Implicit integration under mixed controls of a breakage model for unsaturated crushable soils

This paper discusses a series of stress point algorithms for a breakage model for unsaturated granular soils. Such model is characterized by highly nonlinear coupling terms introduced by breakage‐dependent hydro‐mechanical energy potentials. To integrate accurately and efficiently its constitutive equations, specific algorithms have been formulated using a backward Euler scheme. In particular, because implementation and verification of unsaturated soil models often require the use of mixed controls, the incorporation of various hydro‐mechanical conditions has been tackled. First, it is shown that the degree of saturation can be replaced with suction in the constitutive equations through a partial Legendre transformation of the energy potentials, thus changing the thermomechanical state variables and enabling a straightforward implementation of a different control mode. Then, to accommodate more complex control scenarios without redefining the energy potentials, a hybrid strategy has been used, combining the return mapping scheme with linearized constraints. It is shown that this linearization strategy guarantees similar levels of accuracy compared with a conventional strain–suction‐controlled implicit integration. In addition, it is shown that the use of linearized constraints offers the possibility to use the same framework to integrate a variety of control conditions (e.g., net stress and/or water‐content control). The convergence profiles indicate that both schemes preserve the advantages of implicit integration, that is, asymptotic quadratic convergence and unconditional stability. Finally, the performance of the two implicit schemes has been compared with that of an explicit algorithm with automatic sub‐stepping and error control, showing that for the selected breakage model, implicit integration leads to a significant reduction of the computational cost. Such features support the use of the proposed hybrid scheme also in other modeling contexts, especially when strongly nonlinear models have to be implemented and/or validated by using non‐standard hydro‐mechanical control conditions. Copyright © 2015 John Wiley & Sons, Ltd.     

利用重测定的震源深度特征探讨2013年吉林前郭5.8级震群发震因素

利用Pn-Pg震相组合的局部搜索法,采用5种地壳速度模型对2013年吉林前郭5.8级地震震群进行震源深度测定。计算结果显示,5种结果的震源深度的主要分布特征相似。对选取的32个地震震源深度进行重新测定,结果最小为6 km,最大14 km,主要集中在10 km左右,系统深度偏差约1.74 km,震源深度分辨率约1.58km,重定位后地震震中优势分布呈北西向。与当地的沉积层厚度分布对比,这些地震最有可能发生在盆地基底的基岩顶层,而不在油气圈闭的沉积盖层内,并且地震震群分布走向与通榆—长春断裂走向相一致。结合该地区的历史地震活动与构造关系,最后推测这些地震与北西向的通榆—长春断裂构造运动密切相关,而不是油田作业的直接产物。     

地震层析成像LSQR算法的并行化

讨论了地震层析成像的LSQR算法(最小二乘QR分解). 在建立偏导数矩阵方程组时,对区内地震在方程中保留震源项,引入正交投影算子进行参数分离,对区外远震采用传统的平滑处理方式,用LSQR法求解联立的方程组. 由于区内地震的正交分解处理和区外远震的平滑处理,使得偏导数矩阵中的非零元素成倍增加,对于大型反演问题,这些非零元素常常达到几十GB到几百GB的数量级,巨量的内存占用成为LSQR算法的瓶颈. 针对这一问题,本文研究了偏导数矩阵中非零元素的分布规律,设计出合理的存储结构,采用分布式存储进行矩阵计算,提出了LSQR算法的并行化方案,并在联想深腾6800超级计算机上实现. 导出了LSQR算法的并行效率估算公式. 对两个地区的实际地震层析成像数据进行了效率测试.     

A New Strategy for Solving a Class of Constrained Nonlinear Optimization Problems Related to Weather and Climate Predictability

There are three common types of predictability problemsin weather and climate, which each involve different constrained nonlinearoptimization problems: the lower bound of maximum predictable time, theupper bound of maximum prediction error, and the lower bound of maximumallowable initial error and parameter error. Highly efficient algorithmshave been developed to solve the second optimization problem. And thisoptimization problem can be used in realistic models for weather and climateto study the upper bound of the maximum prediction error. Although afiltering strategy has been adopted to solve the other two problems, directsolutions are very time-consuming even for a very simple model, whichtherefore limits the applicability of these two predictability problems inrealistic models. In this paper, a new strategy is designed to solve theseproblems, involving the use of the existing highly efficient algorithms forthe second predictability problem in particular. Furthermore, a series ofcomparisons between the older filtering strategy and the new method areperformed. It is demonstrated that the new strategy not only outputs thesame results as the old one, but is also more computationally efficient.This would suggest that it is possible to study the predictability problemsassociated with these two nonlinear optimization problems in realisticforecast models of weather or climate.     

Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches

ABSTRACT

In this research, five hybrid novel machine learning approaches, artificial neural network (ANN)-embedded grey wolf optimizer (ANN-GWO), multi-verse optimizer (ANN-MVO), particle swarm optimizer (ANN-PSO), whale optimization algorithm (ANN-WOA) and ant lion optimizer (ANN-ALO), were applied for modelling monthly reference evapotranspiration (ET_o) at Ranichauri (India) and Dar El Beida (Algeria) stations. The estimates yielded by hybrid machine learning models were compared against three models, Valiantzas-1, 2 and 3 based on root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC) and Willmott index (WI). The results of comparison show that the ANN-GWO-1 model with five input variables (T_min, T_max, RH, U_s, R_s) provides better estimates at both study stations (RMSE = 0.0592/0.0808, NSE = 0.9972/0.9956, PCC = 0.9986/0.9978, and WI = 0.9993/0.9989). Also, the adopted modelling strategy can build a truthful expert intelligent system for estimating the monthly ET_o at study stations.     

Accelerating the convergence of coupled geomechanical‐reservoir simulations

The pressure variations during the production of petroleum reservoir induce stress changes in and around the reservoir. Such changes of the stress state can induce marked deformation of geological structures for stress sensitive reservoirs as chalk or unconsolidated sand reservoirs. The compaction of those reservoirs during depletion affects the pressure field and so the reservoir productivity. Therefore, the evaluation of the geomechanical effects requires to solve in a coupling way the geomechanical problem and the reservoir multiphase fluid flow problem. In this paper, we formulate the coupled geomechanical‐reservoir problem as a non‐linear fixed point problem and improve the resolution of the coupling problem by comparing in terms of robustness and convergence different algorithms. We study two accelerated algorithms which are much more robust and faster than the conventional staggered algorithm and we conclude that they should be used for the iterative resolution of coupled reservoir‐geomechanical problem. Copyright © 2006 John Wiley & Sons, Ltd.     

甘肃景泰地区内陆叠合型盆地成因地层格架

在详细研究组成内陆叠合型盆地的各个单型盆地的沉积体系三维构成的基础上 ,采用动态成因地层和层序地层分析方法 ,恢复了叠合型内陆盆地的成因地层格架 ,并根据各类等时地层界面的构型 ,将盆地地层序列划分为 7个沉积幕、3个构造沉积幕。进而分析了内陆盆地在同生裂陷和衰减坳陷等不同阶段的充填过程。     

DCT域遥感影像的自适应遗传优化增强

本文在DCT域中提出了一种基于遗传算法的遥感影像增强技术,针对DCT域遥感影像增强参数需人工设定的问题,利用自适应遗传算法对其进行优化选取:设计了一种考虑偏差的适应度函数以驱动遗传算法进化,通过无分块思想的使用实现了无人工干预、无块效应的遥感影像增强.该方法不但保持了遥感影像的原始色调而且显著提升了遥感影像方差、平均梯度、信息熵等质量测度,有效改善了遥感影像的视觉效果,便于后续影像处理及应用.     

巴西构造应力场的遗传有限单元法反演

基于观测的主应力方位资料,应用遗传有限单元法对巴西及邻区边界位移和板内力源进行了反演,确认了前人的研究结果：板块边界作用是控制区域应力场的主要因素,山脉和海岸地形造成的扩展力是控制应力场的一个重要因素．同时显示了板块在垂直载荷下的弯曲对局部水平应力场造成相当的影响．反演结果也表明,除了过去公认的东西向挤压作用外,巴西还受到相当明显的南北向构造作用．它的存在与最近ＧＰＳ资料的分析是一致的．研究还表明遗传有限单元法是偏微分方程反演问题求解的一种有效方法．     

利用测井资料自动识别藻灰岩

藻灰岩在测井曲线图上具有不同于其它岩性的特征,据此确定了7种测井参数(自然伽玛、自然电位、井径、声波时差、补偿中子、补偿密度及深电阻率),将测井数据归一化后得到了相应的岩性判别样本参数。利用F-Means中速度较快的聚类迭代(LBG)算法对研究样本进行分类,优选了一组数据用于判别分析。在进行Q因子分析的基础上建立了用于岩性判别的3个判别函数,并对花土沟油田某井段进行了岩性识别预测分析。