SVG网络图像标准支持下的地图显示

介绍了一种全新的网络图像标准SVG（Scalable Vector Grcphics），分析了目前网上地图显示的方法，探讨了运用SVG在网上进行地图显示的可能性，提出了其应用体系和结构。     

FHD 磁力仪分量线圈装置误差对磁偏角观测的影响分析

根据FHD 磁力仪观测磁偏角的原理, 计算了分量线圈不水平导致的磁偏角观测值误差,发现分量线圈仅在EW 方向的倾斜对磁偏角观测产生误差。文中将理论分析结果与实际观测结果进行了对比分析, 发现由于分量线圈底座水平的长期漂移, 仪器的垂直分量及磁偏角观测误差也会发生长期漂移, 长期漂移变化的周期约2 ~ 4 个月, 对垂直分量其幅度约3.5~ 11.0nT, 而磁偏角的幅度约0.1′~ 1.0′, 因此认为在将该仪器观测值用作绝对观测值进行地震异常分析时, 应注意回避这种长期漂移变化。     

磁化强度矢量反演方程及二维模型正反演研究

导了磁化强度矢量层析成像方程,并与磁化强度标量层析成像方程进行了对比. 使该矢量层析成像方程既适用于三维的也适用于二维,既适用于使用磁场垂直分量资料,也适用于使用磁场总强度资料. 本文采用改进的高斯-赛德尔迭代求解磁化强度矢量层析成像方程,在求解方程中引进了与深度有关的权系数. 并对二维模型开展了正反演研究. 当模型层数为二层,且每层51个柱体时,采用零初始模型就能获得较好的磁化强度垂直分量和水平分量反演结果,除了异常体边部外,磁化方向比较可靠. 当模型层数为五层,且每层51个柱体时,采用零初始模型不能得到较好结果,改用接近背景值的均匀初始模型,反演的磁化强度垂直分量和水平分量大致接近真实,但不能刻划某些细节.     

Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China

The Support Vector Machine (SVM) is an increasingly popular learning procedure based on statistical learning theory, and involves a training phase in which the model is trained by a training dataset of associated input and target output values. The trained model is then used to evaluate a separate set of testing data. There are two main ideas underlying the SVM for discriminant-type problems. The first is an optimum linear separating hyperplane that separates the data patterns. The second is the use of kernel functions to convert the original non-linear data patterns into the format that is linearly separable in a high-dimensional feature space. In this paper, an overview of the SVM, both one-class and two-class SVM methods, is first presented followed by its use in landslide susceptibility mapping. A study area was selected from the natural terrain of Hong Kong, and slope angle, slope aspect, elevation, profile curvature of slope, lithology, vegetation cover and topographic wetness index (TWI) were used as environmental parameters which influence the occurrence of landslides. One-class and two-class SVM models were trained and then used to map landslide susceptibility respectively. The resulting susceptibility maps obtained by the methods were compared to that obtained by the logistic regression (LR) method. It is concluded that two-class SVM possesses better prediction efficiency than logistic regression and one-class SVM. However, one-class SVM, which only requires failed cases, has an advantage over the other two methods as only “failed” case information is usually available in landslide susceptibility mapping.     

根据空间大地测量结果建立的中国地壳构造块体的运动模型

本文利用空间大地测量的结果初步建立了中国地壳各构造块体的运动模型,该模型与由地质资料建立的地质学模型相比呈现较好的一致性,反映了中国及邻近地区的板块、亚板块和活动构造块体的水平运动特征和趋势。     

Interrupting a multi-species bioinvasion vector: the efficacy of in-water cleaning for removing biofouling on obsolete vessels

Vector management is the primary method for reducing and preventing nonindigenous species (NIS) invasions and their ecological and economic consequences. This study was the first to examine the efficacy of in-water scrubbing using a submersible cleaning and maintenance platform (SCAMP) to prevent invertebrate species transfers from a heavily fouled obsolete vessel. Initially, prior to treatment, 37 species were recorded in a biofouling matrix that reached 30cm depth in some locations. The bryozoan Conopeum chesapeakensis, and bivalves Mytilopsis leucophaeata and Ischadium recurvum, were dominant sessile species that created structure, supporting mobile biota that included crabs and the associated parasitic barnacle Loxothylacus panopae. Scrubbing had the effect of significantly reducing organism extent and the number of species per sample, but a substantial and diverse (30 species) residual fouling community remained across the entire vessel. Further assessments of management options are needed to prevent potentially damaging NIS transfers. Additional measures taken within an integrated vector management (IVM) strategy may further improve invasion prevention measures.     

多方法联合分析未来地震发生趋势

综合多种前兆方法分析地震活动规律,研究目标地区未来地震发生的趋势。首先采用图像信息法(PI)进行扫描,从长期尺度上找出研究区域内地震活动异常的地区,并结合相关的活动断裂分布及区域地震活动确定未来地震的发震危险区;然后采用加卸载响应比(LURR)、态矢量(SV)、矩张量加速释放(AMR)等中短期前兆方法分析这些区域的地震发生可能,并对地震发生的相关信息做进一步估算;在空间上实现向地震危险区域的逐渐逼近,时间上实现从长期预测到中短期预测的自然过渡。作为回顾性震例研究,我们对近3年来发生在中国西部的强震(ML6.5)进行了检验,结果表明,相对于单一算法而言,将不同前兆方法适当组合结合能够更为明确地为未来地震危险性评估提供信息和约束。     

120°E赤道电离异常区电子浓度总含量分析与预测

基于国际全球定位服务中心(International GPS Service,IGS)提供的120°E 上空1999-2009年IONEX格式电离层电子浓度总含量(TEC)资料,分析赤道电离异常驼峰区TEC峰值Inc和Isc的年变化和季节变化以及与太阳、地磁活动的相关性.在11年时间尺度上,Inc和Isc与太阳辐射P指数的日均值有较好的相关性(r=0.90和r=0.84),而与地磁活动指数Dst、Kp和Ap日均值的相关性均不好.驼峰区TEC峰值Inc和Isc都是在北半球春、秋季出现极大值,而且冬季值大于夏季值,即Inc呈现"半年异常"和"冬季异常"现象,我们认为Inc和Isc相似的半年变化特征是与赤道上空电离层电急流相关的东向电场半年变化导致的.利用支持向量回归方法构建了EIA指数的预报模型,预报试验结果表明,该预报模型能较准确地描述Inc和Isc的变化,对南北驼峰TEC峰值预报的平均相对误差分别为22.96%和10.2%.基于支持向量机回归的预测方法为赤道电离异常特征指数预报的实现提供一条有效方法途径和好的应用前景.     

海洋可控源电磁三维非结构矢量有限元数值模拟

本文实现了海洋可控源电磁三维矢量有限元数值模拟.由于采用非结构四面体单元进行三维网格剖分,该方法可以模拟复杂电性异常体和海底地形.一维模型的数值模拟结果表明,电场实、虚部均与解析解吻合得相当好,计算误差基本小于1%.二维模型的计算结果与已有的二维自适应非结构有限元模拟结果吻合很好.带地形模型的数值模拟结果显示,海底地形对电场影响很大,有可能掩盖海底油气藏产生的异常.     

基于土壤反射光谱特性的广东省稻田土壤快速分类

选择广东省215个村镇稻田的土壤样本,首先利用ASD Field Spec3测量土壤样本在350~2 500 nm的光谱,并采用S-G一阶导数平滑滤波降低样本测量中光照差异的影响,然后将遗传算法（Genetic Algorithm,GA）和支持向量机分类（Support Vector Machine,SVM）分别用于提取分类光谱特征和建立分类模型,分别在土纲、亚纲、土类3个层次进行土壤分类。结果表明：1）在不同的分类层次下,与铁氧化物密切相关的650~710以及900 nm附近光谱,与羟基矿物吸收有关的2 207~2 237和2 377~2 397 nm区间均被作为分类特征变量。2）随着土壤类型的细分,分类所需变量增多。在土类级,对有机质敏感的2 080 nm附近的光谱也被引入分类定标模型中,土纲和亚纲下分类精度＞67%,土类级分类精度为58.67%。利用遗传算法提取光谱特征,进行基于支持向量机的土壤分类具有一定优势。