Palynological records of Holocene monsoon change from the Gulf of Tonkin (Beibuwan), northwestern South China Sea

Palynological records in cores C4 and B106 from the Gulf of Tonkin reveal signals of paleo-monsoon and paleoenvironmental change during the late Pleistocene and Holocene. Before ∼ 13.4 cal kyr BP, the Gulf of Tonkin was exposed to the atmosphere and covered by grassland. Starting at ∼ 11.7 cal kyr BP, the Gulf of Tonkin was inundated by brackish water, indicated by the appearance of the brackish algae Cleistosphaeridium, Sentusidinium and Spiniferites, a decrease of herb content, and an increase of Pinus. After Hainan Island was completely separated from the Leizhou Peninsula by Qiongzhou Strait at ∼ 8.5 cal kyr BP, a continuous marine sedimentary environment was found. The current patterns were similar to those of the present, with a general trend of current homogenization reflected by gradually decreasing quantities of Quercus pollen and a narrowing gap between the palynological concentrations of the southern and northern parts of the region. The data suggest that three short periods of strengthened winter monsoons and currents were centered at ∼ 6.0 cal kyr BP, ∼ 2.7 cal kyr BP and ∼ 0.2 cal kyr BP and that two short periods of strengthened summer monsoons and currents were centered at ∼ 7.5 cal kyr BP and ∼ 3.4 cal kyr BP.     

陆相地层的层序地层学:层序的特征与模式

陆相地层的层序地层学研究近年来取得了较大的进展,尤其在层序的组成、层序界线和最大洪泛面的识别以及层序模式的建立等方面研究得比较深入。层序地层的组成包括层序组合和层序的内部单元。层序界线和最大洪泛面的识别是陆相地层层序地层学研究的关键。目前已初步建立了箕状断陷盆地、坳陷盆地以及冲积地层背景的层序模式。     

黄河万家寨水库右岸岩溶渗漏问题

<正> 万家寨水库位于黄河北干流上游段,坝型为混凝土重力坝,坝高约90米,总库容约9.7亿立方米。从五十年代起直到八十年代初,几个单位对本工程曾相继进行了大量地质勘察工作,并对其作过初步设计。三十年来,各家一致认为万家寨水库不存在永久性岩溶渗漏。本文就此问题谈一点认识,不妥之处,欢迎批评指正。      

南极半岛邻近海域夏季POC分布特征及其影响因素

海水中的颗粒有机碳(POC)与生物的生命过程、初级生产力关系密切,是海洋食物链中重要的物质基础和能量来源,因此POC的分布特征可以有效反映其生物地球化学环境。利用中国第33次南极考察期间(2016年12月至2017年1月)在南极半岛邻近海域采集的海水颗粒物样品,研究POC的空间分布特征及其影响因素。结果表明,斯科舍海0—200 m的POC浓度范围为7.44—193.52μg·L~(-1),平均浓度为(48.84±35.09)μg·L~(-1);南斯科舍海岭0—200 m的POC浓度范围为9.13—62.17μg·L~(-1),平均浓度为(29.76±14.12)μg·L~(-1);鲍威尔海盆0—200 m的POC浓度范围为5.87—270.72μg·L~(-1),平均浓度为(48.57±38.92)μg·L~(-1)。表层POC高值出现在斯科舍海区和鲍威尔海盆区,而低值出现在海岭区,与叶绿素a(Chla)的变化趋势一致,与营养盐的变化趋势相反。垂向分布上,各个区域POC平均浓度随深度的增加而减少,鲍威尔海盆和斯科舍海POC最高值都出现在25 m层。分析结果表明光合浮游植物是研究海域POC的主要来源, POC的主要影响因素为温度、水团混合以及海冰环境。斯科舍海与鲍威尔海盆整体非生命POC占比高,可能是由于高磷虾生物量、海冰碎屑以及陆源输入的干扰;南斯科舍海岭整体非生命POC占比低。     

Quantitative analysis of the impact factors of conventional energy carbon emissions in Kazakhstan based on LMDI decomposition and STIRPAT model

Quantitative analysis of the impact factors in energy-related CO₂ emissions serves as an important guide for reducing carbon emissions and building an environmentally-friendly society. This paper aims to use LMDI method and a modified STIRPAT model to research the conventional energy-related CO₂ emissions in Kazakhstan after the collapse of the Soviet Union. The results show that the trajectory of CO₂ emissions displayed U-shaped curve from 1992 to 2013. Based on the extended Kaya identity and additive LMDI method, we decomposed total CO₂ emissions into four influencing factors. Of those, the economic active effect is the most influential factor driving CO₂ emissions, which produced 110.86 Mt CO₂ emissions, with a contribution rate of 43.92%. The second driving factor is the population effect, which led to 11.87 Mt CO₂ emissions with a contribution rate of 4.7%. On the contrary, the energy intensity effect is the most inhibiting factor, which caused–110.90 Mt CO₂ emissions with a contribution rate of–43.94%, followed by the energy carbon structure effect resulting in–18.76 Mt CO₂ emissions with a contribution rate of–7.43%. In order to provide an in-depth examination of the change response between energy-related CO₂ emissions and each impact factor, we construct a modified STIRPAT model based on ridge regression estimation. The results indicate that for every 1% increase in population size, economic activity, energy intensity and energy carbon structure, there is a subsequent increase in CO₂ emissions of 3.13%, 0.41%, 0.30% and 0.63%, respectively.     

1995年世界气候概况

１９９５年，世界气候普遍偏暖。欧亚大陆大部、北美大陆中部、南美大陆中部、非洲西北部的年平均气温大多偏高１－２℃，其中俄罗斯大部、美国西南部显著偏高，部分地区偏高４℃，北半球中高纬度地区冬暖非常显著。由于北半球中纬度地区，许多测站气温高达３５－４０℃，为近几十年乃至百年一遇。年内，全球许多地区旱涝频繁，诸如年初欧洲的暴风雪与美国加利福尼亚洲的特大洪水及夏季朝鲜、与中南半岛的大水灾、加勒比海的飓风等危     

海上风电电磁辐射对海洋生物影响的研究综述

海上风电工程是我国解决能源危机的新举措。为更好地推动我国海上风电建设与海洋生态环境保护的和谐发展,文章综述海上风电的电磁辐射对海洋生物的影响。已有的研究表明:海上风电对海洋生物的电磁辐射影响的主要来源是海底电缆,电磁辐射产生影响主要是因为某些海洋生物具有磁敏感性;磁场可能影响鱼类迁徙等海洋生物运动,且足够强度的磁场还会影响海洋生物的生殖和发育等生理过程;随着海上风电的快速发展,深远海海上风电的电磁辐射影响亟待更多的研究。     

2010年10月24日周口太康MS4．6地震的震源机制解

利用河南省区域地震台网的近震波形资料,采用近震时间域方法,反演2010年10月24日16点58分太康 MS 4．6地震的地震矩张量,结果显示,该地震为走滑型地震。结合震后灾害考察,认为太康地震发生在周口-太康断裂上。     

地震灾害风险评估及减灾策略研究——以贵州罗甸县为例

为探究贵州省地震灾害风险薄弱环节,减轻地震灾害风险,以贵州省罗甸县为示范,采用地震危险性概率分析方法对各乡镇进行危险性分析,开展地震灾害承灾体现场抽样调查,通过层次分析法和问卷调查的方式,首次构建乡镇级别的地震灾害风险和减灾能力指标体系,评估各乡镇地震灾害综合指数和程度,计算各乡镇地震灾害风险指数,确定红水河镇为高风险区、罗悃镇为中风险区、木引等7个乡镇为低风险区,并从建筑物设防、地震地质灾害及水库地震等角度剖析罗甸县地震灾害特点,从农村危房改造、移民搬迁、地质灾害防护及交通等方面提出减小地震灾害风险的建议。     

Nonlinear inversion of electrical resistivity imaging using pruning Bayesian neural networks

Conventional artificial neural networks used to solve electrical resistivity imaging (ERI) inversion problem suffer from overfitting and local minima. To solve these problems, we propose to use a pruning Bayesian neural network (PBNN) nonlinear inversion method and a sample design method based on the K-medoids clustering algorithm. In the sample design method, the training samples of the neural network are designed according to the prior information provided by the K-medoids clustering results; thus, the training process of the neural network is well guided. The proposed PBNN, based on Bayesian regularization, is used to select the hidden layer structure by assessing the effect of each hidden neuron to the inversion results. Then, the hyperparameter α _k , which is based on the generalized mean, is chosen to guide the pruning process according to the prior distribution of the training samples under the small-sample condition. The proposed algorithm is more efficient than other common adaptive regularization methods in geophysics. The inversion of synthetic data and field data suggests that the proposed method suppresses the noise in the neural network training stage and enhances the generalization. The inversion results with the proposed method are better than those of the BPNN, RBFNN, and RRBFNN inversion methods as well as the conventional least squares inversion.