Construction of three-phase data to model multiphase flow in porous media: Comparing an optimization approach to the finite element approach     

Raphaël di Chiara Roupert  Gerhard Schäfer  Philippe Ackerer  Michel Quintard  Guy Chavent 《Comptes Rendus Geoscience》2010,342(11):855-863

Multiphase flow modelling is a major issue in the assessment of groundwater pollution. Three-phase flows are commonly governed by mathematical models that associate a pressure equation with two saturation equations. These equations involve a number of secondary variables that reflect the fluid behaviour in a porous medium. To improve the computational efficiency of multiphase flow simulators, several simplified reformulations of three-phase flow equations have been proposed. However, they require the construction of new secondary variables adapted to the reformulated flow equations. In this article, two different approaches are compared to quantify these variables. A numerical example is given for a typical fine sand.  相似文献   

Investigating the effect of soil models on deformations caused by braced excavations through an inverse-analysis technique   总被引：1，自引：0，他引：1  

Yu-Geng Tang  Gordon Tung-Chin Kung 《Computers and Geotechnics》2010

In this study, a series of inverse-analysis numerical experiments was performed to investigate the effect of soil models on the deformations caused by excavation by using the finite element method. The nonlinear optimization technique that was incorporated into the finite element code was used for the inverse-analysis numerical experiments. Three soil models (the hyperbolic model, pseudo-plasticity model, and modified pseudo-plasticity model) were employed in the intended numerical experiments on a well-documented excavation case history. The results indicate that wall deflection due to excavation can be accurately back-figured by each of the three soil models, while the ground surface settlement can be reasonably optimized only by the pseudo-plasticity model and the modified pseudo-plasticity model. Importantly, the modified pseudo-plasticity model can yield more reasonable simulations when the wall deflection and the ground surface settlement are simultaneously back-figured. The results show that selection of an adequate soil model that is capable of adequately describing the stress–strain-strength characteristics of the soils is essentially crucial when predicting the excavation-induced ground response.  相似文献   

输电线路抢修塔拉线基础的优化设计     

甘凤林  包永挺  张建波 《中国勘察设计》2010,(7):65-67

通过分析以往地锚结构设计方法的不足,研究输电线路抢修塔拉线基础的优化设计方法。运用现行杆塔基础设计理论建立数学优化模型,应用MATLAB优化工具箱,编制地锚结构优化程序,设计出输电线路抢修塔最安全,经济,合理的基础型式。此优化方法可以在目前输电线路拉线基础中推广使用,并发挥其显著的经济效应和社会效益。  相似文献   

全国重点地震台站观测环境优化改造关键技术与展望     

肖武军  赵刚  何少林  郑先进  谭俊义  张尧  张克诚 《中国地震》2021,37(1):156-163

稳定可靠的地震台站观测环境是台站产出高质量观测数据的基本要求。对台站观测环境的改造,是中国地震局近年来项目重点支持的方向。全国重点地震台站优化改造项目致力于地震台站的观测环境、基础设施和工作条件等保障系统的优化改造,使其适应和满足现代地震观测技术发展的需要。本文系统地总结了重点台站优化改造项目自2001年开展实施的相关情况,并对关键技术进行分析,主要体现在测震仪器的防震加固设计、地磁观测室无磁性工艺、形变山洞防潮保温、地电阻率观测架空线路防雷改造、台站标准化改造等方面。通过这些新技术和工艺,保障了台站观测环境,提升了观测数据质量,为地震监测、地震预测预报和地震科学研究提供了高质量的基础数据支撑。此外,探讨了新形势下台站优化改造的新内容与新技术,为今后台站改造的发展方向提供一定参考。  相似文献   

面向山地徒步应急救援路径规划的改进蚁群算法研究     

伍跃飞  李建微  毕胜  朱馨  王前锋 《地球信息科学学报》2023,25(1):90-101

当消防事故发生在无明显道路或道路稀疏的野外复杂山区时,如何在复杂山地环境中规划安全、快速通过的路线至关重要。针对蚁群算法在复杂山地路径规划中容易陷入局部最优以及搜索时间较长的问题,本文提出一种适用于细粒度野外山地环境的徒步应急救援路径规划算法。本文首先根据已有文献分析地表信息与人类运动速度之间的关系,综合地表灌木盖度与地形坡度因素设计寻优算法的目标函数和启发函数;接着采用定向范围视野的蚂蚁搜索方式,决定蚁群算法寻优过程中每一步的网格选择;最后采用拉普拉斯分布调整初始信息素、添加隔离信息素、融合遗传算子与分组更新常规信息素的方法改进蚁群算法。将算法应用到400×400、1000×1000、5000×5000、10 000×10 000网格数的野外山地环境进行实验对比,实验结果表明,采用定向范围视野与优化启发函数的各蚁群算法在四组实验中均能得到可行路径,验证了方法的有效性;本文算法求解的路径质量优于另外三种算法,在四组实验中分别提高了0.52%～4.95%、4.71%～5.39%、2.26%～13.11%、3.84%～9.16%;此外,在野外三维山地环境中,定向范围视野的搜索方式缩减了搜索...  相似文献   

华北地区跨断层流动形变监测场地优化初探   总被引：1，自引：0，他引：1  

陆明勇  于海生  李志雄  李杰  王秀文  李岩峰  寇建新  冯军  张平法 《中国地震》2018,34(4):837-844

首先分析了华北地区跨断层流动形变监测场地布测现状及监测效能,继而依据跨断层流动形变测量规范及监测的可控性、均匀性、目的性以及监测场地监测效能等,结合该区域断裂活动及地震活动性研究结果,给出华北地区跨断层流动形变监测场地拟优化布设结果。该优化布设结果消除了华北地区跨断层流动形变网监测地震活动盲区,有助于提高该区跨断层流动形变网监测地震活动能力。  相似文献   

Time-frequency decomposition of seismic signals via quantum swarm evolutionary matching pursuit     

Amir Semnani  Liang Wang  Mehdi Ostadhassan  Majid Nabi-Bidhendi  Babak Nadjar Araabi 《Geophysical Prospecting》2019,67(7):1701-1719

Matching pursuit belongs to the category of spectral decomposition approaches that use a pre-defined discrete wavelet dictionary in order to decompose a signal adaptively. Although disengaged from windowing issues, matching point demands high computational costs as extraction of all local structure of signal requires a large size dictionary. Thus in order to find the best match wavelet, it is required to search the whole space. To reduce the computational cost of greedy matching pursuit, two artificial intelligence methods, (1) quantum inspired evolutionary algorithm and (2) particle swarm optimization, are introduced for two successive steps: (a) initial estimation and (b) optimization of wavelet parameters. We call this algorithm quantum swarm evolutionary matching pursuit. Quantum swarm evolutionary matching pursuit starts with a small colony of population at which each individual, is potentially a transformed form of a time-frequency atom. To attain maximum pursuit of the potential candidate wavelets with the residual, the colony members are adjusted in an evolutionary way. In addition, the quantum computing concepts such as quantum bit, quantum gate, and superposition of states are introduced into the method. The algorithm parameters such as social and cognitive learning factors, population size and global migration period are optimized using seismic signals. In applying matching pursuit to geophysical data, typically complex trace attributes are used for initial estimation of wavelet parameters, however, in this study it was shown that using complex trace attributes are sensitive to noisy data and would have lower rate of convergence. The algorithm performance over noisy signals, using non-orthogonal dictionaries are investigated and compared with other methods such as orthogonal matching pursuit. The results illustrate that quantum swarm evolutionary matching pursuit has the least sensitivity to noise and higher rate of convergence. Finally, the algorithm is applied to both modelled seismograms and real data for detection of low frequency anomalies to validate the findings.  相似文献   

SegOptim—A new R package for optimizing object-based image analyses of high-spatial resolution remotely-sensed data     

《International Journal of Applied Earth Observation and Geoinformation》2019

Geographic Object-based Image Analysis (GEOBIA) is increasingly used to process high-spatial resolution imagery, with applications ranging from single species detection to habitat and land cover mapping. Image segmentation plays a key role in GEOBIA workflows, allowing to partition images into homogenous and mutually exclusive regions. Nonetheless, segmentation techniques require a robust parameterization to achieve the best results. Frequently, inappropriate parameterization leads to sub-optimal results and difficulties in comparing distinct methods.Here, we present an approach based on Genetic Algorithms (GA) to optimize image segmentation parameters by using the performance scores from object-based classification, thus allowing to assess the adequacy of a segmented image in relation to the classification problem. This approach was implemented in a new R package called SegOptim, in which several segmentation algorithms are interfaced, mostly from open-source software (GRASS GIS, Orfeo Toolbox, RSGISLib, SAGA GIS, TerraLib), but also from proprietary software (ESRI ArcGIS). SegOptim also provides access to several machine-learning classification algorithms currently available in R, including Gradient Boosted Modelling, Support Vector Machines, and Random Forest.We tested our approach using very-high to high spatial resolution images collected from an Unmanned Aerial Vehicle (0.03 – 0.10 m), WorldView-2 (2 m), RapidEye (5 m) and Sentinel-2 (10 – 20 m) in six different test sites located in northern Portugal with varying environmental conditions and for different purposes, including invasive species detection and land cover mapping. The results highlight the added value of our novel comparison of image segmentation and classification algorithms. Overall classification performances (assessed through cross-validation with the Kappa index) ranged from 0.85 to 1.00. Pilot-tests show that our GA-based approach is capable of providing sound results for optimizing the parameters of different segmentation algorithms, with benefits for classification accuracy and for comparison across techniques. We also verified that no particular combination of an image segmentation and a classification algorithm is suited for all the tasks/objectives. Consequently, it is crucial to compare and optimize available methods to understand which one is more suited for a certain objective.Our approach allows a closer integration between the segmentation and classification stages, which is of high importance for GEOBIA workflows. The results from our tests confirm that this integration has benefits for comparing and optimizing both processes. We discuss some limitations of the SegOptim approach (and potential solutions) as well as a future roadmap to expand its current functionalities.  相似文献   

鄂尔多斯盆地东部二叠系山西组页岩气成藏条件与有利区筛选          下载免费PDF全文

兰朝利  郭伟  王奇  张欣 《地质学报》2016,90(1):177-188

鄂尔多斯盆地东部榆106井区山西组页岩沉积在三角洲平原分流河道间沼泽、天然堤、决口扇远端与洼地环境。根据有机地球化学、物性、含气性、岩性实验分析,结合钻井、录井、测井资料与沉积相研究,开展了该区山西组页岩气成藏条件与有利区分布研究。山西组页岩有机碳含量较高,山二段平均5.28%,山一段平均3.02%,有机质类型以Ⅱ_2、Ⅲ型干酪根为主,有机质成熟度较高,R_o平均1.89%,生气条件优越。页岩孔隙度平均1.7%,渗透率平均0.0415×10~(-3)μm~2,平均含气量0.64 m~3/t,页岩单层厚度小,垂向上普遍与致密砂岩、煤层组成互层,累计厚度较大(平均达75 m),页岩渗透性较好而储集性能稍差。页岩脆性矿物含量平均49.9%,黏土矿物含量平均50.1%,黏土矿物含量较高,资源丰度普遍较低,但是页岩埋深小于3000 m,试气产能较高(0.64×10~4m~3/d),商业开发潜力较好。山二段有机质含量比山一段更高,含气性比山一段更好。主要基于页岩厚度与沉积相展布预测的山二段页岩气有利区呈北东向、南北向条带状展布,受分流河道间沼泽微相的控制。  相似文献   

下古生界海相页岩TOC测井预测模型优选及应用——以川南长宁地区为例     

陈轶林  孔令明  梁浩然 《地质论评》2022,68(6):2022112013-2022112013

有机碳含量（TOC）是页岩气资源评价与预测选区的关键指标之一,测井预测是实现单井TOC连续识别的重要手段,本次研究拟揭示各类预测方法在下古生界海相页岩中的预测效果。本次以川南长宁地区龙一段黑色页岩为对象,尝试采用多类预测方法（ΔLogR法及其改进方法、多元线性回归法与神经网络法）与不同的研究尺度（全段或分层）建立TOC测井预测模型,并对不同方法的预测效果进行深入探讨。研究表明,各方法预测效果差异较大,适用性各不相同。整体而言,多元线性回归法与BP神经网络法在研究区的预测效果均优于ΔLogR法及其改进方法。笔者等研究提出多元线性回归法对研究区TOC高值段的预测效果更佳,而神经网络法对TOC低值段的预测精度更高。本次研究根据龙一段各亚段有机质分布特征与测井响应特征的差异,提出通过“精细分层与最优方法匹配”的方式,因地制宜地选择相应的方法进行TOC测井预测。针对龙一1a-c与龙一1d-龙一2,分别采用多元线性回归法与BP神经网络法进行分层精细建模,并获得了最佳的预测效果,不仅预测精度较高,而且相对误差较小,绝大部分样品相对误差不超过20%。  相似文献