Fracture Characterization Using Borehole Radar: Numerical Modeling

Forward modeling of borehole radar data for a series of synthetic discrete-fracture network (DFN) models provides a conceptual framework for interpreting experimental field data at fractured rock sites. A finite-difference time-domain (FDTD) radar wave propagation model was developed for this purpose. Synthetic examples demonstrate the utility of single-hole reflection-mode and cross-hole transmission-mode borehole radar for (1) identification of fracture location and orientation, and (2) identification of fracture pore-fluid properties, which might change as a result of tracer tests or flooding exercises in support of resource development or site remediation. A two-dimensional, synthetic DFN was generated statistically based on hypothetical distributions of fracture length, orientation, aperture, permeability, and inter-connectivity. The DFN includes a zone of permeable fractures embedded within a network of lower-permeability fractures and a low-permeability rock matrix. We modeled the unconnected and non-permeable fractures as being filled with freshwater. To simulate tracer experiments, contaminant releases, or engineered-remediation processes, we considered alternately the inter-connected, permeable fractures to be filled with freshwater air, or saline water (tracer). Synthetic radar data sets for both single-hole reflection and cross-hole transmission modes were generated. The features in synthetic radargrams were then examined and compared to the DFN model to evaluate the likelihood of identifying fracture location, orientation, and pore fluid in field situations. This comparison demonstrates that (1) the replacement of freshwater with saline water in permeable fractures generally increases the amplitude of reflections from permeable, connected fractures; and (2) in general, radar reflection-mode data contains more information about fracture properties than transmission-mode data.     

RBF神经网络在土壤重金属污染评价中的应用

运用MATLAB软件的K均值聚类算法结合神经网络工具箱,通过建立RBF神经网络对郑汴路圃田—杏花营段路旁土壤进行了评价。结果表明,土壤重金属积累峰值污染峰值多出现在距路基50m范围以内,并随着距路基距离的增加,污染程度逐渐下降。风力因素和邻近铁路对路段两侧土壤重金属污染影响较大,且公路运营时间越早,对两侧土壤污染越严重。杏花营断面土壤质量以Ⅱ级为主,北侧断面土壤质量多为Ⅲ级。圃田断面各样点综合评价结果均为Ⅱ级,整体质量优于杏花营断面。RBF神经网络评价方法具有计算速度快,评价结果客观等优点。     

基于综合主成分及径向基网络的环境质量评价

运用综合主成分分析对监测数据进行处理,集成径向基函数人工神经网络(RBF-ANN),参考国家环境质量评价标准设定RBF的学习样本,从而构建区域环境质量综合评价模型,对安徽省合肥市新站综合开发试验区进行环境质量综合评价。实例分析结果表明,运用综合主成分法可以精准的统计出一个区域的环境综合数据,而且在matlab环境下运用RBF-ANN模型既可以准确,客观的评定环境质量的等级,又可以表现其环境污染的具体程度,能在同一评价等级内对不同环境质量的评价对象进行更加细微的污染程度的比较。结果表明,合肥市新站综合开发试验区环境综合质量介于轻度污染和中度污染的标准极限值之间,属于中度污染。     

Adaptive neuro fuzzy inference system for classification of water quality status

An adaptive neuro fuzzy inference system was used for classifying water quality status of river. It applied several physical and inorganic chemical indicators including dissolved oxygen, chemical oxygen demand, and ammonia-nitrogen. A data set (nine weeks, total 845 observations) was collected from 100 monitoring stations in all major river basins in China and used for training and validating the model. Up to 89.59% of the data could be correctly classified using this model. Such performance was more competitive when compared with artificial neural networks. It is applicable in evaluation and classification of water quality status.     

基于因子-神经网络预测南渡江海口段水质状况

针对海南省南渡江日益严重的水污染现状,根据南渡江海口段2001年-2006年的监测资料,应用因子分析法找出影响水质的主要污染指标,建立了基于BP神经网络的主要污染指标预测模型,并对其进行训练检验,结果证明该模型预测精度满足要求,再利用训练、检验后的模型对近几年主要污染因子进行预测,发现了变化规律,最后提出南渡江污染防治措施。     

城市电磁环境建模及网络监测系统的构想

随着科技的发展和城市建设速度的加快,电磁污染的问题已经越来越受到各界的重视与关注。该文首先介绍了时域有限差分法的基本概念,并建立了基于时域有限差分法计算的数学模型,然后针对城市电磁环境的特点,从电磁污染产生的根源和电磁干扰的种类入手进行研究,对电磁污染的监测方式进行了讨论,在此基础上,给出了一种基于GPS定位技术和GPRS无线传输技术的城市电磁环境网络监测系统的构想,并对系统结构及其所实现的功能进行了讨论。     

江阴市小城镇污水集中处理的研究与思考

随着城镇化进程加快,经济快速发展的同时环境污染问题随之出现,主要表现在小城镇污水处理率较低,各地区发展不平衡等方面。通过几年来的摸索与实践,江阴市小城镇污水集中处理已成一定规模,也凸现出一些问题和不足之处。通过对江阴市小城镇污水集中处理水质特点及工艺的研究,结合江阴市污水处理厂运行情况进行问题分析,提出相应的对策及思考。通过加强城镇污水管网建设,运用成熟经济高效的处理工艺,引进先进管理制度,促进环保部门的监管工作,达到改善小城镇污水处理效率的目的。     

Hydraulic model for multi-sources reclaimed water pipe network based on EPANET and its applications in Beijing, China

Water shortage is one of the major water related problems for many cities in the world. The planning for utilization of reclaimed water has been or would be drafted in these cities. For using the reclaimed water soundly, Beijing planned to build a large scale reclaimed water pipe networks with multi-sources. In order to support the plan, the integrated hydraulic model of planning pipe network was developed based on EPANET supported by geographic information system (GIS). The complicated pipe network was divided into four weak conjunction subzones according to the distribution of reclaimed water plants and the elevation. It could provide a better solution for the problem of overhigh pressure in several regions of the network. Through the scenarios analysis in different subzones, some of the initial diameter of pipes in the network was adjusted. At last the pipe network planning scheme of reclaimed water was proposed. The proposed planning scheme could reach the balances between reclaimed water requirements and reclaimed water supplies, and provided a scientific basis for the reclaimed water utilization in Beijing. Now the scheme had been adopted by Beijing municipal government.     

基于熵权的属性识别模型在城市轨道线网方案评价中的应用

城市轨道线网规划对优化城市社会、经济、生态环境,实现城市生态环境可持续发展有着至关重要的作用。文章通过熵值法与属性识别模型相结合,建立了基于熵权的属性识别模型,用该方法对城市轨道线网方案进行综合评价,并结合西安市城市轨道交通线网规划中的4个预选方案进行了评价,推选出最优方案。结果表明,该评价方法结果合理,决策科学,具有较好的应用前景。     

BP模型在水泥行业清洁生产评价案例中的应用

基于BP神经网络的原理与算法,建立清洁生产多指标综合评价模型,采用隶属度函数对评价指标进行描述。以避免评价过程中的人为失误,达到客观评价的目的。用该模型对重庆某水泥厂进行清洁生产评价,结果表明：该评价方法操作性强、准确性高、可为实现清洁生产的客观评价提供科学依据和技术方法。