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.     

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。     

基于DBSCAN算法的孔深测量与地层反演理论研究

论文针对地质勘探取心工况复杂、钻进深度大、事故发生率高、岩性复杂、钻进参数难以获取、无法实时掌握钻进状态的难题,在充分调研现有工况识别技术的采集原理、功能种类、现场实测的基础上,结合钻探智能化、自动化的发展方向,运用硬件与软件结合的思想,设计了实时测量钻探参数的方案。论文首次提出使用DBSCAN（针对噪声空间基于分布密度进行聚类的算法）密度聚类法分析钻进参数,结合光电编码器增量的正负性进行工况判别,以获取钻头位置以及钻孔深度。该方案可以判别钻进状态,获取钻进工艺参数,将测量数据作为所钻地层的可钻参数来反演地层。钻机的自动化、智能化研究能获取大量地层资料,有利于推进钻进工艺学发展,实现从经验钻探到智能钻探进的突破。同时,为智能钻进系统奠定基础。     

基于优化经验模态分解和聚类分析的滑坡位移智能预测研究

针对三峡库区"阶跃式"滑坡的变形特征,提出了一种新的滑坡位移预测方法。以白水河滑坡ZG118和XD-01监测点位移数据为例,采用基于软筛分停止准则的经验模态分解(SSSC-EMD)将累计位移-时间曲线和影响因子时间序列自适应地分解为多个固有模态函数(IMF),并采用K均值(K-Means)聚类法对其进行聚类累加,得到有物理含义的位移分量(趋势性位移、周期性位移以及随机性位移)和影响因子分量(高频影响因子和低频影响因子)。使用最小二乘法对趋势性位移进行拟合预测;采用果蝇优化-最小二乘支持向量机(FOA-LSSVM)模型对周期性位移和随机性位移进行预测。将各位移分量预测值进行叠加处理,实现滑坡累计位移的预测。研究结果表明,所提出的(SSSC-EMD)-K-Means-(FOA-LSSVM)模型能够预测"阶跃式"滑坡的位移变化规律,且预测精度高于传统的支持向量机回归(SVR)、最小二乘支持向量机(LSSVM)模型;并通过改变训练集长度,进行单因素分析,发现其与预测精度之间呈正相关关系。