Geostatistical analysis and conditional simulation for estimating the spatial variability of hydraulic conductivity in the Choushui River alluvial fan,Taiwan

This work evaluated the spatial variability and distribution of heterogeneous hydraulic conductivity (K) in the Choushui River alluvial fan in Taiwan, using ordinary kriging (OK) and mean and individual sequential Gaussian simulations (SGS). A baseline flow model constructed by upscaling parameters was inversely calibrated to determine the pumping and recharge rates. Simulated heads using different K realizations were then compared with historically measured heads. A global/local simulated error between simulated and measured heads was analysed to assess the different spatial variabilities of various estimated K distributions. The results of a MODFLOW simulation indicate that the OK realization had the smallest sum of absolute mean simulation errors (SAMSE) and the SGS realizations preserved the spatial variability of the measured K fields. Moreover, the SAMSE increases as the spatial variability of the K field increases. The OK realization yields small local simulation errors in the measured K field of moderate magnitude, whereas the SGS realizations have small local simulation errors in the measured K fields, with high and low values. The OK realization of K can be applied to perform a deterministic inverse calibration. The mean SGS method is suggested for constructing a K field when the application focuses on extreme values of estimated parameters and small calibration errors, such as in a simulation of contaminant transport in heterogeneous aquifers. The individual SGS realization is useful in stochastically assessing the spatial uncertainty of highly heterogeneous aquifers. Copyright © 2004 John Wiley & Sons, Ltd.     

基于网格计算环境下的空间信息多级格网研究

本文提出一种适应网格计算环境下的空间信息多级格网(SIMG，Spatial Information Multi-Grid)技术，通过将网格计算技术与地学格网相结合，提出SIMG的构想并探讨其关键技术的实现；通过研究格网内细部地物表示的精度，力求建立SIMG的划分标准，从而建立我国空间信息多级格网的体系结构。     

Spatial Autocorrelation and Localization of Urban Development

A nonlinear analysis of urban evolution is made by using of spatial autocorrelation theory. A first-order nonlinear autoregression model based on Clark’s negative exponential model is proposed to show urban population density. The new method and model are applied to Hangzhou City, China, as an example. The average distance of population activities, the auto-correlation coefficient of urban population density, and the auto-regressive function values all show trends of gradual increase from 1964 to 2000, but there always is a sharp first-order cutoff in the partial auto- correlations. These results indicate that urban development is a process of localization. The discovery of urban locality is significant to improve the cellular-automata-based urban simulation of modeling spatial complexity.     

The spatial division of industries of the pearl river delta in the 1990s: Characteristics and trends

The article analyzed the spatial division of industries of the Pearl River Delta (PRD) in the 1990s, a period that witnessed the deepening of the reform and opening policies and continued rapid development of the region. By adopting the index of specialization, the extent of specialization, its change and its spatial distribution in the region are measured and demonstrated. The research revealed that, despite the trend of more balanced development of the secondary sector between the PRD and the rest of the province, in the PRD, it is the unbalanced development that dominated and the spatial division of the secondary sector has been shaped. The tertiary industry has also been experiencing unbalance development and the spatial division of the tertiary sector has been emerging, its extent of concentration appearing even more evident than that of the secondary sector. The author also discussed the linkage between the spatial division of the two sectors. In the end of the paper, the trends of the spatial division of the industries in the PRD are forecasted. Foundation item: Under the auspices of the National Natural Science Foundation of China (No. 49831030). Biography: XU Yong-jian (1972 – ), female, a native of Changsha City, Ph. D.. Her research interest includes urban geography.     

REVIEWS OF GEOGRAPHIC SOFTWARE

Geographic Software Reviewed in this article: LIFE TABLES and the LESLIE MATRIX. CONDUIT MENTMAP2 . Lawrence W. Carstensen Jr . PC-MATLAB , Version 1.0. The Math Works, Inc. QUICKMAP , version 1.0. Sammamish Data Systems, Inc. SYSTAT: THE SYSTEM FOR STATISTICS , Version 2.1, Systat Inc. USA DISPLAY . Instant Tecall     

Knowledge based systems for risk assessment of the subsurface of contaminated sites

Computer-based risk evaluation methods are practical tools to compare automatically and evaluate contaminated sites. HYDRISK is an example of adequate knowledge-based systems: HYDRISK evaluates hydrogeological properties and chemical criteria relevant to contaminant transport with respect to the necessary remedial actions. It works at a high investigative level and enables geologists and engineers to draw differentiated conclusions for selecting remediation methods for the contaminated site. HYDRISK emphasizes space and time dependent aspects of the contaminant transport. It is supported by a geographic information system (GIS) to display the evaluation results. Some ideas on considering the spatiotemporal variability of relevant parameters by means of geostatistical methods and numerical models will be given.     

Spatial averaging of hydraulic conductivity under radial flow conditions

Steady-state radial flow in three-dimensional heterogeneous media is investigated using a geostatistical approach. The goal of the study is to develop a model of the relationship between corescale hydraulic conductivities measured at the wellbore and the conductivity of the surrounding drainage region as measured by a larger scale flow experiment such as a pump test. Conductivity at the point or core-scale is modeled as a stationary and multivariate lognormal spatial random function. Conductivity of the drainage region is obtained by a weighted nonlinear spatial average over the point-scale values within. This empirical spatial averaging process is shown to yield excellent approximations of true effective drainage region conductivities calculated using a numerical flow model. The geostatistical model for point-scale conductivity and the spatial averaging process are used to determine the first and second order ensemble moments of drainage region conductivity. In particular, an expression is derived for the conditional expectation of drainage region conductivity given point-scale values measured at the wellbore. The results are illustrated in a case study of a well from a sandstone oil reservoir where both core and transient-test conductivity data from the same interval are available for comparison.     

我国恐龙空间分异原因初探

本文讨论了中国境内先后出现的恐龙动物在古地理位置、分布范围上的变化特点，提出引起中国恐龙空间分异的主要原因是占地理环境的巨大变迁，同时也与占气候、占植物的变化有密切关系。并进一步指出，中国恐龙绝灭的原因应该是突发的灾变。     

Bias in estimating fractal dimension with the rescaled-range (R/S) technique

Fractal geostatistics are being applied to subsurface geological data as a way of predicting the spatial distribution of hydrocarbon reservoir properties. The fractal dimension is the controlling parameter in stochastic methods to produce random fields of porosity and permeability. Rescaled range (R/S)analysis has become a popular way of estimating the fractal dimension, via determination of the Hurst exponent (H). A systematic investigation has been undertaken of the bias to be expected due to a range of factors commonly inherent in borehole data, particularly downhole wireline logs. The results are integrated with a review of previous work in this area. Small datasets. overlapping samples, drift and nonstationariry of means can produce a very large bias, and convergence of estimates of H around 0.85–0.90 regardless of original fractal dimension. Nonstationarity can also account for H>1, which has been reported in the literature but which is theoretically impossible for fractal time series. These results call into question the validity of fractal stochastic models built using fractal dimensions estimated with the R/Smethod.     

Random vectors and spatial analysis by geostatistics for geotechnical applications

Geostatistics is extended to the spatial analysis of vector variables by defining the estimation variance and vector variogram in terms of the magnitude of difference vectors. Many random variables in geotechnology are in vectorial terms rather than scalars, and its structural analysis requires those sample variable interpolations to construct and characterize structural models. A better local estimator will result in greater quality of input models; geostatistics can provide such estimators: kriging estimators. The efficiency of geostatistics for vector variables is demonstrated in a case study of rock joint orientations in geological formations. The positive cross-validation encourages application of geostatistics to spatial analysis of random vectors in geoscience as well as various geotechnical fields including optimum site characterization, rock mechanics for mining and civil structures, cavability analysis of block cavings, petroleum engineering, and hydrologic and hydraulic modelings.