Signatures in flowing fluid electric conductivity logs

Flowing fluid electric conductivity logging provides a means to determine hydrologic properties of fractures, fracture zones, or other permeable layers intersecting a borehole in saturated rock. The method involves analyzing the time-evolution of fluid electric conductivity (FEC) logs obtained while the well is being pumped and yields information on the location, hydraulic transmissivity, and salinity of permeable layers. The original analysis method was restricted to the case in which flows from the permeable layers or fractures were directed into the borehole (inflow). Recently, the method was adapted to permit treatment of both inflow and outflow, including analysis of natural regional flow in the permeable layer. A numerical model simulates flow and transport in the wellbore during flowing FEC logging, and fracture properties are determined by optimizing the match between simulation results and observed FEC logs. This can be a laborious trial-and-error procedure, especially when both inflow and outflow points are present. Improved analyses methods are needed. One possible tactic would be to develop an automated inverse method, but this paper takes a more elementary approach and focuses on identifying the signatures that various inflow and outflow features create in flowing FEC logs. The physical insight obtained provides a basis for more efficient analysis of these logs, both for the present trial and error approach and for a potential future automated inverse approach. Inflow points produce distinctive signatures in the FEC logs themselves, enabling the determination of location, inflow rate, and ion concentration. Identifying outflow locations and flow rates typically requires a more complicated integral method, which is also presented in this paper.     

Retrofit-reinforcement of cohesive soil slopes using high strength geotextiles with drainage capability

A series of geotechnical centrifuge physical modeling tests were performed to assess the potential use of a new cost-effective mechanically stabilized earth system for retrofitting marginally stable cohesive slopes. The proposed system utilizes the dual functions of reinforcement and drainage by directly inserting high strength non-woven geotextile strips into slopes, with little or no excavation required behind the slope face. The system significantly increases the factor of safety of potentially unstable cohesive slopes, and can be constructed at less expense and more rapidly than conventional mechanically stabilized earth systems.     

形变数字化观测曲线畸变与处理——兼论资料预处理中遇到的一些问题

数字化形变台站日常下载、处理直至管理数据,都应用《数字化通讯控制软件》、《形变前兆台站(网)数据处理系统》及《形变前兆数据库服务软件(台站版)》等三个软件交互进行,在实际使用过程中会遇到一些棘手的问题。本文结合我台的观测工作的实践,总结经验,以供台站同行共享。     

数字社区数据标准研究

当前数字社区建设缺乏统一的智能化综合管理平台以及相应的各项数据标准，使得数字社区难以真正实现统一的数字化管理。该文参考国内外相应数据标准，研究数字社区的标准体系结构。针对现有标准体系缺乏相关数据标准的现状，提出数字社区系列数据标准，包括数据接口层标准、数据分类编码标准、数据模型、数据字典和元数据标准。阐述系列标准中各标准间的相互关系以及各标准所包含的具体内容。     

空间运动对象与时空数据类型研究

当前时空数据模型多以描述空间实体的离散变化为主。该文中对空间运动对象在抽象层次的无限连续空间、离散层次的有限离散空间上的数据类型进行分析和定义，将其分别划分为时间类型、空间类型和时态类型来研究，并提出支持空间运动对象的表示方法和操作方式。该方法既能表示空间实体的连续运动，也能表示其离散变化，为空间运动对象时空数据模型的建立奠定了基础。     

基于A- CGAN的深反射地震数据随机噪声压制方法研究

基于深度学习的地震数据噪声压制方法是当前地震数据去噪处理的重要方向。深度学习方法突破了传统滤波处理的局限,在对常规地震数据的噪声压制中表现出效率高、信噪分离效果好的特点。但针对深部弱有效反射数据,当前的深度学习方法特征提取能力有限,难以取得较好的去噪效果。笔者等结合深反射地震数据特点,针对当前深度学习噪声压制方法在特征提取及对数据集依赖上的局限,提出了基于注意力循环生成对抗网络(Attention Cycle- Consistent Generative Adversarial Networks,A- CGAN)的深反射地震数据随机噪声压制方法。借助循环一致生成对抗网络(Cycle- Consistent Generative Adversarial Networks,Cycle- GAN)的域映射思想,降低对数据集的要求。为了构建适用于深反射地震数据的去噪网络,从3个方面对Cycle- GAN进行改进：在Cycle- GAN的生成器（去噪器）中加入残差结构和注意力机制,用于加深网络深度和提高其特征提取能力;在Cycle- GAN的鉴别器中使用块判决,提高鉴别精度和准确度;在损失函数部分加入感知一致性损失函数,提升网络模型恢复纹理细节信息的能力。通过合成地震数据和实际深反射地震数据测试,验证了优化算法的有效性,体现了良好的应用价值。