reconstruct geochemical surfaces utilizing conventional point interpolation procedures. These interpolation procedures assume, generally, that a data point exerts geographical influence away from itself in all directions, and that influence declines with distance away from that data point. Conventional interpolation procedures are poorly suited for reconstructing geochemical surfaces from stream samples; they do not take into account the true geographic area that geochemical sample points represent (topographic catchments). In this paper we propose a method of interpolation which assumes that data points are representative of their topographic catchment areas. Experimental data indicates that a surface reconstruction procedure which preserves the areal character of geochemical samples provides results more meaningful than surfaces reconstructed using more conventional interpolation techniques.     

Interpolation with Splines in Tension: A Green''s Function Approach

Interpolation and gridding of data are procedures in the physical sciences and are accomplished typically using an averaging or finite difference scheme on an equidistant grid. Cubic splines are popular because of their smooth appearances; however, these functions can have undesirable oscillations between data points. Adding tension to the spline overcomes this deficiency. Here, we derive a technique for interpolation and gridding in one, two, and three dimensions using Green's functions for splines in tension and examine some of the properties of these functions. For moderate amounts of data, the Green's function technique is superior to conventional finite-difference methods because (1) both data values and directional gradients can be used to constrain the model surface, (2) noise can be suppressed easily by seeking a least-squares fit rather than exact interpolation, and (3) the model can be evaluated at arbitrary locations rather than only on a rectangular grid. We also show that the inclusion of tension greatly improves the stability of the method relative to gridding without tension. Moreover, the one-dimensional situation can be extended easily to handle parametric curve fitting in the plane and in space. Finally, we demonstrate the new method on both synthetic and real data and discuss the merits and drawbacks of the Green's function technique.     

A Comparative Study for 3D Surface Modeling of Coal Deposit by Spatial Interpolation Approaches

The planning stages of mining require comprehensive and detailed analyses. The proper determination of the orebody boundary is one of the most important points to provide optimum model structure and projections. The limits can be determined by different methods based on the site geology. Although some three dimensional (3D) models were proposed for providing detailed information concerning a mine deposit, developing a solid model via a 3D approach is novelty. In other words, surface modeling should be performed for creating a solid model and determining limits of the deposit. In this way, sensible generation of the surface model can be achieved. This study investigated the estimation capability of the polynomial approach, which is a novel spatial interpolation method, for modeling a coal deposit surface. The performance of the proposed model was compared with some conventional methods in the literature. The results showed that the polynomial interpolation method is an effective method to employ for surface modeling of a mine deposit.     

考虑参数空间变异性的非饱和土坡可靠度分析

在考虑多个土体参数空间变异性的基础上,提出了基于拉丁超立方抽样的非饱和土坡稳定可靠度分析的非侵入式随机有限元法。利用Hermite随机多项式展开拟合边坡安全系数与输入参数间的隐式函数关系,采用拉丁超立方抽样技术产生输入参数样本点,通过Karhunen-Loève展开方法离散土体渗透系数、有效黏聚力和内摩擦角随机场,并编写了计算程序NISFEM-KL-LHS。研究了该方法在稳定渗流条件下非饱和土坡可靠度分析中的应用。结果表明：非侵入式随机有限元法为考虑多个土体参数空间变异性的非饱和土坡可靠度问题提供了一种有效的分析工具。土体渗透系数空间变异性和坡面降雨强度对边坡地下水位和最危险滑动面位置均有明显的影响。当降雨强度与饱和渗透系数的比值大于0.01时,边坡失效概率急剧增加。当土体参数变异性或者参数间负相关性较大时,忽略土体参数空间变异性会明显高估边坡失效概率。     

Interpolation with Splines in Tension: A Green's Function Approach

Interpolation and gridding of data are procedures in the physical sciences and are accomplished typically using an averaging or finite difference scheme on an equidistant grid. Cubic splines are popular because of their smooth appearances; however, these functions can have undesirable oscillations between data points. Adding tension to the spline overcomes this deficiency. Here, we derive a technique for interpolation and gridding in one, two, and three dimensions using Green's functions for splines in tension and examine some of the properties of these functions. For moderate amounts of data, the Green's function technique is superior to conventional finite-difference methods because (1) both data values and directional gradients can be used to constrain the model surface, (2) noise can be suppressed easily by seeking a least-squares fit rather than exact interpolation, and (3) the model can be evaluated at arbitrary locations rather than only on a rectangular grid. We also show that the inclusion of tension greatly improves the stability of the method relative to gridding without tension. Moreover, the one-dimensional situation can be extended easily to handle parametric curve fitting in the plane and in space. Finally, we demonstrate the new method on both synthetic and real data and discuss the merits and drawbacks of the Green's function technique.     

深层搅拌桩复合地基的优化设计

在满足承载力控制和沉降量控制要求的基础上,采用数值模拟的方法分析最优桩长和桩间距,完成了深层搅拌桩复合地基的优化设计,以达到满足建设需求的同时最大限度地节省建设费用的目的。根据深层搅拌桩复合地基的特点,讨论了复合地基承载力特征值的计算方法,分析了沉降量的构成,对沉降计算方法进行了探讨,并选用单位元法和分层总和法来评价复合地基加固区和下卧层的压缩性。结合深层搅拌桩复合地基的设计要素、承载力、荷载压力、沉降量等指标和具体数值参数,通过设置最优桩长和桩间距来求取满足承载力要求的最小总桩长,并通过编程实现了优化过程。通过程序计算,得到了一组满足地基承载力要求的桩长、基础底面积、置换率的最优数据组合,实现了对深层搅拌桩复合地基的优化设计。     

水资源系统管理的广义保证率与多目标模糊优化

综述了水资源系统管理可靠性研究现状,指出水资源系统管理中存在着两种不确定性——随机性和模糊性.从一般情况出发,在同时考虑随机性与模糊性的基础上,提出了广义保证率概念、定义及其分析计算方法,并用多目标模糊优选随机动态规划(MOFOSDP)模型研究了一类带广义保证率目标的水资源系统运行管理多目标优化问题.实例计算结果表明,提出的带广义保证率目标的MOFOSDP模型合理可行,优于传统方法.     

Moving Surface Spline Interpolation Based on Green’s Function

Some commonly used interpolation algorithms are analyzed briefly in this paper. Among all of the methods, biharmonic spline interpolation, which is based on Green’s function and proposed by Sandwell, has become the mainstream method for its high precision, simplicity and flexibility. However, the minimum curvature method has two flaws. First, it suffers from undesirable oscillations between data points, which is solved by interpolation with splines in tension. Second, the computation time is approximately proportional to the cube of the number of data constraints, making the method slow for situations with dense data coverage. Focusing on the second problem, this paper introduces the moving surface spline interpolation method based on Green’s function, and the interpolation error equations are deduced. Because the proposed method only chooses the nearest data points by using the merge sort algorithm for interpolating, the computation time is greatly decreased. The optimal number of the nearest points can be determined by using the interpolation error estimation equation. No matter how many data points there are, this method can be implemented without difficulty. Examples show that the proposed method can obtain high interpolation precision and high computation speed at the same time.     

深埋隧道衬砌稳定可靠度的Kriging插值法与遗传算法协同优化求解法

首先利用遗传算法(GA)的全局优化能力,解决了Kriging插值预测算法中相关参数的精确求解问题。针对衬砌结构力学状态表征量与基本参数是高度非线性关系而无法明晰表达的难题,建立了基于Kriging插值预测法的状态表征量等代显式通用模型。以抗拉强度控制参量为例,提出了采用拉丁抽样等代显式通用模型的具体隧道工程衬砌结构力学状态方程构造程序。基于以状态方程为约束条件建立的可靠度指标计算模式,构建起Kriging与GA协同优化的隧道衬砌稳定可靠度求解方法。实例应用对比表明,该方法与精确解的相对误差为4.8%,计算工作量仅为其2.5%。与目前已有的同类方法相比,具有明显的优越性。     

CSAMT相位校正及其在煤矿采空积水区探测中的应用

可控源音频大地电磁法（CSAMT）磁场数据易受电磁干扰,且会导致相位数据质量严重下降。采用参考邻近测点高质量的相位数据,并结合最小二乘拟合及插值的方法,对受影响的相位数据进行校正。经正演模拟处理分析发现:三次多项式插值效果较好,不但能够更准确地还原相位曲线的变化特征,而且将校正后的相位数据参与到反演计算中,使反演结果更加准确、可靠。将该方法应用于山西某矿采空积水区探测,探测结果较为准确地反映了已知采空积水区的范围。