北淮阳构造带的属性及其对板块缝合线位置的启示

北淮阳构造带为一条沿大别造山带北麓展布的狭长构造带,是华北板块与扬子板块碰撞造山的产物,其物源组成、构造属性等研究内容是揭示南北板块在中国东部的大地构造单元格局以及时空演化的关键信息。结合构造变形、岩石学、岩石地球化学、锆石U-Pb年代学等方法对北淮阳前中生代岩石地层单元苏家河群/卢镇关群和信阳群/佛子岭群进行对比分析,就其构造属性形成这样的基本认识:北淮阳构造带是杨子板块向华北板块俯冲-碰撞的结合带,其物质组成具有双源性-主体部分来源于杨子板块,由代表了杨子板块北缘基底的晚元古代苏家河群(784±19Ma)/卢镇关群(756±12Ma)和杨子板块被动陆缘复理石沉积建造-中泥盆世至晚泥盆世早期的信阳群南湾组组成,后者相当于商丹缝合带以南的刘岭群;信阳群龟山组亲缘于华北板块并构成华北板块南缘的弧前楔形沉积体,是沿商丹缝合带分布的桃花铺弧前楔形沉积体的东延。佛子岭群总体可与信阳群相连,全岩成分显示,佛子岭群沉积构造环境既有活动大陆边缘,又有被动大陆边缘。由于中生代以来多期构造变形叠加(至少三期),佛子岭群岩石地层单元的叠置关系被改造或重置,使其缺少"构造解体"的证据,而无法在构造单元上细分。对于两大板块之间的缝合带位置,笔者认为:商丹缝合带进入北淮阳构造带后,应沿着北淮阳的北界,与龟山-梅山-六安-磨墩水库一线延伸的断裂带相连。     

鲁中东-苏西北Au- Fe金刚石成矿带特征及资源潜力

随着找矿的深入,在鲁中东-苏西北地区新发现了一批大中型金、铁和金刚石矿床,该区已成为我国重要的矿产资源基地。本文在新的研究成果和认识基础上,通过综合分析,在该地区划分新的重要成矿部署区带,厘定边界并命名为"鲁中东-苏西北Au-Fe金刚石成矿带",初步建立了该成矿带成矿谱系,结果显示成矿带内存在太古宙、元古宙、古生代、中生代以及新生代等5期成矿。区内矿产以金、铁以及金刚石为主,金矿床类型主要有石英脉型、破碎蚀变岩型,铁矿类型主要有沉积变质型和矽卡岩型,金刚石矿主要为岩浆型。结合研究区找矿最新进展和全国矿产资源潜力评价最新成果,认为本区Au、Fe、金刚石资源潜力巨大。在此基础上划分了6处远景区,为进一步实现找矿突破及地质找矿战略部署提供了科学依据。     

基于GPS无验潮水深测量在自然河段冲淤变化的精度探讨

无验潮模式已经在相关领域得到成熟的运用,但是在内河用于水文泥沙冲淤分析尚属空白。本文简要介绍了GPS无验潮测量的基本原理及作业方法,结合2014年长江三峡工程库区重庆市主城区河段河道演变观测项目,以九龙坡典型河段为例,对该河段进行了验潮与无验潮模式测量,并对两种方法得出的结果进行了计算与分析。测试结果表明,基于GPS无验潮测量模式进行水文泥沙项目观测,对水文泥沙分析精度有一定的影响,对勘测项目的经济效益上有一定的提高。     

土地利用现状和规划数据建库容差浅析

通过对第二次全国土地调查(以下简称"二调")和土地利用总体规划数据建库过程中误差的产生和传播分析,通过误差理论和容差设置实际研究。提出现行数据建库容许误差(以下简称容差)设置不合理,设置过高引起大量无用的数据处理工作。建议合理设置容差参数,提高数据库建设和更新效率。     

Weak GPS signal tracking using FFT discriminator in open loop receiver

An open loop tracking architecture, which tracks GPS signals under weak and challenging conditions, is analyzed. The in-phase and quadrature-phase integration pair is regarded as a single tone complex signal. An FFT-based method is used as a frequency discriminator to estimate the Doppler frequency residual of the single tone signal. Another FFT-based method applies complex squaring to eliminate the effect of the navigation data bits polarities. The performance of the FFT-based discriminators is assessed in three criteria. Those criteria are the signal strength and dynamic range that can be tracked and the accuracy of the estimated Doppler frequency. In addition, the performance of the discriminators is analyzed to provide the theoretical and simulated peak detection probability. The results indicate that the FFT discriminator can track signals about 5 dB weaker than the signals that can be tracked by the complex squared FFT discriminator. In a quasi-static environment, the Doppler frequency residual can be assumed to be around zero, which can enable the FFT-based discriminators to track signals with approximately 2 dB less power. Moreover, the performance of the FFT-based discriminators is compared with the performance of two other frequency discriminators, namely the fast–slow and power-based. The comparison results indicate that these two frequency discriminators give higher frequency estimation accuracy, but they have a narrower dynamic range.     

Validation of MODIS Vegetation Continuous Fields for monitoring deforestation and forest degradation: two cases in Mexico

This study assesses whether MODIS Vegetation Continuous Fields percent tree cover (PTC) data can detect deforestation and forest degradation. To assess the usefulness of PTC for detecting deforestation, we used a data set consisting of eight forest and seven non-forest categories. To evaluate forest degradation, we used data from two temperate forest types in three conservation states: primary (dense), secondary (moderately degraded) and open (heavily degraded) forest. Our results show that PTC can differentiate temperate forest from non-forest categories (p = 0.05) and thus suggests PTC can adequately detect deforestation in temperate forests. In contrast, single-date PTC data does not appear to be adequate to detect forest degradation in temperate forests. As for tropical forest, PTC can partially discriminate between forest and non-forest categories.     

A support vector machine classifier based on a new kernel function model for hyperspectral data

The kernel function is a key factor to determine the performance of a support vector machine (SVM) classifier. Choosing and constructing appropriate kernel function models has been a hot topic in SVM studies. But so far, its implementation can only rely on the experience and the specific sample characteristics without a unified pattern. Thus, this article explored the related theories and research findings of kernel functions, analyzed the classification characteristics of EO-1 Hyperion hyperspectral imagery, and combined a polynomial kernel function with a radial basis kernel function to form a new kernel function model (PRBF). Then, a hyperspectral remote sensing imagery classifier was constructed based on the PRBF model, and a genetic algorithm (GA) was used to optimize the SVM parameters. On the basis of theoretical analysis, this article completed object classification experiments on the Hyperion hyperspectral imagery of experimental areas and verified the high classification accuracy of the model. The experimental results show that the effect of hyperspectral image classification based on this PRBF model is apparently better than the model established by a single global or local kernel function and thus can greatly improve the accuracy of object identification and classification. The highest overall classification accuracy and kappa coefficient reached 93.246% and 0.907, respectively, in all experiments.     

Approach to calculating spatial similarity degrees of the same river basin networks on multi-scale maps

Obtaining spatial similarity degrees among the same objects on multi-scale maps is of importance in map generalization. This paper firstly defines the concepts of ‘map scale change’ and ‘spatial similarity degree’; then it proposes a model for calculating the spatial similarity degree between a river basin network at one scale and its generalized version at another scale. After this, it validates the new model and gets 16 points in the model validation process. The x-coordinate and y-coordinate of each point are map scale change and spatial similarity degree, respectively. Last, a formula for calculating spatial similarity degree taking map scale change as the only variable is obtained by the curve fitting method. The formula along with the model can be used to automate the algorithms for simplifying river basin networks.     

Laboratory Estimation of Rock Joint Stiffness and Frictional Parameters

Numerical modeling of complex rock engineering problems involves the use of various input parameters which control usefulness of the output results. Hence, it is of utmost importance to select the right range of input physical and mechanical parameters based on laboratory or field estimation, and engineering judgment. Joint normal and shear stiffnesses are two popular input parameters to describe discontinuities in rock, which do not have specific guidelines for their estimation in literature. This study attempts to provide simple methods to estimate joint normal and shear stiffnesses in the laboratory using the uniaxial compression and small-scale direct shear tests. Samples have been prepared using rocks procured from different depths, geographical locations and formations. The study uses a mixture of relatively smooth natural joints and saw-cut joints in the various rock samples tested. The results indicate acceptable levels of uncertainty in the calculation of the stiffness parameters and provide a database of good first estimates and empirical relations which can be used for calculating values for joint stiffnesses when laboratory estimation is not possible. Joint basic friction angles have also been estimated as by-products in the small scale direct shear tests.