沉积物粒度组分空间预测的神经网络残余kriging方法

针对近海表层沉积物粒度组分空间变异的尺度差异性,提出了基于广义回归神经网络残余kriging的沉积物粒度组分空间预测方法,并以海州湾沉积物粒度数据为基础,分析了其在沉积物粒度组分空间预测和底质类型制图中的应用效果。结果表明,广义回归神经网络残余kriging方法能够获得比普通kriging方法更高的沉积物粒度组分空间预测精度,并且其底质类型的总体空间预测精度达到85%以上,相应的Kappa系数也在0.8以上,显示底质制图的预测类型与样本的实测类型具有较高的一致性。新方法对于开展定量化的沉积物粒度组分空间预测和底质类型制图具有参考价值。     

海洋底质类型图矢量化智能制作技术研究

海洋底质类型图是海洋学研究重要的基础图件之一。针对现有海洋底质类型图制作方法存在的问题,本文利用随机森林算法,对作图数据网格点处的底质类型进行预测,并基于ArcGIS矢量操作功能改进了底质类型面要素生成与平滑方法,解决了常规成图方法存在的锯齿状边界、后期修改不方便等问题,开发了海洋底质类型图矢量化智能制作技术。利用渤海区域沉积物粒度数据开展应用试验,结果表明,在缺乏沉积物组分信息的情况下,该方法可以获得较好的制图结果,具有一定的实用价值,是对传统底质类型图制作方法的补充与改进。     

常用沉积物粒度分类命名方法探讨

关于含砾沉积物的粒度分类命名和以单一粒级组分命名的沉积物（主要为砂）的进一步细分命名,在908《海洋底质调查技术规程》和最新的《海洋调查规范》中均没有详细规定。根据各规范的描述,都采用无砾沉积物的粒度分类命名方法对含砾石沉积物进行命名,未能真实反映沉积物的含砾特征;对于单一粒级组分命名的沉积物没有进一步细分命名,也不能直观地、更清楚地显示其沉积特征。通过查阅文献进行对比分析后,认为宜采用含砾福克法对含砾沉积物进行粒度分类命名和采用把砂分成粗砂、中砂、细砂3级后按照优势粒级法进一步细分命名,更能真实展示沉积物粒度特征。     

南海南部表层沉积物粒度分布特征及其影响因素

对取自南海南部的153个表层沉积物样品,进行了粒度、粒度参数计算和Q型聚类分析,探讨了表层沉积物的粒度分布特征、沉积类型及影响因素。结果表明,研究区沉积物主要粒级组分为黏土和粉砂,底质类型主要为泥,还有粉砂质砂、砂质粉砂和粉砂,平行于等深线基本呈带状分布;结合Q型聚类分析结果,研究区沉积物可分为外陆架及陆架坡折区粗粒沉积物(砂和粉砂)和陆坡区细粒沉积物(泥)两类,500m等深线是两类沉积物的分界线;对比粒度分析结果,判定两类沉积物属于不同成因类型,外陆架及陆架坡折区是现代沉积和残留沉积的混合沉积,陆坡区是现代沉积;影响南海南部表层沉积物粒度分布的主要因素是物源、水动力条件、水深与地形及洋流。     

苏北废黄河三角洲海域表层沉积物粒度的空间变异和分布特征

在苏北废黄河三角洲海域采集了266个表层沉积物样品,经粒度分析获得各样品粘土、粉砂和砂3种粒级组分的质量百分数,在此基础上运用地统计学方法和GIS软件分析了粘土、粉砂和砂3种粒级组分的空间变异特征,根据空间变异特征研究结果,用Kriging插值方法绘制了各粒级组分的空间分布图.结果表明：废黄河三角洲表层沉积物中砂组分具有较强的空间自相关性,其自相关距离为30.5 km;粘土和粉砂具有中等的空间自相关性,其自相关距离分别为40.3 km和13.8km.在大于17.0 km的尺度上,3种粒级组分的空间变异性表现出明显的各向异性特征,且以NE55°和SE145°两组相互垂直的方向上差异最为明显,这是由于波浪、潮流等因素作用所引起的沉积物离岸梯度变化造成的.制图结果显示,废黄河三角洲表层沉积物3种粒级组分的空间分布状况,粘土表现为近岸低、远岸高;砂表现为近岸高、远岸低,高值区主要在河口附近;粉砂表现为均匀性特征.     

海州湾表层沉积物粒度的空间变异特征

在海州湾海域采集了表层沉积物60个,经实验室分析获得了各采样点不同粒级组分的质量分数,在此基础上运用地统计学方法分析了砂、粉砂和黏粒三组分的空间变异特征,并利用Kriging插值方法绘制了相应的沉积物分布图。结果表明,海州湾表层沉积物中砂和黏粒组分均具有较强的空间自相关性,其空间自相关距离分别达到15 km和28 km,而粉砂则具有中等的空间自相关性,自相关距离在12 km左右;在大于9 km的尺度上,3种粒级沉积物的空间变异性表现出明显的各向异性特征。制图结果表明,研究海域沉积物的总体组成状况是北部偏砂、南部偏黏、中部以粉沙为主。研究结果对连云港航道疏浚、整治及近海水产养殖规划等工作具有一定指导意义。     

南黄海中西部表层沉积物粒度特征分析

对南黄海中西部海域316个表层沉积物进行了粒度分析、粒度参数计算和多元统计分析,探讨了沉积物的粒度分布特征、搬运方式和沉积环境。结果表明,粉砂是南黄海中西部海域表层沉积物的优势粒级,含量高达47%,主要粒级范围是1.14~10.13Ф,核心粒级范围是3.92~7.44Ф,底质类型主要是砂质粉砂、粉砂质砂、砂质泥和粉砂。粒度R型聚类分析得出4类,第1类由5.5~10.5Ф粒级组成,第2类由-0.84~-3.2Ф粒级组成,即粗、细两端元粒级组分,第3类是3.5~4.5Ф,第4类为1.5~2.5Ф,分别对应于粉砂质砂(或泥质砂)和砂;Q型聚类分析根据不同的阈值可分为4类、6类或8类,各类归类良好,图形清晰。典型站位的分析表明,研究区的主控因素是黄海沿岸流、潮流沙脊区往复流及苏北沿岸流,各粒级组分的分布、粒度参数特征、搬运方式与海流的强弱和方向有着密切的相关性。     

海南岛东南浅海表层沉积物粒度特征及沉积环境

对海南岛东南浅海377组表层沉积物样品(平均水深112.3 m)开展粒度测定及沉积物类型划分,并运用统计规律及沉积输运趋势分析,探讨了底质沉积物分布格局与物质来源及沉积动力环境关系.结果表明,海南岛东南浅海沉积物类型复杂,共分布13种沉积物类型,粒度组成以粉砂和砂为主,平均粒径均值为5.73Φ,优势粒级为细粉砂级(6～...     

山东半岛近海不同粉砂粒级含量分布的空间差异性及其沉积学意义

粉砂是现代陆架和河口沉积物最主要的粒级组分,但由于其复杂的沉积动力学行为,我们对于不同粉砂粒级的沉积学特征和行为并没有完全掌握。本文分析了山东半岛近海表层沉积物中不同粉砂粒级组分含量的空间分布特征,探讨了其控制因素。结果表明,山东半岛周边近海沉积物类型以泥质沉积为主,但不同粉砂粒级组分具有明显的空间分布差异性。极细粉砂、细粉砂与黏土组分的含量变化一致,表明了其相似的沉积动力学行为。粗粉砂组分的含量空间变化最大,在山东半岛的北部泥质条带、东部“泥楔”和南部近岸具有明显的高含量,其含量与其他各粉砂组分具有明显的负相关性。从表层沉积物的粒度组分与分布规律,可以证明研究区表层沉积物主要以黄河携带物质为主,但流入本区的一些中小型河流也明显提供了部分物源,只是其提供物质的扩散范围有限。结合水动力分析和物理海洋学等方面的认识,我们认为造成不同粉砂组分空间分布差异性的原因主要受物源、区域水动力学条件、局部地形（底形）等因素控制。另外,还发现了山东半岛“泥楔”沉积物的粗粉砂组分含量偏高、现代沉积速率也偏大,这些现象在空间上与“海洋锋面”位置一致,对于海洋锋面的沉积学意义还需要进一步的调查研究和数值模拟工作。     

海南洋浦湾沉积物中生源组分的粒度效应

利用激光粒度分析仪,对2005年夏季采集于海南岛洋浦湾的B1168柱状样品进行了粒度分析测试,并利用同位素比质谱仪,对其总有机碳(TOC)和总氮(TN)的含量进行了测定,同时,利用连续提取法提取了沉积物中的生物硅(BSi),并利用分光光度计进行了测量。通过对上述生源组分与粒度间相关性以及人类影响因子的分析,探讨了人类活动对沉积物中生源组分含量的影响及生源组分的粒度效应,结果表明:从B1168柱状样品的45 cm处起至表层,人类活动明显影响了生源组分的含量,除此之外,生源组分含量还较显著地受粒度控制,沉积物组分的粒度越小,生源组分的含量则越高,但是当沉积物粒径小于0.5μm的时候,TN和BSi含量与其粒级含量的相关性变弱。φ5粒级与各生源组分的含量均呈弱相关,这一结果与该海区表层沉积物粒度组分与TOC、TN含量相关性分析的结果相一致。     

一种近海底质类型图生成的非参数方法

近海底质类型图在近海工程和经济活动中有着广泛的应用价值.针对传统制图方法中存在的问题,提出了一种基于非参数指示Kriging的底质类型图生成方法.该方法能够有效地规避制图过程中的主观性,且对取样数据的平稳性和统计分布没有特殊要求,并能对制图结果的不确定性进行定量评价.该方法在连云港南部海域的应用实践表明,在相同的条件下...     

一种近海底质类型图生成的非参数方法

近海底质类型图在近海工程和经济活动中有着广泛的应用价值。针对传统制图方法中存在的问题,本文提出了一种基于非参数指示Kriging的底质类型图生成方法。该方法能够有效地规避制图过程中的主观性,且对取样数据的平稳性和统计分布没有特殊要求,并能对制图结果的不确定性进行定量评价。该方法在连云港南部海域的应用实践表明,在相同的条件下,该方法可获得比传统方法更为精确地的制图结果,具有一定的实用价值。     

Mapping Submerged Aquatic Vegetation with GIS in the Caloosahatchee Estuary: Evaluation of Different Interpolation Methods

This article evaluates different spatial interpolation methods for mapping submerged aquatic vegetation (SAV) in the Caloosahatchee Estuary, Florida. Data used for interpolation were collected by the Submersed Aquatic Vegetation Early Warning System (SAVEWS). The system consists of hydro-acoustic equipment, which operates from a slow-moving boat and records bottom depth, seagrass height, and seagrass density. This information is coupled with geographic location coordinates from a Global Positioning System (GPS) and stored together in digital files, representing SAV status at points along transect lines. Adequate spatial interpolation is needed to present the SAV information, including density, height, and water depth, as spatially continuous data for mapping and for comparison between seasons and years. Interpolation methods examined in this study include ordinary kriging with five different semivariance models combined with a variable number of neighboring points, the inverse distance weighted (IDW) method with different parameters, and the triangulated irregular network (TIN) method with linear and quintic options. Interpolation results were compared with survey data at selected calibration transects to examine the suitability of different interpolation methods. Suitability was quantified by the determination coefficient (R2) and the root-mean-square error (RMSE) between interpolated and observed values. The most suitable interpolation method was identified as the one yielding the highest R2 value and/or the lowest RMSE value. For different geographic conditions, seasons, and SAV parameters, different interpolation methods were recommended. This study identified that kriging was more suitable than the IDW or TIN method for spatial interpolation of all SAV parameters measured. It also suggested that transect data with irregular spatial distribution patterns such as SAV parameters are sensitive to interpolation methods. An inappropriate interpolation method such as TIN can lead to erroneous spatial representation of the SAV status. With a functional geographic system and adequate computing power, the evaluation and selection of interpolation methods can be automated and quantitative, leading to a more efficient and accurate decision.     

Mapping Submerged Aquatic Vegetation with GIS in the Caloosahatchee Estuary: Evaluation of Different Interpolation Methods

This article evaluates different spatial interpolation methods for mapping submerged aquatic vegetation (SAV) in the Caloosahatchee Estuary, Florida. Data used for interpolation were collected by the Submersed Aquatic Vegetation Early Warning System (SAVEWS). The system consists of hydro-acoustic equipment, which operates from a slow-moving boat and records bottom depth, seagrass height, and seagrass density. This information is coupled with geographic location coordinates from a Global Positioning System (GPS) and stored together in digital files, representing SAV status at points along transect lines. Adequate spatial interpolation is needed to present the SAV information, including density, height, and water depth, as spatially continuous data for mapping and for comparison between seasons and years. Interpolation methods examined in this study include ordinary kriging with five different semivariance models combined with a variable number of neighboring points, the inverse distance weighted (IDW) method with different parameters, and the triangulated irregular network (TIN) method with linear and quintic options. Interpolation results were compared with survey data at selected calibration transects to examine the suitability of different interpolation methods. Suitability was quantified by the determination coefficient (R2) and the root-mean-square error (RMSE) between interpolated and observed values. The most suitable interpolation method was identified as the one yielding the highest R2 value and/or the lowest RMSE value. For different geographic conditions, seasons, and SAV parameters, different interpolation methods were recommended. This study identified that kriging was more suitable than the IDW or TIN method for spatial interpolation of all SAV parameters measured. It also suggested that transect data with irregular spatial distribution patterns such as SAV parameters are sensitive to interpolation methods. An inappropriate interpolation method such as TIN can lead to erroneous spatial representation of the SAV status. With a functional geographic system and adequate computing power, the evaluation and selection of interpolation methods can be automated and quantitative, leading to a more efficient and accurate decision.     

振荡流层移输沙条件下悬沙层泥沙扩散系数垂向分布特征研究

层移输沙是海岸带泥沙运动的主要形式之一,其垂向悬沙浓度分布规律的研究一直是海岸工程关心的重点。一般情况下,经典的纯扩散模型被用来描述和解释悬沙浓度的试验数据,该模型认为周期平均悬沙浓度主要由参考浓度、泥沙沉速和泥沙扩散系数确定。泥沙扩散系数可以由泥沙沉速和悬沙浓度的垂向梯度反演得到。既往研究大多直接给出泥沙扩散系数的结果,对于不同反演计算方法间结果差别的研究较少。本研究汇总了已有振荡流层移输沙试验数据,采用曲线拟合方法和直接差分方法计算了相应的泥沙扩散系数,研究表明两种方法得到的计算结果在垂向位置z 0.15 m处差异不大,随着垂向位置的升高,差分方法的计算结果略微大于拟合方法。考虑到拟合方法可以得到连续的泥沙扩散系数垂向分布,本研究推荐使用幂函数形式的曲线拟合方法求解悬移泥沙扩散系数。基于此,对比分析了层移输沙悬沙层泥沙扩散系数随泥沙粒径、振荡流周期、均方根流速和振荡流类型等物理参数的变化规律。在纯振荡流层移输沙条件下,泥沙扩散系数随泥沙粒径的增大而增大,而振荡流周期和均方根流速几乎不影响泥沙扩散系数。在振荡流和定常流共同作用下,泥沙扩散系数受振荡流周期和定常流流速的影响,泥沙扩散系数随着振荡流周期的增大或定常流流速的减小而增大。     

黄河三角洲埕北海域底质沉积物粒度特征及泥沙输运分析

通过黄河三角洲埕北海域表层沉积物的粒度资料、实测潮流数据以及50 a一遇的波浪要素,分析了埕北海域表层沉积物的分布类型以及粒度参数特征,并计算了表层沉积物的临界起动应力、日平均单宽输沙通量以及波流共同作用下的单宽推移质输沙率。结果表明:研究区表层沉积物主要是粉砂质砂、砂质粉砂、粉砂和泥四种,分选中等偏差,近对称到正偏,中等尖锐到尖锐峰态;对大潮期间埕北海域潮流底应力和表层沉积物的临界起动应力的计算说明潮流在大部分区域具有起动和输运泥沙的作用;秋季潮流作用下的日平均单宽输沙通量均值为372.32 kg/（m·d）,方向为涨潮流向。利用贝克尔（Bijker）和经典的Van Rijn公式计算出的50 a一遇的波流共同作用下单宽推移质日平均净输沙率结果近似相等。     

Imaging spectroscopy as a tool to study sediment characteristics on a tidal sandbank in the Westerschelde

This paper focuses on the use of imaging spectroscopy for the mapping of sediment characteristics on a tidal sandbank in the Westerschelde, called the Molenplaat. On June 8, 2004, during low tide, a HyMap™ scanner recorded the Molenplaat at 4 m pixel resolution. The hyperspectral data were radiometrically calibrated, geometrically corrected, and atmospherically corrected to give apparent surface reflectance data. On the calibrated and corrected dataset a supervised binary classification was performed, based on linear discriminant analysis. Simultaneous to the flight, 25 sediment samples were collected in the field and analysed in the lab to define the median grain size, the water content, the total organic matter content and the chlorophyll-a concentration. These four parameters play a crucial role in sediment stability and macrofaunal habitat definition. Prior to the classification, a feature selection, based on sequential floating forward search (SFFS), was performed. For each of the four parameters two to three bands were retained for the classification. These bands were most frequently selected in the visible and near infrared parts of the spectrum, except for the organic matter content where also SWIR bands were used. The overall classification accuracy was highest for the water content (88%), the median grain size (88%) and the chlorophyll-a concentration (84%). The organic matter content, for which three instead of two classes were distinguished, scored somewhat lower but still reached 80%. The classifications were limited to a small number of classes in order to obtain reliable statistics with a small number of training samples. The spatial patterns in the classified images indicated that the four parameters under study are highly correlated. In most cases coarse sediment coincided with dry conditions, low organic matter and low concentrations of chlorophyll-a. The wet and muddy parts of the Molenplaat were in general characterised by a notably higher amount of organic matter and chlorophyll-a. The individual classification results for the median grain size, the water content and the chlorophyll-a concentration were combined to generate a sediment ecotope map. The presented study illustrates how airborne hyperspectral data can be used to achieve accurate classified maps of intertidal sediment ecotope types, applying feature selection and a binary classification approach.     

Spatial analysis of grain size in Santa Monica Bay

Maps are useful scientific tools for presenting environmental information, but the statistical techniques necessary to prepare scientifically rigorous maps have primarily focused on terrestrial habitats. This study compares three popular techniques (triangulation, kriging, and co-kriging) to map sediment grain size in Santa Monica Bay, California. Two grain size data sets, one collected in 1994 (79 sites) and one collected in 1997 and 1998 (149 sites) were used for model development. A bathymetric data set collected in 1997 was used as a model covariate. A third grain size data set (40 sites) collected in 1996 from independent sites was used for model evaluation. Predictions were compared to validation data by average difference, prediction mean square error (PMSE), and a goodness-of-prediction measure, G. The average difference between prediction and truth was similar for all methods, but the PMSE for triangulation was more than twice that for kriging or co-kriging, which were similar. The G measure also shows triangulation to be a far worse predictor than kriging and co-kriging. Small-scale differences were observed between kriging and co-kriging at steep depth contours, where co-kriging predicted values commensurate with the expected depth-defined grain size.