地震作用下桩锚组合结构加固边坡稳定性分析----灾后重建专刊

对于一些性质复杂,滑坡推力巨大的边（滑）坡,单一的抗滑结构对其整治已显得无能为力,于是各种组合抗滑结构应运而生,然而目前对采用组合防治结构加固边坡稳定性的研究又十分稀缺。因此,本文以桩锚组合结构加固边坡为例,运用极限分析上限定理,推导出了地震作用下桩锚组合结构加固边坡中抗滑桩所需提供抗力的计算公式以及该加固边坡的屈服加速度计算公式,并提出了永久位移的预测方法,为地震带边坡防治结构的设计提供理论依据。通过对桩锚组合结构加固的实例边坡的计算分析,得到了在确定的锚索布置条件下,抗滑桩所需提供抗力和边坡屈服加速度与地震系数、抗滑桩布置位置、边坡特性和坡体材料特性等因素相关关系的一系列研究结果,揭示了地震作用下桩锚组合结构加固边坡的稳定性。     

改进纤维单元及在配筋墙体抗震研究中的应用

为了研究配筋砌块砌体剪力墙的抗震性能,在6个配筋砌块砌体剪力墙物理试验的基础上,研究并提出了一种考虑弹塑性剪切变形的纤维模型.通过按照试验条件所进行的滞回性能数值模拟与物理试验结果的对比,表明应用所改进的纤维模型模拟计算能较好地反应配筋砌块砌体剪力墙滞回性能.分析也表明,在一定条件下普通的纤维模型对于配筋砌块砌体剪力墙滞回性能分析仍是适用的,但应用本文所研究的纤维单元计算结果更好.在此基础上,利用均匀设计法安排数值试验,回归给出了一字型配筋砌块短肢砌体剪力墙滞回性能特征参数的经验公式,建议了此类配筋砌块砌体剪力墙结构弹塑性地震反应分析的滞回规律模型.     

高分辨力地震是提高中国南方碳酸盐岩地震勘探效能的基础——一个由失败到成功的史例

南方碳酸盐岩复杂地区的地震工作经历了长期艰难曲折的道路,其中异常判别的经验教训极为深刻。本文从地震异常分析入手,揭示其实质,寻求问题的关键,探讨解决的办法,介绍高分辨率地震方法技术,展示高分辨率地震新成果,以得出有意义的结论和建议。     

实腹式巨型框架与空腹式巨型框架抗震受力性能对比研究

运用有限元分析软件SAP2000对实腹式巨型框架与空腹式巨型框架的抗震受力性能进行地震反应的弹性时程分析,比较实腹式巨型框架与空腹式巨型框架抗震受力性能的差别;为简便起见,本文只比较实腹式巨型框架梁与空腹式巨型框架梁的不同抗震特性.分析表明,空腹式巨型结构在受力性能上要优于实腹式巨型结构.     

潜江凹陷建新地区地震储层预测研究

以古地貌分析、砂体展布特征为指导,开展了潜江凹陷建新地区多种技术手段的地震储层预测研究。结果表明,振幅类与波形类属性是该区的敏感性参数,并且通过本地区的岩石物理分析,认为波阻抗也能较好地区分储层;在刻画储层展布形态的同时与构造相叠合落实了多个构造-岩性圈闭,进一步明确该区构造-岩性油藏的勘探潜力。     

A study of suspended roofs for near-source seismic motions

The non-linear behavior of multi-suspended roof systems for seismic loads is studied. The study is based on a formulation that can be easily employed for a preliminary design of multi-suspended roofs subjected to seismic loads. Specifically, applying Lagrange’s equations, the corresponding set of equations of motion for discrete models of multiple suspension roofs is obtained and numerical integration of the equations of motion is performed via the Runge–Kutta scheme. For representative realistic combinations of geometric, stiffness and damping parameters, a non-linear analysis is employed to study the behavior of suspended roofs for near-source and far-field seismic motions. The analysis demonstrates that: (i) code-specified design loads could dramatically underestimate the response of suspended roofs subjected to near-source ground motions and (ii) flexible roofing systems are greatly affected by near-source ground motions, a behavior that is not observed for stiff systems.     

中原探区地震勘探数据可视化共享系统研究与实现

随着油田勘探开发程度的不断提高,积累了大量的地震成果数据资料,为了有效地管理和应用这些资料,提高勘探开发研究工作效率,进行了地震成果数据可视化应用系统研发。应用现代信息技术,整合离散的地震成果数据资源,构建面向勘探开发研究人员的网络化、可视化的管理与共享服务体系,实现数据采集、加工、保存的标准化、规范化。分析了地震成果数据分类和管理关键问题,介绍了地震成果数据共享应用系统的技术思路、建设目标、系统安全策略和系统架构。最后阐述了在研发建设过程中所采用的几项关键技术和具体实现效果。     

Petrophysical data prediction from seismic attributes using committee fuzzy inference system

This study presents an intelligent model based on fuzzy systems for making a quantitative formulation between seismic attributes and petrophysical data. The proposed methodology comprises two major steps. Firstly, the petrophysical data, including water saturation (S_w) and porosity, are predicted from seismic attributes using various fuzzy inference systems (FISs), including Sugeno (SFIS), Mamdani (MFIS) and Larsen (LFIS). Secondly, a committee fuzzy inference system (CFIS) is constructed using a hybrid genetic algorithms-pattern search (GA-PS) technique. The inputs of the CFIS model are the outputs and averages of the FIS petrophysical data. The methodology is illustrated using 3D seismic and petrophysical data of 11 wells of an Iranian offshore oil field in the Persian Gulf. The performance of the CFIS model is compared with a probabilistic neural network (PNN). The results show that the CFIS method performed better than neural network, the best individual fuzzy model and a simple averaging method.     

基于GPU的地震剖面图形快速绘制算法

地震剖面图的绘制是二维地震数据可视化的基础。目前基于通用绘制引擎的地震剖面图绘制是在CPU上实现的,随着地震数据规模越来越大,传统绘制方法的绘制效率已经不能达到交互效率的要求,因此提出了一种地震剖面图快速绘制算法。该算法将地震数据的绘制和GPGPU技术相结合,利用GPU强大的并行计算能力实现图形光栅化处理。实验表明,在保证绘制效果的前提下,该方法极大地提高了绘制效率。     

3D porosity prediction from seismic inversion and neural networks

In this work, we address the problem of transforming seismic reflection data into an intrinsic rock property model. Specifically, we present an application of a methodology that allows interpreters to obtain effective porosity 3D maps from post-stack 3D seismic amplitude data, using measured density and sonic well log data as constraints. In this methodology, a 3D acoustic impedance model is calculated from seismic reflection amplitudes by applying an L₁-norm sparse-spike inversion algorithm in the time domain, followed by a recursive inversion performed in the frequency domain. A 3D low-frequency impedance model is estimated by kriging interpolation of impedance values calculated from well log data. This low-frequency model is added to the inversion result which otherwise provides only a relative numerical scale. To convert acoustic impedance into a single reservoir property, a feed-forward Neural Network (NN) is trained, validated and tested using gamma-ray and acoustic impedance values observed at the well log positions as input and effective porosity values as target. The trained NN is then applied for the whole reservoir volume in order to obtain a 3D effective porosity model. While the particular conclusions drawn from the results obtained in this work cannot be generalized, such results suggest that this workflow can be applied successfully as an aid in reservoir characterization, especially when there is a strong non-linear relationship between effective porosity and acoustic impedance.