桂林-阳朔地区DEM地形特征与岩性相关性分析及分类研究

地形地貌是岩性解译的重要信息,地形因子作为描述DEM数字曲面几何特征的定量指标参数,可用来定量化表达不同岩性所在地区地形地貌特征。本文以桂林-阳朔地区为研究区,研究地形因子数学、地质意义,建立岩性与地形因子组合间的定量关联,进而实现岩石类型划分。本文基于ASTERGDEM提取坡度、起伏度等12个地形因子,在分析各个地形因子地质意义基础上,通过聚类分析及方差分析的多元统计分析方法,研究各岩性地形因子特性及其关联性,建立研究区岩性之间的定量差异;此外,利用因子分析方法研究岩性分类过程中的主导因素,确定适宜岩性分类方法以实现定量化岩性分类。实验结果表明：不同岩性、不同地形地貌的地形因子（组合）之间具有显著差异,基于因子分析得到的宏观地形复杂度指数（MTI）以及微观曲率指数（MCI）对岩石类型的分类精度达77.36%。研究表明,地形复杂度等地形因子可用于岩性分类,采用因子分析方法可获取反映地形地貌宏观、微观特征的定量指标,且岩性分类效果良好。     

PS测井在机场隧道地基勘查中的应用

PS测井技术是地震勘探方法之一,也是一种简便、快速、准确的原位测试技术。该文介绍了单孔法PS测井的原理及其在铁路工程勘察中的应用,主要包括根据等效剪切波,进行建筑抗震场地类别划分,铁路工程抗震场地类别划分。根据岩土动力学参数,达到评价岩体质量和划分围岩类别目的,利用剪切波速法估算岩土的承载力基本值。通过青岛机场隧道场地的工程实例说明了PS测井技术在岩土工程勘察设计中的应用情况及应用效果。     

Peaks of People: Using Topographic Prominence as a Method for Determining the Ranked Significance of Population Centers

This article borrows a statistical method from physical geography—topographical prominence—to suggest a new technique for measuring the relative significance or rank of population centers. Unlike raw population measures, prominence gives consideration to both the spatial intensity of concentrated population areas as well as the spatial dependence or independence of neighboring settlement clusters in relation to one another. We explain how to apply the topographic prominence calculation method to gridded population data and examine its practical utility through case studies of several U.S. states. We then discuss some ways in which parametric choices about point-to-surface transformations can result in considerably different outcomes and offer further suggestions for conceptualizing and measuring population center significance.     

Classification of inorganic natural fine-grained soils in Korea based on modified plasticity chart

Classification of fine-grained soils is typically conducted using plasticity charts. The typically used plasticity chart proposed by Casagrande was questioned by Polidori proposing different classification criterion in separating clayey and silty soils. Using natural clayey and silty soils sampled from four different coastal sites in Korea, applicability of both Casagrande’s and Polidori’s plasticity charts was evaluated. Classification results of Korean natural soils based on the Casagrande’s and Polidori’s plasticity charts did not match well with those based on the soils’ behavior reported in the previous publication. The disagreement in classification of Korean natural fine-grained soils may result from disregard of considerable silt fraction effect on plastic and liquid limits for Polidori’s chart. Consequently, revised proposal of Polidori’s plasticity chart was tentatively made for further classification of fine-grained soils suitable for Korean natural soils by accounting the effect of silt fraction on soil classification.     

An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing

New Earth observation missions and technologies are delivering large amounts of data. Processing this data requires developing and evaluating novel dimensionality reduction approaches to identify the most informative features for classification and regression tasks. Here we present an exhaustive evaluation of Guided Regularized Random Forest (GRRF), a feature selection method based on Random Forest. GRRF does not require fixing a priori the number of features to be selected or setting a threshold of the feature importance. Moreover, the use of regularization ensures that features selected by GRRF are non-redundant and representative. Our experiments based on various kinds of remote sensing images, show that GRRF selected features provides similar results to those obtained when using all the available features. However, the comparison between GRRF and standard random forest features shows substantial differences: in classification, the mean overall accuracy increases by almost 6% and, in regression, the decrease in RMSE almost reaches 2%. These results demonstrate the potential of GRRF for remote sensing image classification and regression. Especially in the context of increasingly large geodatabases that challenge the application of traditional methods.     

城镇化与生态环境耦合的动态模拟——研究进展与展望

Urbanization and eco-environment coupling is a research hotspot.Dynamic simulation of urbanization and eco-environment coupling needs to be improved because the processes of coupling are complex and statistical methods are limited.Systems science and cross-scale coupling allow us to define the coupled urbanization and eco-environment system as an open complex giant system with multiple feedback loops.We review the current state of dynamic simulation of urbanization and eco-environment coupling and find that:(1)The use of dynamic simulation is an increasing trend,the relevant theory is being developed,and modeling processes are being improved;(2)Dynamic simulation technology has become diversified,refined,intelligent and integrated;(3)Simulation is mainly performed for three aspects of the coupling,multiple regions and multiple elements,local coupling and telecoupling,and regional synergy.However,we also found some shortcomings:(1)Basic theories are inadequately developed and insufficiently integrated;(2)The methods of unifying systems and sharing data are behind the times;(3)Coupling relations and the dynamic characteristics of the main driving elements are not fully understood or completely identified.Additionally,simulation of telecoupling does not quantify parameters and is not systemically unified,and therefore cannot be used to represent spatial synergy.In the future,we must promote communication between research networks,technology integration and data sharing to identify the processes governing change in coupled relations and in the main driving elements in urban agglomerations.Finally,we must build decision support systems to plan and ensure regional sustainable urbanization.