Vertical Sound Velocity Transition in the Coral Reef Core and Its Significance of Indicating Facies

On the basis of the sound velocity measurements of the coral reef core from Nanyong No.1 well of Yongshu Reef in the Nansha Islands,the paper studies the relations between the vertical sound velocity transition features in the coral reef core and the corresponding stratigraphic depositional facies change as well as stratigraphic gap of erosion,analyses the cause of the sound velocity transition,expounds the concrete process of the sea level change resulting in the stratigraphic gap of erosion and facies change in the coral reef and explains the relations between the vertical sound velocity transition in the coral reef core and the corresponding stratigraphic paleoclimate and the sea level change.This study is of important practical value and theoretical significance to the island and reef engineering construction and the acoustic logging for oil exploration in the reef limestone area as well as the paleoceanographic study of the marginal sea in the westerm Pacific Ocean.     

长江口羽状锋海区浮游植物的生态研究：—昼夜分布动态

本文对长江口羽状锋海区浮游植物的昼夜变动进行了初步研究，结果表明，浮游植物数量的昼夜变化明显，潮汐作用是影响数量昼夜分布变化的主导因素，同时也与该地区浮游植物的水平分布有着密切的联系。     

广东红海湾、珠江口及电白—阳江近海第四纪沉积

根据物探和钻探资料，研究区内第四纪沉积物可分为两部分，下部地震反射结构复杂，斜层理发育，粒度较粗，为晚更新世产物；上部的地震反射结构简单，呈水平状或近水平状结构，粒度较细，为全新世沉积。     

激光雷达技术在我国地形测图中应用前景广阔

激光雷达技术（Light Detection and Ranging,LiDAR）是近十年来快速发展并得到广泛应用的测量手段。美国地质调查局（USGS）、美国国家宇航局（NASA）以及美国地质学会（AASG）已经开始讨论建设全美高分辨率激光雷达数据库。我国正处于东部城市化、土地利用巨变、西部无图而经济高速发展的时期,国家、地方、企业生产单位迫切需要现实性强、精确度高、比例尺大的地形数据产品。满足这些要求的主要途径是采用激光雷达这一先进的测绘技术。我们建议国家、地方与企业统筹规划,各司其职,努力建设我国的激光雷达基础数据库。本文通过Stoker等（2008）介绍的第二届全美激光雷达战略研讨会上论述的激光雷达技术特点、激光雷达数据的科学需求与应用以及商业化运作问题,结合我国目前的激光雷达发展趋势、测绘数据需求以及土地利用变化监测等科学问题,简析激光雷达技术在我国的应用前景。     

基于RS与GIS技术的变更图斑自动识别方法研究

基于RS与GIS技术的土地利用变更图斑自动识别方法,即"以遥感正射影像图为基础,以地理信息和地物反射光谱为判别依据"的变更信息识别方法,能够尽量避免传统利用遥感影像分类技术进行地物变更识别存在的不足。本文通过对距离算法的改良,有效利用遥感数据的实时性和丰富性,在地理信息系统的支持下,提高了对地物变更地块的识别精度。     

Tephra Discovered in High Resolution Peat Sediment and Its Indication to Climatic Event

Floating tephra was deposited together with ice core,snow layer,abyssal sediment,lake sediments,and other geological records.It is of great significance to interpret the impact on the climate change of volcanic eruptions from these geological records.It is the first time that volcanic glass was discovered from the peat of Jinchuan(金川)Maar,Jilin(吉林)Province,China.And it is in situ sediments from a near-source explosive eruption according to particle size analysis and identification results.The tephra were neither from Tianchi(天池)volcano eruptions,Changbai(长白)Mountain,nor from Jinlongdingzi(金龙顶子)volcano about 1 600 aBP eruption,but maybe from an unknown eruption of Longgang(龙岗)volcano group according to their geochemistry and distribution.Geochemical characters of the tephra are similar to those of Jingiongdingzi,which are poor in s.Jica,deficient in alkali,Na20 content is more than K20 content,and are similar to distribution patterns of REE and incompatible elements,which helps to speculate that they originated from the same mantle magma with rare condemnation,and from basaltic explosive eruption of Longgang volcano group.The tephra,from peat with age proved that the eruption possibly happened in 15 BC-26 AD,is one of Longgang volcano group eruption that was not recorded and is earlier than that of Jinglongdingzi about 1 600 aBP eruption.And the sedimentary time of tephra is during the period of low temperature alteration.which may be the influence of eruption toward the local climate according to the correlativity of eruption to local temperature curve of peat cellulose oxygen isotope.     

基于物方影像匹配的DEM聚类变化检测

提出了DEM的变化检测问题,采用了基于密度的聚类分析方法对匹配所得的初始变化区域进行分析,实现了地表高程变化区域的检测.     

对我国西南地区河谷深厚覆盖层成因机理的新认识

近年来,在我国水能资源开发过程中,发现各河流现代河床以下普遍堆积厚达数十米甚至上百米的松散堆积物。河谷深厚覆盖层的存在,不仅严重制约了工程坝址的选择,影响相关流域水电资源的开发利用,也给坝工设计带来巨大的困难。由于深厚覆盖层埋藏于现代河床之下,其形成年代一般先于一二级阶地,有悖于河流发育演化的常理,其成因一直令人费解。首次将河谷深切和深厚堆积事件与全球气候变化、海平面升降运动、地壳运动等有机地联系起来,并提出冰期、间冰期全球海平面大幅度升降,是导致河流深切成谷并形成深厚堆积的主要原因的新观点。在此基础上,引入层序地层学原理,从理论上较好地解释了全球气候变化导致海平面和河流侵蚀基准面大幅变化,并产生河谷深切和深厚堆积的原因和过程。最后,进一步将沿河大型古滑坡的孕育和发生与河谷深切事件相联系,提出沿河大型古滑坡是在河谷深切期因前缘临空较好而形成的新观点,从而对沿河古滑坡前缘剪出口高程往往低于现代河床数十米的原因给出了较合理的解释。     

Prediction of UT1–UTC,LOD and AAM χ<Subscript>3</Subscript> by combination of least-squares and multivariate stochastic methods

This article presents the application of a multivariate prediction technique for predicting universal time (UT1–UTC), length of day (LOD) and the axial component of atmospheric angular momentum (AAM χ ₃). The multivariate predictions of LOD and UT1–UTC are generated by means of the combination of (1) least-squares (LS) extrapolation of models for annual, semiannual, 18.6-year, 9.3-year oscillations and for the linear trend, and (2) multivariate autoregressive (MAR) stochastic prediction of LS residuals (LS + MAR). The MAR technique enables the use of the AAM χ ₃ time-series as the explanatory variable for the computation of LOD or UT1–UTC predictions. In order to evaluate the performance of this approach, two other prediction schemes are also applied: (1) LS extrapolation, (2) combination of LS extrapolation and univariate autoregressive (AR) prediction of LS residuals (LS + AR). The multivariate predictions of AAM χ ₃ data, however, are computed as a combination of the extrapolation of the LS model for annual and semiannual oscillations and the LS + MAR. The AAM χ ₃ predictions are also compared with LS extrapolation and LS + AR prediction. It is shown that the predictions of LOD and UT1–UTC based on LS + MAR taking into account the axial component of AAM are more accurate than the predictions of LOD and UT1–UTC based on LS extrapolation or on LS + AR. In particular, the UT1–UTC predictions based on LS + MAR during El Niño/La Niña events exhibit considerably smaller prediction errors than those calculated by means of LS or LS + AR. The AAM χ ₃ time-series is predicted using LS + MAR with higher accuracy than applying LS extrapolation itself in the case of medium-term predictions (up to 100 days in the future). However, the predictions of AAM χ ₃ reveal the best accuracy for LS + AR.