南海大洋钻探及海洋地质与地球物理前沿研究新突破

南海是西太平洋地区规模最大且具有代表性的边缘海盆地之一。经过近几十年的研究积累,尤其是通过实施5个国际大洋钻探航次(1999–2018年)与国家自然科学基金委“南海深海过程演变”重大研究计划(2011–2019年),我国科学家获得了大量宝贵的第一手资料,取得了一系列创新进展与重大突破,标志着南海海洋地质与地球物理研究正走向国际前沿。重要研究成果包括:(1)新提出南海是“板缘张裂”盆地,与经典的大西洋型陆缘模式不同;(2)大洋钻探首次获取了基底玄武岩样品,结合中国在南海首次深拖地磁测量实验,精确测定了南海海盆玄武岩年龄,揭示南海海盆从东向西分段扩张;(3)大洋钻探结果发现南海陆缘岩石圈减薄之初岩浆迅速出现,未发现缓慢破裂造成的蛇纹岩出露;(4)发现南海扩张结束后仍存在大量岩浆活动,可能受控于多种构造与地幔因素;(5)地球化学证据与地球动力学模拟都显示南海岩浆的形成受到周边俯冲带的影响。目前我国的海洋地球科学正在进入崭新的发展阶段,有望以南海为基点,开始拓展到周边大洋,通过主导大型研究计划以及建设我国大洋钻探平台,以提升我国在南海、西太平洋与印度洋海洋地质科学研究的实质性影响力与引领地位。     

Sea Level Prediction in the Yellow Sea From Satellite Altimetry With a Combined Least Squares-Neural Network Approach

Accessible high-quality observation datasets and proper modeling process are critically required to accurately predict sea level rise in coastal areas. This study focuses on developing and validating a combined least squares-neural network approach applicable to the short-term prediction of sea level variations in the Yellow Sea, where the periodic terms and linear trend of sea level change are fitted and extrapolated using the least squares model, while the prediction of the residual terms is performed by several different types of artificial neural networks. The input and output data used are the sea level anomalies (SLA) time series in the Yellow Sea from 1993 to 2016 derived from ERS-1/2, Topex/Poseidon, Jason-1/2, and Envisat satellite altimetry missions. Tests of different neural network architectures and learning algorithms are performed to assess their applicability for predicting the residuals of SLA time series. Different neural networks satisfactorily provide reliable results and the root mean square errors of the predictions from the proposed combined approach are less than 2?cm and correlation coefficients between the observed and predicted SLA are up to 0.87. Results prove the reliability of the combined least squares-neural network approach on the short-term prediction of sea level variability close to the coast.     

我国瓯江口和珠江口凤鲚洄游模式和生境利用研究

Habitat use of the tapertail anchovy(Coilia mystus Linnaeus, 1758) from the Oujiang River Estuary and the Zhujiang(Pearl) River Estuary was studied by examining the environmental signatures of Sr and Ca in otoliths using electron probe microanalysis. Individuals from the Oujiang River had higher and varied Sr:Ca ratios(expressed as(Sr:Ca)×1 000, 3.83–13.0 average) in the otolith core regions, suggesting that they were born in brackish or sea waters, and that a freshwater habitat might not be necessary for egg hatching and larval growth.While, individuals from the Zhujiang River had lower Sr:Ca ratios(0.39–2.51 average) in the core regions,suggesting a freshwater origin. After hatching, anchovies from the Zhujiang River migrate downstream to the river estuary close to brackish water. Our results demonstrated varied habitat use for spawning during stages of early life history between the two populations, and suggested that such variations are promoting diversity of life history strategies of this species.     

毛蚶天津群体形态性状对体质量的影响研究

为探讨形态性状对体质量的影响,指导毛蚶的选育,以毛蚶天津群体为研究对象,测量壳长、壳宽、壳高、体质量,并进行统计分析。结果表明,壳宽是体质量的主要影响因子,其与体质量的相关系数为0.953,直接作用为0.505,间接作用为0.448,壳长、壳高间接作用分别为0.483、0.480,综合决定系数为0.706 6。毛蚶体质量多元回归方程为Y=-40.8+0.438X1+1.124X2+0.469X3,R2=0.924;以壳宽为自变量,体质量回归方程为:Y=5.443×10-3X22.519,R2=0.937。     

基于无人机多源遥感数据海岛植被叶面积指数协同反演研究

无人机多源遥感数据的获取、融合以及应用是当今研究的热点和难点。文中以城洲岛为例,针对海岛特殊的地理生态环境,获取无人机多源遥感数据。结合无人机多光谱遥感数据定量分析各遥感植被指数与植被叶面积指数(Leaf Area Index, LAI)的响应关系,构建单因子遥感反演模型;基于无人机激光LiDAR点云提取海岛植被冠层高度模型(Canopy Height Model,CHM),并将其作为自变量引入到多源统计回归分析中,从而构建多源遥感数据协同反演模型,对区域尺度下海岛叶面积指数(LAI)进行估算,开展验证和精度评价。结果显示,加入植被冠层高度因子的协同反演模型的判定系数R2为0.92,绝对平均误差系数为12.29%,预测精度要优于单因子反演模型(判定次数R2为0.86,绝对平均误差系数19.95%)。研究表明,加入了植被冠层高度因子的协同反演模型能在一定程度上提高乔木植被LAI的预测精度。实践证明,无人机多源遥感技术在生态学定量研究中具有巨大的潜力和广阔的应用前景。     

一种海洋混合层深度的智能识别方法研究

文章提出了一种识别混合层深度的人工智能方法。该方法在温度(密度)与压强(或深度)间建立线性模型, 并且将其系数和方差做成一组表征廓线特征的统计量。初始时为模型设定一个主观的先验分布, 在一个自海表向下移动的窗口内通过贝叶斯链式法则和最小描述长度原理学习新数据, 得到系数均值的最大后验概率估计。用F-检验识别系数发生突变的位置, 以此确定混合层的存在性及其深度。通过2017年2月太平洋海域的地转海洋学实时观测阵(Array for Real-time Geostrophic Oceanography, ARGO)数据进行测试, 并且以质量因子(Quality Index, QI)值作为判断识别混合层深度结果准确性的依据, 发现该方法相比于梯度法、阈值法、混合法、相对变化法、最大角度法和最优线性插值法在识别结果上具备更大的QI值。表明该方法能够准确识别混合层深度。     

Molecular Identification and Expression Analysis of Carbonic Anhydrase VII in Pufferfish (Takifugu rubripes)

Ocean Science Journal - A carbonic anhydrase VII gene, encoding 277 amino acids, was identified in the intestinal tissue of pufferfish (Takifugu rubripes). The translated protein with an 833-bp...     

Temperature-Dependent Bifurcated Seasonal Shift of Phytoplankton Community Composition in the Coastal Water off Southwestern Korea

Ocean Science Journal - Coastal water around the archipelago off southwestern Korea has been bathed in an unprecedented pool of warm water in summer in recent years. Here, we examined phytoplankton...     

A Review on Marine N2 Fixation: Mechanism,Evolution of Methodologies,Rates, and Future Concerns

Ocean Science Journal - Investigations on marine N2 fixation have gained momentum since 1960s with eventual establishments of relevant methodologies to identify species involved and quantify the...     

基于北斗短报文的震源船跟踪系统设计与实现

通过在震源船上部署3套GPS接收机,并对3个点的位置信息进行汇总和压缩,利用北斗短报文将压缩后的位置信息推送到陆海联测指挥部,实现对震源船航速、航向的实时监控.根据3个点位信息计算的实时震源船航向,有助于准确把握震源船在进行固定点悬停激发时的航向,克服了以往使用前后两个时刻推算的平均航向代替实时航向等问题.使用我国自主知识产权的北斗短报文进行通讯,大大提高了系统的安全性、可靠性和稳定性;同时陆海联测指挥部可以实时查看震源船是否按事先设定的测线、事先设定的速度和航向进行作业,有效保障了监控平台对震源船的监控,进一步提升安全生产的风险控制能力.