短期暴雨的前期特征和预报实践

打破季节和月份的限制，将所有的暴雨作为研究对象，运用自组织理论总结归纳出一套自成体系的统一的关于短期暴雨预报的前期环流特征和判据。在预报业务上，作者成功地建立三个精确到点的高质量的短期暴雨预报方法。     

山西岩溶水强径流带的成因类型及其水文地质特征

根据成因特征,将山西岩溶水强径流带分为五种类型: 接触带型、河谷型、构造带型、向斜谷地型和复合型。通过野外调查及对大量实测资料的分析研究,揭示出山西岩溶水强径流带的主要发育与分布特征: 岩溶水强径流带呈网状分布;发育深度50～300m;地下水面呈槽谷型,水力坡度一般在0. 6‰～2. 8‰之间。岩溶强径流带水量丰富,单井出水量大于1000m3 /d,是建立岩溶水水源地的理想场所。      

平行F42-9发育的结构面对锦屏水电站高边坡稳定性的影响

锦屏一级水电站左岸高边坡F42-9断层及煌斑岩脉组合的滑块对左岸缆机平台及拱肩槽边坡的稳定性起到控制性的作用。针对F42-9断层两侧平行结构面的发育程度,进行了锚固边坡开挖过程中的弹塑性力学行为分析。结果表明：平行于F42-9断层发育的结构面使锚固边坡岩体的性状急剧下降,现有的加固方案无法满足边坡稳定性的要求。建议根据边坡及抗剪洞开挖过程中揭露的地质信息,及时制定合理的边坡加强、加固措施。     

赤道东太平洋SST强增温过程特征分析

本文利用太平洋海温距平场资料,对１９８６年以来发生的厄尔尼诺过程的开始时间、发展过程及强度等方面进行了分析。并与历史上的厄尔尼诺过程进行比较,结果表明,９０年代是厄尔尼诺事件的多发期,在１９９１￣９８年的８年中,间连续发生四次厄经诺事件,其中１９９７￣９８年的厄尔尼诺,成为有历史记录以来最强的一次过程。     

基于改进DeepLabv3+孪生网络的遥感影像变化检测方法

传统遥感影像变化检测方法依赖人工构建特征,算法设计复杂且精度不高;而将2幅不同时相影像叠加后输入神经网络的遥感影像变化检测方法会造成不同时相的特征相互影响,难以保持原始影像的高维特征,且模型鲁棒性较差。因此,本文提出一种基于改进DeepLabv3+孪生网络的遥感影像变化检测方法,以经典DeepLabv3+网络的编解码结构为基础对网络进行改进:(1)在编码阶段利用共享权值的孪生网络提取特征,通过2个输入端分别接收2幅遥感影像,以保留不同时相影像的高维特征;(2)在特征融合中用密集空洞空间金字塔池化模型代替空洞空间金字塔池化模型,通过密集连接的方式结合每个空洞卷积的输出,以提高对不同尺度目标分割的精度;(3)在解码阶段中针对不同层级特征图信息差异较大,难以融合的问题,引入基于注意力机制的特征对齐模型,引导不同层级的特征对齐并强化学习重要特征,以提升模型的鲁棒性。应用开源数据集CDD验证本文方法的有效性,并与UNet-EF、FC-Siam-conc、Siam-DeepLabv3+和N-Siam-DeepLabv3+网络对比试验。试验结果表明,本文方法在精确率、召回率、F1值和总体精度上达到8...     

The upper Volgian Substage in Northeast Siberia (Nordvik Peninsula) and its Panboreal correlation based on ammonites

Section of the middle and upper Volgian substages and basal Boreal Berriasian in the Cape Urdyuk-Khaya (Nordvik Peninsula) is largely composed of dark argillites substantially enriched in C_org. Characteristic of the section is a continuous succession of ammonite, foraminiferal, ostracode, and dinocyst zones known also in the other Arctic areas. Boundaries of the upper Volgian Substage are recognizable only based on biostratigraphic criteria. The succession of the middle Volgian Taimyrosphinctes excentricus to basal Ryazanian Hectoroceras kochi zones is characterized. The range of the substage is revised. The lower Exoticus Zone, where ammonites characteristic of the Nikitini Zone upper part in the East European platform have been found, is referred to the middle Volgian Substage. Newly found ammonites are figured. Two possible positions of the Jurassic-Cretaceous boundary in the Arctic region, i.e., at the lower and upper boundaries of the Chetae Zone at the top of the upper Volgian Substage, are discussed.     

The Influence of Undersea Feature Naming on Delimitation of Continental Shelf: A Case Study of Japan

Base on the tracing study on the Japanese undersea feature naming activity, this study shows that, Japan has named Bando Basin, removed Hangetsu Trough, Shingetsu Hole and Oki-Daito Trough, and altered some features names near Ogasawara Plateau and Oki-Daito Ridge, before and after the submission for Delimitation in outer limits of continental shelves. Contrary to the general undersea feature naming rule, these actions aim to extend its continental shelf boundaries. It is suggested that the study of relationships between naming undersea feature and marine right and interest should be enhanced.     

A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments

Geographic Object-Based Image Analysis (GEOBIA) is becoming more prevalent in remote sensing classification, especially for high-resolution imagery. Many supervised classification approaches are applied to objects rather than pixels, and several studies have been conducted to evaluate the performance of such supervised classification techniques in GEOBIA. However, these studies did not systematically investigate all relevant factors affecting the classification (segmentation scale, training set size, feature selection and mixed objects). In this study, statistical methods and visual inspection were used to compare these factors systematically in two agricultural case studies in China. The results indicate that Random Forest (RF) and Support Vector Machines (SVM) are highly suitable for GEOBIA classifications in agricultural areas and confirm the expected general tendency, namely that the overall accuracies decline with increasing segmentation scale. All other investigated methods except for RF and SVM are more prone to obtain a lower accuracy due to the broken objects at fine scales. In contrast to some previous studies, the RF classifiers yielded the best results and the k-nearest neighbor classifier were the worst results, in most cases. Likewise, the RF and Decision Tree classifiers are the most robust with or without feature selection. The results of training sample analyses indicated that the RF and adaboost. M1 possess a superior generalization capability, except when dealing with small training sample sizes. Furthermore, the classification accuracies were directly related to the homogeneity/heterogeneity of the segmented objects for all classifiers. Finally, it was suggested that RF should be considered in most cases for agricultural mapping.