Textural segmentation of digital rock images into bedding units using texture energy and cluster labels

The texture of digital rock images, as recorded, for instance, with borehole imaging devices, is shown to reflect different bedding types. Textural segmentation of borehole images, therefore, subdivides the recorded sequence into bedding units. We show that a textural segmentation algorithm based on the concept of texture energy achieves good results when compared with synthetic as well as real data in which petroleum geologists have performed zonations on cores. Texture energy involves filtering of the original image with a set of texture sensitive masks. The filtering is done as a finite convolution over the size of the masks. On the resulting images the variance is computed over a relatively large sliding window, which, in its practical implementation, covers the full width of the image. The resulting nine one-dimensional curves are then clustered hierarchically into a user-determined number of image texture or lithological bedding classes. Principal component analysis previous to clustering can be used to reduce redundancy in the data. A recurring and relatively ill-defined problem in this field are macro-textures, i.e., the cyclic interbedding of two or more bedding types. We show that sliding Fourier transforms and variable mask scale can successfully address the zonation of macro-textures. In general, the method gives best results with mask sizes equivalent to 2–4 centimeters, reflecting the length scale at which the investigated geological bedding seems to have its highest variation.     

Statistical analysis for the hydrogeological evaluation of the fracture networks in hard rocks

The hydrogeological effectiveness of fracture sets is determined and evaluated by the fuzzy c-mean and hierarchical clustering. These cluster analyses combine the geological spatial attributes and the hydraulic relevant attributes of fractures. Based on the results of the clustering the fracture set volumes are estimated.     

T -distributed Random Fields: A Parametric Model for Heavy-tailedWell-log Data1

Histograms of observations from spatial phenomena are often found to be more heavy-tailed than Gaussian distributions, which makes the Gaussian random field model unsuited. A T-distributed random field model with heavy-tailed marginal probability density functions is defined. The model is a generalization of the familiar Student-T distribution, and it may be given a Bayesian interpretation. The increased variability appears cross-realizations, contrary to in-realizations, since all realizations are Gaussian-like with varying variance between realizations. The T-distributed random field model is analytically tractable and the conditional model is developed, which provides algorithms for conditional simulation and prediction, so-called T-kriging. The model compares favourably with most previously defined random field models. The Gaussian random field model appears as a special, limiting case of the T-distributed random field model. The model is particularly useful whenever multiple, sparsely sampled realizations of the random field are available, and is clearly favourable to the Gaussian model in this case. The properties of the T-distributed random field model is demonstrated on well log observations from the Gullfaks field in the North Sea. The predictions correspond to traditional kriging predictions, while the associated prediction variances are more representative, as they are layer specific and include uncertainty caused by using variance estimates.     

层次化网络SCADA通讯模型及其应用

为在大型网络分布式SCADA（Super Control and Data Acquisition）应用系统中解决诸如电力、通信、石油、化工、制造业等行业或领域，在过程、流程、状态方面的数据监视、数据控制、数据采集与数据管理的高效、实时问题，解决各级子系统之间人工逐级汇接的问题，提高应用系统的稳定性、可维护性，这里提出了一种层次化网络SCADA通讯模型。该模型是通过在层次化网络SCADA模型中，引入基于TCP／IP协议的客户机／服务器查询方式数据通讯技术和点对点主动数据传递技术而建立的。根据该模型所设计的产品，经过生产实践证明，应用该模型可以较方便地构造分布在不同区域计算机上的SCADA系统，进行高效的实时通信。该模型为具有多层结构的大型分布式网络化集中SCADA系统的设计、开发，探索出了一条十分有效的新路径。     

Using Spatial Statistics to Examine the Pattern of Urban Land Development in Halifax–Dartmouth

This article examines the locational patterns of population and employment and the resultant changes in urban form in the Halifax–Dartmouth region between 1970 and 1996. The article employs the univariate and bivariate K function to measure spatial dependence or clustering within and between the classes of residential and commercial land parcels. The results of the K function estimates suggest that residential land parcels cluster together, commercial land parcels cluster together, and residential and commercial land parcels have become more clustered over time. Evidence of clustering provides insight into the changing urban form of the region and possible multinucleation.     

水文地质分区中的模糊聚类系数法

利用隶属函数构造模聚类系数，给出了一种新的分区方法。运用此方法对水文地质进行分区可大大降低了计算量，并且避免了一些主观因素，该方法的关键是确定隶属函数，可以通过计算机的模拟计算解决。     

混合二维泊松过程的分解算法及其在提取地震丛集模式中的应用.

将一定范围内的地震数据假设为背景地震和丛集地震的叠加，并同时认为背景地震和丛集地震分别满足不同参数的二维泊松过程．通过引入N阶距离概念，将叠加的二维泊松过程转化为一维的混合密度函数，在对距离阶数进行选择的基础上，最终采用遗传算法进行混合密度分解，以达到提取地震丛集模式的效果．文中将该算法应用于我国西南地区松潘及龙陵主震前丛集地震的提取，并与C值的时间扫描结合，深化了这两次大地震前地震活动图象的认识．      

The algorithm of decomposing superim-posed 2-D Poisson processes and its applica-tion to the extracting earthquake clustering pattern

IntroductionClusteringearthquakesareusuallyconsideredasomensofstrongearthquakesorasignaloftectonicmovement.Thus,theyarenotonlyoneoftheprimaryevidencestopredictearthquakesbutalsoasignificantindicatortorecognizetectonicmovement(MEI,etal,1993;EarthquakePre-dictionandPreventionDepartmentofChinaSeismologicalBureau,1998).Ongeneralconditions,webelievethatclusteringearthquakesexistrelativelytobackgroundearthquakes,howtoeffectivelyseparateonefromtheotherbecomesthekeypointofextractingtheclusteringea…     

Spatiotemporal variability in hydrochemistry of shallow groundwater in a small pre‐alpine catchment: The importance of landscape elements

Topography and landscape characteristics affect the storage and release of water and, thus, groundwater dynamics and chemistry. Quantification of catchment scale variability in groundwater chemistry and groundwater dynamics may therefore help to delineate different groundwater types and improve our understanding of which parts of the catchment contribute to streamflow. We sampled shallow groundwater from 34 to 47 wells and streamflow at seven locations in a 20‐ha steep mountainous catchment in the Swiss pre‐Alps, during nine baseflow snapshot campaigns. The spatial variability in electrical conductivity, stable water isotopic composition, and major and trace ion concentrations was large and for almost all parameters larger than the temporal variability. Concentrations of copper, zinc, and lead were highest at sites that were relatively dry, whereas concentrations of manganese and iron were highest at sites that had persistent shallow groundwater levels. The major cation and anion concentrations were only weakly correlated to individual topographic or hydrodynamic characteristics. However, we could distinguish four shallow groundwater types based on differences from the catchment average concentrations: riparian zone‐like groundwater, hillslopes and areas with small upslope contributing areas, deeper groundwater, and sites characterized by high magnesium and sulfate concentrations that likely reflect different bedrock material. Baseflow was not an equal mixture of the different groundwater types. For the majority of the campaigns, baseflow chemistry most strongly resembled riparian‐like groundwater for all but one subcatchment. However, the similarity to the hillslope‐type groundwater was larger shortly after snowmelt, reflecting differences in hydrologic connectivity. We expect that similar groundwater types can be found in other catchments with steep hillslopes and wet areas with shallow groundwater levels and recommend sampling of groundwater from all landscape elements to understand groundwater chemistry and groundwater contributions to streamflow.     

空间层次聚类显著性判别的重排检验方法

同时顾及空间邻近与专题属性相似的空间层次聚类是挖掘空间分布模式的一种有效手段。空间层次聚类方法虽然可以获得多层次的聚集结构,但聚类结果显著性的统计判别依然是一个尚未解决的难题。为此,本文提出了一种空间层次聚类结果显著性的统计判别方法,用于确定空间层次聚类的停止准则,减少聚类过程对参数设置的依赖。通过试验分析与比较发现,该方法能够有效判别空间层次聚类结果的显著性和确定层次聚类合并过程的停止条件,同时具有很好的抗噪性,避免随机结构的干扰。