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合成孔径雷达(SAR)以其高分辨率、能不受雨云影响实施全天时全天候全方位监测,在海面溢油灾害应急监测过程中发挥着越来越重要的作用。溢油是因为海面油膜抑制了毛细波和重力波,在SAR图像上呈暗斑而被识别。然而,海面溢油的乳化过程直接影响SAR对海面溢油后向散射截面的观测精度。本研究以物理海洋学和激光原理以及海面电磁散射理论为基础,通过实验利用激光扫描仪观测海面溢油粗糙度,分别与溢油特征参数、后向散射系数建立对应关系;耦合海面溢油参数与后向散射截面的关系,利用电磁散射数值建模方法,建立海面溢油散射模型,研究海面溢油乳化过程对微波后向散射截面的影响。本项目的研究将为SAR监测海面溢油量、溢油厚度及油品分布格局提供了可能;将进一步揭示海面溢油的散射机制,提高SAR海面监测溢油的精度和能力。 相似文献
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Tremendous economic loss and environmental damages are caused by oil-spilling accidents in sea. Accurate prediction of the underwater spreading and surface drifting of oil spills is important for the emergency response. In the present study, numerical investigation on the underwater spread and surface drift of oil spilled from a submarine pipeline under the combined action of wave and current was carried out to examine the effects of physical ocean environment, leaking flux and spilled oil density and viscosity. Reynolds-Averaged-Navier-Stokes (RANS) equations, realizable k-ε turbulence model and volume of fluid (VOF) model are employed to describe the multiphase flow, and velocity-boundary wave-making technique combined with the sponge layer damping absorber technique realizes the numerical wave flume. Oil spill experiments were conducted to validate the numerical model. The calculation results indicate that compared with the environmental conditions of still water, only current and only wave, a larger scope of underwater spreading and relatively slower rising rate and relatively faster drifting rate of oil droplets are observed under the combined action of wave and current. The leaking flux affects the floating time and dispersion concentration, while the ocean environment affects the horizontal migration and surface drifting. Under the specific conditions of present work, oil density has obvious effect on the underwater spread but limited effect on the surface drifting, while oil viscosity has little effect on both the two processes. 相似文献
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文章以假设发生在南海北部深水油气勘探开发海域的1279m深水井喷溢油事故为例,采用深海溢油输移扩散模型,以2020年冬季（1月）和夏季（7月）三维流场和海面风场为主要环境动力,数值模拟研究未实施和实施海底消油剂处理情景下的溢油在海洋环境中的输移扩散全过程。模拟结果显示:不同深度的海流对溢油输移轨迹有着显著的影响;深水环... 相似文献
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C. Kranenburg 《Applied Ocean Research》1984,6(1):23-30
The rupture of a submarine oil pipeline starts various mechanisms leading to an oil spill. Among these mechanisms the leakage of oil driven by the difference in specific gravities of oil and sea-water is difficult to estimate. A simple mathematical model has been developed and laboratory experiments have been carried out to obtain an insight into the density-driven exchange flow and to determine the leak rate. The mathematical model is predictive and takes account of the effects of friction, inclination of the pipeline, and inertia of the fluid. The experiments were done in a horizontal model pipeline. Theoretical and experimental results are in satisfactory agreement. 相似文献
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采用物理模型试验研究了海流作用下海底输油管道溢油扩散特性。水槽中的原油从管道的泄漏点溢出，并从海底扩散到海面。采用非接触式光学测量技术，设计了图像采集与数据分析系统，研究原油泄漏机理和特性。通过该系统获得了溢油的轨迹、速度和海面上升时间。从定性和定量两个角度分析了水流和溢油量对溢油行为的影响。之后给出了特征物理量对各种因素的敏感性研究。溢油在水下主要以不同粒径的散状颗粒的形式分布。原油的上升过程可分为三个阶段：饱满、分散和聚集阶段。溢油量是影响油粒子上升时间的主要因素。在油粒子的上升过程中，原油的垂直速度主要受溢油量的影响，横向速度则更依赖于水流速度。不同水流与溢油量下，横向溢油速度的偏差远大于海面上升时间和垂直溢油速度的偏差。该研究可为溢油应急预测系统提供理论参考。 相似文献
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溢油事件的发生会给海洋环境的保护和经济发展带来巨大的影响。运用现代化的监测手段和技术进行监测,及时发现溢油现象和违规行为,保护海洋环境是非常重要的。合成孔径雷达（SAR）技术是溢油检测的有效工具,在SAR图像中溢油表现为黑色的区域,但是黑色区域也可能会由其他的因素引起。本文提出了一种基于二维经验模态分解（BEMD）的方法来识别溢油和疑似溢油。首先通过BEMD方法将感兴趣的区域分解为局部窄带的各分量—内蕴模函数（BIMF）之和,并对分解后得到的各分量IMF进行Hilbert变换,通过Hibert谱分析得到64维的特征空间,然后使用Relief方法得到5个特征向量,最后利用马氏距离分类器进行分类。通过实验结果表明,该方法能够有效、准确地检测出溢油,准确率超过90%。 相似文献
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海底石油管线泄漏可能导致海床内部形成高浓度石油污染。在波浪作用下,海床沉积物易发生再悬浮甚至液化失稳现象,进而导致海床内部石油类污染物通过多种途径向水体再次释放并在土体内部发生迁移,造成更大范围的石油扩散。本研究以总石油烃（Total Petroleum Hydrocarbon,TPH）设为代表性污染物,将污染泥浆以椭球状埋设在沉积物内部,采用波浪水槽试验研究不同强度波浪作用下TPH向上覆水体的释放规律及在沉积物内部的迁移规律。结果表明,在沉积物静置固结阶段前期,TPH随孔隙水由沉积物向上覆水体迁移释放,固结阶段前期TPH向上覆水体的释放量高于后期。在波浪作用未引起沉积物液化阶段,波浪促进石油类污染物向水体释放的作用较弱,由于悬浮泥沙对石油类污染物的吸附作用,水体中石油类污染物的浓度略低于静置固结阶段。在波浪作用引起沉积物液化阶段,随着悬浮泥沙浓度升高,TPH向上覆水体释放量加大;TPH在沉积物内部垂向迁移及平面扩散迁移距离加大,平面迁移距离大于垂向迁移距离,垂向扩散深度与液化深度基本一致,污染土体体积占比约为土体未液化时的3倍。 相似文献
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海面溢油事故发生后需要进行精准的溢油检测,从而为溢油事故现场应急响应与海面污染的快速有效处理提供支持。利用哨兵二号(Sentinel-2)多光谱遥感影像,基于最佳指数因子(OIF)构建光谱特征指数提取海面溢油光谱特征,基于灰度共生矩阵(GLCM)提取海面溢油纹理特征,利用巴氏距离法进行特征选择,构建海面溢油空谱特征数据集。提出了多核决策融合支持向量机(SVM)海面原油检测模型,对黄海4.27“交响乐号”油轮溢油事故开展检测研究。实验结果表明,在复杂水色背景下,经过筛选的最佳空谱特征数据集检测结果的总体精度可以达到89.25%,Kappa系数为0.854,F1分数可达0.889;多核SVM决策融合后的总体精度可以达到90.26%,Kappa系数为0.866,F1可达0.898,总体精度较单核提高了1.0%~8.2%,Kappa系数提高了0.013~0.122,F1分数提高了0.009~0.097。实验结果表明,海面溢油检测模型具有较高的溢油检测精度和模型鲁棒性,适用于海面溢油检测研究。 相似文献
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水下溢油数值模拟研究 总被引：1，自引：0，他引：1
基于Lagrange积分法和Lagrange粒子追踪法建立了一个水下溢油数值模型。该溢油模型由两个子模型组成:羽流动力模型和对流扩散模型,其中羽流动力模型用以模拟溢油的喷发阶段和浮力羽流阶段;对流扩散模型用以模拟溢油的对流扩散阶段。通过数值实验,结合实验室水槽实验和水下溢油现场实验的观测资料进行模型验证。实验结果表明,模拟结果与观测资料一致性较好,从而验证了本文溢油模型的合理性和准确性;羽流动力模型为对流扩散模型提供源,海流、海水的垂向密度结构和油滴的直径分布是影响溢油在对流扩散阶段运动和分布的主要因素。 相似文献
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海上环境变化多端,造成溢油的漂移和扩散会出现不可预测的情况,精确、实时地监测海上溢油是现今亟待解决的问题.无人机以其部署快、成本低、环境适应性强的优势在海上溢油监测领域得到重视,但单架无人机监测能力弱,而多架无人机监测的准确性仍需提高.为此,本文提出一种无人机群海面溢油自动导航跟踪监测的架构和方法,根据海上溢油浓度的变化进行路径规划.该方法包括建立溢油模型和设计无人机跟踪控制系统.溢油模型主要描述海上溢油时空变化的形态复杂性;控制器可控制无人机追踪和监测溢油漂移及扩散的情况.同时,将无人机跟踪控制系统与人工势场法相结合,避免无人机相撞.最后,进行数值仿真,结果表明该跟踪系统与溢油的重合率达到70％～80％,验证了该方法的可行性.未来,该系统可广泛应用于无人机群对不同环境现象和灾害的跟踪监测. 相似文献
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This study extends previous two-dimensional research [Wang, S.D., Shen, Y.M., Zheng, Y.H., 2005. Two-dimensional numerical simulation for transport and fate of oil spills in seas. Ocean Engineering 32, 1556–1571] to three dimensions in order to investigate the vertical dispersion/motion of the spilled oil slick, which is a more realistic model of the motion of the spilled oil. To this end, a three-dimensional (3-D) model, based on the particle approach, is developed for simulating oil spill transport and fate in seas. The amount of oil released at sea is distributed among a large number of particles tracked individually. These particles are driven by a combination of water current, wave- and wind-induced speed and move in a 3-D space. Horizontal and vertical diffusion are taken into account using a random walk technique. The model simulates the most significant processes which affect the motion of oil particles, such as advection, surface spreading, evaporation, dissolution, emulsification, turbulent diffusion, the interaction of the oil particles with the shoreline, sedimentation and the temporal variations of oil viscosity, density and surface tension. In addition, the processes of hydrolysis, photo-oxidation and biodegradation are also considered in this model. The model has been applied to simulate the oil spill accident in the Bohai Sea. 相似文献
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海面发生大面积溢油事故时,由于太阳耀斑区的存在,海面的油膜在遥感影像上会发生明暗的变化。这对溢油的检测会产生严重的干扰。如何在海面太阳耀斑区准确地检测出溢油是目前溢油检测的难题。针对这一问题,本文利用Landsat7 ETM+多光谱影像数据,开展了基于卷积神经网络(CNN)的海面太阳耀斑区溢油检测方法研究。通过设置对照实验,对比支持向量机、最大似然、随机森林等分类方法,我们发现在相同实验条件下CNN模型的分类精度为95%～99%, Kappa系数为0.92～1,均高于其他三种分类方法,表明了CNN模型在海面太阳耀斑区溢油的检测具有更高的精度与一致性。 相似文献
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全极化SAR图像中溢油极化特征研究 总被引：2，自引：1，他引：1
相比于单极化SAR图像,全极化SAR图像不仅能体现海面目标的几何特征、后向散射特征,还能体现目标的极化特征。因此,在溢油检测方面,极化SAR更具优势。特征提取作为溢油检测的关键步骤,直接影响到溢油检测的精度。在本文中,我们分析了全极化SAR图像中海面溢油的极化特征,如极化散射熵、平均散射角等。并提出了新的极化特征P,该特征参数能够反映海面目标电磁散射过程中布拉格散射机制和镜面散射机制的比例。为了研究极化特征溢油检测的能力,本文基于SIR-C/X-SAR和Radarsat-2全极化SAR图像开展了相关实验,并对比分析了溢油的多种极化特征。实验结果显示,在中低风速情况下,C波段溢油探测效果优于L波段;本文提出的极化特征P对海面散射机制敏感;基准高度和特征参数P在C波段比其他极化特征更适于溢油检测。 相似文献
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浮冰界面融化速率参数化方案的实验室研究 总被引：2，自引：2，他引：0
融冰季节时天然浮冰表面、底面和侧向融化共存,三者融化速率是底面大于侧向,侧向大于表面。而且浮冰尺寸越小,侧向速率占比越高。为了解决将小尺度浮冰块尺度指标计入融化参数化方案,在低温环境实验室无辐射、无流速、控制气温和水温条件下对天然海冰和人工冻结淡水冰的圆盘试样,开展了不同初始水温和不同初始直径的圆盘试样融化过程实验,获得了圆盘试样直径、厚度和质量融化过程。依据这些实验数据,构建试样直径厚度比这一新指标,通过物理分析和数学统计手段,建立了海冰和淡水冰试样表面、底面融化速率同温度梯度,侧向融化速率和温差以及直径厚度比的关系式。这些关系式能够应用于天然直径100 m范围内浮冰的融化参数化方案,期望能解决北冰洋海冰和入海口近岸淡水冰夏季融化季节能量和质量平衡数值模拟的需求。 相似文献
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南沙海域深水区表层沉积物声速与孔隙度相关关系 总被引：5，自引：0，他引：5
采用同轴差距测量法对南沙海域海底沉积物取样进行甲板现场声学测量,并计算了声速,结合沉积物取样分析获得的孔隙度,研究了海底表层100 cm沉积物的声速和孔隙度之间的相互关系,分析获得了孔隙度与海底沉积物声速关系的经验公式,结果表明此海域沉积物声速的临界孔隙度为67.80%。将此公式与国内外其他学者建立的经验公式进行了对比,分析发现各预测方程之间存在着差异,说明不同海域的沉积物声学特性具有差异性,经验公式具有区域性,同时分析了其他影响声速的因素。此项工作对于建立和研究南海声场环境具有重要意义。 相似文献
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Accurate detection of an oil spill is of great significance for rapid response to oil spill accidents. Multispectral images have the advantages of high spatial resolution, short revisit period, and wide imaging width, which is suitable for large-scale oil spill monitoring. However, in wide remote sensing images, the number of oil spill samples is generally far less than that of seawater samples. Moreover, the sea surface state tends to be heterogeneous over a large area, which makes the identification of oil spills more difficult because of various sea conditions and sunglint. To address this problem, we used the F-Score as a measure of the distance between forecast value and true value, proposed the Class-Balanced F loss function (CBF loss function) that comprehensively considers the precision and recall, and rebalances the loss according to the actual sample numbers of various classes. Using the CBF loss function, we constructed convolution neural networks (CBF-CNN) for oil spill detection. Based on the image acquired by the Coastal Zone Imager (CZI) of the Haiyang-1C (HY-1C) satellite in the Andaman Sea (study area 1), we carried out parameter adjustment experiments. In contrast to experiments of different loss functions, the F1-Score of the detection result of oil emulsions is 0.87, which is 0.03–0.07 higher than cross-entropy, hinge, and focal loss functions, and the F1-Score of the detection result of oil slicks is 0.94, which is 0.01–0.09 higher than those three loss functions. In comparison with the experiment of different methods, the F1-Score of CBF-CNN for the detection result of oil emulsions is 0.05–0.12 higher than that of the deep neural networks, supports vector machine and random forests models, and the F1-Score of the detection result of oil slicks is 0.15–0.22 higher than that of the three methods. To verify the applicability of the CBF-CNN model in different observation scenes, we used the image obtained by HY-1C CZI in the Karimata Strait to carry out experiments, which include two studies areas (study area 2 and study area 3). The experimental results show that the F1-Score of CBF-CNN for the detection result of oil emulsions is 0.88, which is 0.16–0.24 higher than that of other methods, and the F1-Score of the detection result of oil slicks is 0.96–0.97, which is 0.06–0.23 higher than that of other methods. Based on all the above experiments, we come to the conclusions that the CBF loss function can restrain the influence of oil spill and seawater sample imbalance on oil spill detection of CNN model thus improving the detection accuracy of oil spills, and our CBF-CNN model is suitable for the detection of oil spills in an area with weak sunglint and can be applied to different scenarios of CZI images. 相似文献