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1.
陈莹  赵辉 《海洋学研究》2021,39(3):84-94
本文使用2003年1月—2019年12月MODIS遥感数据,结合海表温度、风速分析南海中西部叶绿素质量浓度分布特征和影响因素。结果显示南海中西部叶绿素质量浓度分布存在时空变化。EOF分解表明,EOF1可能反映台风等极端天气对叶绿素的影响;而EOF2 和EOF3均反映了夏季沿岸上升流对叶绿素分布的影响。相关分析表明南海中西部叶绿素质量浓度与海面风场呈正相关(r=0.87,p<0.01),与海表温度呈负相关(r=-0.59,p<0.05)。夏季在西南季风影响下越南东南沿海形成上升流,导致该区浮游植物旺发、叶绿素质量浓度升高;冬季受强东北季风影响,研究区海洋上层混合作用强烈,营养盐供应增加,促进了浮游植物生长,叶绿素质量浓度高于其他季节。  相似文献   
2.
针对三峡库区"阶跃式"滑坡的变形特征,提出了一种新的滑坡位移预测方法。以白水河滑坡ZG118和XD-01监测点位移数据为例,采用基于软筛分停止准则的经验模态分解(SSSC-EMD)将累计位移-时间曲线和影响因子时间序列自适应地分解为多个固有模态函数(IMF),并采用K均值(K-Means)聚类法对其进行聚类累加,得到有物理含义的位移分量(趋势性位移、周期性位移以及随机性位移)和影响因子分量(高频影响因子和低频影响因子)。使用最小二乘法对趋势性位移进行拟合预测;采用果蝇优化-最小二乘支持向量机(FOA-LSSVM)模型对周期性位移和随机性位移进行预测。将各位移分量预测值进行叠加处理,实现滑坡累计位移的预测。研究结果表明,所提出的(SSSC-EMD)-K-Means-(FOA-LSSVM)模型能够预测"阶跃式"滑坡的位移变化规律,且预测精度高于传统的支持向量机回归(SVR)、最小二乘支持向量机(LSSVM)模型;并通过改变训练集长度,进行单因素分析,发现其与预测精度之间呈正相关关系。  相似文献   
3.
角度域共成像点道集是衔接叠前地震数据与储层特征的重要桥梁,对地震偏移成像与储层描述具有重要意义.与克希霍夫偏移和单向波动方程偏移相比,逆时偏移是复杂地区最精确的成像方法.高效稳健地生成逆时偏移角度道集目前仍然是一个挑战.本文主要讨论如何采用光学流方法高效、高质量地提取角度道集.在逆时偏移波场外推过程中,光学流方法可以估计波场传播方向.其中Lucas-Kanade(LK)和Horn-Schunck(HS)方法是光学流方法中两种典型的方法.LK光学流方法是一种局部方法,该方法依赖于局部点的梯度值,但是容易出现奇异现象,HS光学流方法属于全局方法,波场方向估计依赖于整个波场,易受噪声影响,对异常值比较敏感,导致整体波场方向计算精度不高.本文提出采用局部和整体结合(Combining Local and Global,CLG)的光学流方法估计波场传播方向.该方法可以有效地提高波场方向的精度,并且简单高效,便于并行处理.对比HS光学流方法,CLG光学流方法几乎不增加额外的计算量.另外,为了弱化光学流方法无法处理波前重叠问题,本文利用解析波场和方向滤波对波场进行方向分解,仅需波场的空间傅里叶变换即可实现任意波场方向分解,将分解后的波场分别估计波场反向,提取成像结果.进一步地,在估计反射张角和方位角时,本文提出有效的归一化方法和改进的最小二乘除法,提高角度估计的精度和稳定性.最后,理论和实际资料例证了本文提出方法的有效性.  相似文献   
4.
As a cold and dry planet, Mars contains water resources in the form of water ice, so that the electromagnetic waves can be transmitted to the deep underground to get the information of the topography and subsurface geological structure. Subsurface penetrating radar(SPR) can be widely used in deep space exploration for a long time because of its non-destructive detection mode and its working characteristics not limited by visible light. It is an important type of equipment for detecting the subsurface structure of planets. Orbiter radar is mainly used in Mars exploration. However, because of its low resolution, it is difficult to describe the near surface structure, so there is a lack of radar data which can reflect the shallow information. In this paper, a three-dimensional near surface model of Utopia Planitia on Mars is established. In order to make the simulation results more reasonable, the key factors such as topographic relief, subsurface rocks and water ice, and the variation of dielectric constant in different layers are taken into account. Then the full polarization forward modeling is carried out by using the three-dimensional finite-difference time-domain method. The acquired full polarimetric subsurface penetrating radar(FP-SPR) data with noise is preprocessed and further processed by Pauli decomposition. The underground reflection can be picked up more clearly from the Pauli decomposition results. This work is helpful to identify more details of subsurface structures and provides a reference for the measured data in the future.  相似文献   
5.
针对不同排序算法对模糊度解算存在降相关性能影响的问题,从理论上分析了自然升序法、对称旋转法及扰动升序法的降相关原理,并基于模拟数据和实测数据,从降相关时间、搜索时间、总体耗时、Bootstrapping成功率及条件数5个方面对3种算法进行对比分析。结果表明,降相关效率与搜索椭球压缩程度呈负相关关系,搜索椭球压缩程度越高,降相关效率越低;对于不同的排序算法,提高降相关性能的关键在于减少降相关时间及对条件方差按一定方向排序,进而提高搜索效率。  相似文献   
6.
There is major uncertainty in the responses of litter decomposition to the inundation regimes in field studies, mainly because of the difficulties in identification of the individual effect of duration and frequency using field studies alone. The interactive role of inundation regime and litter quality also remains unclear. The responses of mass loss to simulated inundation regime (duration and frequency) and litter quality were investigated in leaves of Carex brevicuspis and leaves and stems of Miscanthus sacchariflorus from Dongting Lake, China. Three litter types differing in litter quality were incubated under seven different inundations over 360 days (three single inundations of 90, 180, and 360 days; three repeated 180-day inundations of 2, 3, and 6 times; and no inundations) in a pond near Dongting Lake. Initial N and P contents were highest in C. brevicuspis leaves, intermediate in M. sacchariflorus leaves, and lowest in M. sacchariflorus stems, whereas the organic C, cellulose, and lignin contents were ranked in the opposite order among the three litter types. Decomposition rate was highest in M. sacchariflorus leaves (0.00222–0.00900 day−1), intermediate in C. brevicuspis leaves (0.00135–0.00500 day−1), and lowest in M. sacchariflorus stems (0.00080–0.00100 day−1). The decomposition rate of both C. brevicuspis and M. sacchariflorus leaves increased with increasing inundation duration or decreasing frequency. However, both duration and frequency of inundation had no effect on decomposition of M. sacchariflorus stems. At the end of the incubation, N mineralization was complete in leaf litters with increasing rates with increasing inundation duration or decreasing inundation frequency, but accumulation was found in M. sacchariflorus stems. Organic C decayed quickly in both leaf litters compared with the stem litter. These data indicate that inundation regime has no effect on the decomposition of refractory stem litter while prolonged and stable inundation stimulates the degradation of labile leaf litter.  相似文献   
7.
利用北天山地区2016~2019年观测的4期流动重力观测资料,分析研究一年尺度的重力场动态变化特征,并利用小波分析方法,将不同场源深度的重力异常进行分离。通过功率谱分析,获取各阶小波重力细节对应的场源深度。研究结果表明,2017年8月9日精河MS6.6地震前,震中位于负值集中区,四阶小波重力细节显示震中附近出现明显的四象限分布;2020年1月16日库车MS5.6地震前,震中位于负值区,小波重力细节整体量值较小;功率谱估算的场源近似深度与2次地震的震源深度相近。  相似文献   
8.
针对实际工程应用中遇到的参数带有范围约束的情形,提出带椭球约束的平差算法,并给出其具体模型和解算步骤。数值模拟实验和病态测边网数据计算表明,在处理病态问题时,最小二乘平差(least-squares,LS)已不适用,而与岭估计、奇异值分解法(singular value decomposition,SVD)以及不等式约束相比,本文算法精度更高。  相似文献   
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10.
ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.  相似文献   
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