基于五种油源的中重度风化溢油的常规鉴定指标的应用效果评估

本文利用采集于我国三大油田的五种原油样品开展了长达210天的溢油风化模拟实验,并依据相对偏差和重复性限数学分析法,进行溢油风化过程分析和诊断比值应用效果评估。研究结果表明:经过210天的风化,溢油鉴定诊断比值发生明显改变;其中来源于萜烷、甾烷和多环芳烃的诊断比值变化率要远低于正构烷烃,可用于中长期风化溢油鉴定。此外,研究发现,在这些有效诊断比值中有4个变化率较小,相对偏差低于5%,保持了较好的稳定性,更适合于重度风化溢油鉴定。     

典型矿区尾矿库特征污染物识别研究

通过对典型尾矿库土壤样品分析,利用重金属超标率、植物体BCF、污染因子的权重值筛选等通进行典型污染物识别,为开展尾矿库污染治理工作提供依据。     

基于岩石图像深度学习的岩性自动识别与分类方法

岩石岩性的识别与分类对于地质分析极为重要,采用机器学习的方法建立识别模型进行自动分类是一条新的途径。基于Inception-v3深度卷积神经网络模型,建立了岩石图像集分析的深度学习迁移模型,运用迁移学习方法实现了岩石岩性的自动识别与分类。采用此方法对所采集的173张花岗岩图像、152张千枚岩图像和246张角砾岩图像进行了学习和识别分类研究,通过训练学习建立岩石图像深度学习迁移模型,并分别采用训练集和测试集中的岩石图像对模型进行了检验分析。对于训练集中的岩石图像,每组岩石分别用3张图像测试,三种岩石的岩性分类均正确,且分类概率值均达到90%以上,显示了模型良好的鲁棒性;对于测试集中的岩石图像,每组岩石分别采用9张图像进行识别分析,三种岩石的岩性分类均正确,并且千枚岩组图像分类概率均高于90%,但是花岗岩组2张图像和角砾岩组的1张图像分类概率值不足70%,概率值较其他岩石图像低,推测其原因是训练集中相同模式的岩石图像较少,导致模型的泛化能力减小。为了提高识别精确度,对准确率较低的岩石图像进行截取,分别取其中的3张图像加入训练集进行再训练,增加与测试图像具有相同模式的训练样本;在新的模型中,对3张图像进行二次检验,测试概率值均达到85%以上,说明在数据足够的状况下模型具有良好的学习能力。与传统的机器学习方法相比,所提出的岩石图像深度学习方法具有以下优点:第一,模型通过搜索图像像素点提取物体特征,不需要手动提取待分类物体特征;第二,对于图像像素大小,成像距离及光照要求低;第三,采用适当的训练集可获得较好的识别分类效果,并具有良好鲁棒性和泛化能力。     

黄骅坳陷古生界潜山储层沥青成因判识

正黄骅坳陷古生界潜山是大港油田重要的油气勘探领域,近年来,先后在王官屯、乌马营潜山古生界发现油气流,且均为石炭—二叠系煤系源岩供烃成藏,如王官屯潜山王古1井二叠系石盒子组发现天然气,同位素特征判断为煤成天然气。这些发现拓展了古生界潜山的勘探领域,表明了石炭—二叠系煤系烃源岩的油气充注范围不仅仅局限于奥陶系碳酸盐岩储层,石炭—二叠系的碎屑岩储层也可以充注成藏,证实了古生界潜山具有更大的油气勘探价值。随着勘探进程与理论认识的不断深化,     

夏季亚洲阻塞高压识别及其对中国东北气候异常的可能影响:不同再分析资料对比

基于阻塞高压（阻高）客观识别方法,利用1979-2016年夏季（6-8月）NCEP-Ⅰ、NCEP-Ⅱ逐日再分析资料和ERA-interim逐6 h再分析资料对识别结果进行对比分析;并以D类（130°-160°E）阻高为例,讨论其对6月中国东北地区气候的可能影响。结果表明:NCEP-Ⅰ和NCEP-Ⅱ再分析资料对阻高活动天数、发生频次及年代际变化的识别结果差异较小,而ERA-interim与前两种资料的结果差别较大。3种再分析资料下,夏季各类阻高活动天数均与500 hPa高度场存在相应的显著相关区,且形态相近。但前两种资料对于各类阻高的表征结果较为一致,而ERA-interim再分析资料对各类阻高面积和范围的表征偏小。6月D类阻高活动日数与东北地区气温和降水关系密切,D类阻高活跃年,大气环流以经向型为主,东北地区低层低温、暖平流,高层高温、冷平流的结构指示大气层结不稳定,且东北上空为异常低压环流控制,上升气流较强,有利于6月东北地区出现低温多雨天气。鄂霍次克海地区是6月罗斯贝波的重要来源地之一,而6月D类阻高的形成可能与海-陆温差有关。      

基于不同邻近标签数选择的LANDMARC定位算法研究

对于基于射频识别的LANDMARC室内定位算法而言,该系统采用"k近邻"算法,通过选取k个与待测标签相邻且符合特定条件的参考标签,最后根据这些标签的位置结合权值估算出待测标签的坐标信息.但是在实际的操作过程中,最近邻参考标签数的选取无指导原则,具有一定的盲目性.针对这一问题,通过选取与待定位参考点最近邻的参考节点作为未知点,通过搜索法获得最优的邻近标签数k,在随后的未知节点的定位过程中,选取k个邻近标签进行定位.经过多次实验,最终得出结论,使用改进后的LANDMARC算法的性能要优于原来经典的LANDMARC算法,精确度提升了10%左右,同时避免了k值选取的盲目性.     

Bed material texture in low order streams on the Queen Charlotte Islands,British Columbia

Low order channels comprise a large proportion of the links of every drainage basin, and are often at the centre of land management concerns. They exhibit hydrological and geomorphological characteristics atypical of higher order links. This paper examines the nature and causes of variations in the bed material texture of two streams on the Queen Charlotte Islands, British Columbia. The extant, functional exponential model is found to be inadequate for explaining observed changes in grain size parameters with distance downstream. Recurrent disruption of sediment transport by large organic debris jams, and the sporadic contamination of the fluvial sediment population by colluvial inputs, preclude the development of longitudinal structure. Rather, grain size varies erratically over short distances. A stochastic model best describes the observed variations, and should be adopted as an alternative to the exponential model in low order links. Characteristic variances are controlled by the degree of hillslope-channel coupling, and the extent and characteristics of non-alluvial storage mechanisms.     

MODELLING UNKNOWN STRUCTURAL SYSTEMS THROUGH THE USE OF NEURAL NETWORKS

This paper explores the potential of using neural networks to identify the internal forces of typical systems encountered in the field of earthquake engineering and structural dynamics. After formulating the identification task as a neural network learning procedure, the method is applied to a representative chain-like system under deterministic and stochastic excitations. The neural network based identification method provides very good results for general classes of multi-degree-of-freedom structural systems. The range of validity of the approach is demonstrated, and some application issues are discussed for (a) partially known multi-degree-of-freedom systems and (b) completely unknown systems.     

THE THEORY OF RESPONSE ANALYSIS OF FUZZY STOCHASTIC DYNAMICAL SYSTEMS WITH A SINGLE DEGREE OF FREEDOM

Most real-life structural/mechanical systems have complex geometrical and material properties and operate under complex fuzzy environmental conditions. These systems are certainly subjected to fuzzy random excitations induced by the environment. For an analytical treatment of such a system subjected to fuzzy random excitations, it becomes necessary to establish the general theory of dynamic response of a system to fuzzy random excitations. In this paper, the theory of response, fuzzy mean response and fuzzy covariance response of a single-degree-of-freedom (sdf) system to fuzzy random excitations in the time domain and frequency domain is put forward. The theory of response analysis of an sdf system to both stationary and non-stationary fuzzy random excitations in the time domain and frequency domain is established. Two examples are considered in order to demonstrate the rationality and validity of the theory, and the models of stationary filtered white noise and non-stationary filtered white noise fuzzy stochastic processes of the earthquake ground motion are set up. Methods of analysis for fuzzy random seismic response of sdf systems are put forward using the principles of response analysis of an sdf fuzzy random dynamic system.     

PARAMETER AND VARIANCE ESTIMATION IN GEOTECHNICAL BACKANALYSIS USING PRIOR INFORMATION

A probabilistic framework to perform inverse analysis of geotechnical problems is presented. The formulation allows the incorporation of existing prior information on the parameters in a consistent way. The method is based on the maximum likelihood approach that allows a straightforward introduction of the error structure of field measurements and prior information. The difficulty of ascribing definite values to the uncertainties associated with the various types of observations is overcome by including the corresponding variances in the set of parameters to be identified. The inverse analysis results in a minimization problem that is solved by coupling the optimization technique to the finite element method. Two examples are presented to illustrate the performance of the method. The first one corresponds to a synthetic case simulating the excavation of a tunnel. Young's modulus, K₀ value and measurements variances are identified. The second case concerns the excavation of a large underground cavern in which again Young's modulus and K₀ are identified. It is shown that introduction of prior information permits the estimation of parameters more consistent with all available informations that include not only monitored displacements but also results from in situ tests carried out during the site investigation stage.