气溶胶吸收光学特性对低层大气短波加热率的影响

根据气溶胶折射指数的参数化方法确定其虚部ｋ的日变化，并考虑其对短波加热率的影响，发现用ｋ的日平均值代替其逐时值计算大气气溶胶国中热率，将所得结果增大最大增值可达１．６℃／ｄ。     

TRACE-P期间硫酸盐、硝酸盐和铵盐气溶胶的模拟研究

结合最新评估的东亚地区 1°× 1°污染源资料 ,利用由区域大气模式系统 (RAMS)和区域大气质量模式系统 (CMAQ)耦合的空气质量模式系统 ,对东亚地区 2 0 0 1年春季气溶胶的输送及其化学转化过程进行了研究。为了检验模式系统的模拟效果 ,我们将模拟的硫酸盐、硝酸盐和铵盐气溶胶的浓度与TRACE -P观测期间获取的观测值进行了比较。比较结果显示 ,模拟值与观测值具有相当好的一致性 ,模式很好地反映了气溶胶浓度的分布特征和变化规律 ,再现了许多观测到的重要特征。模拟结果表明 ,中国区域硫酸盐气溶胶浓度高值主要是人为排放的二氧化硫造成的 ,10 0°E以东的中国广大地区的硫酸盐气溶胶柱含量超过了 6mg·m- 2 ,最高值达到 2 4mg·m- 2 ,柱含量 >16mg·m- 2 的区域延伸到中国近海的广大海域。东亚地区的人类活动不仅使污染地区气溶胶柱含量显著增加 ,而且使近海无源区的广大海域的污染加重。本模式系统的建立为今后进一步研究区域大气污染物的变化机理提供了有效的手段。     

Adaptive depth controller design for a submerged body moving near free surface

A submerged body that moves near a free surface needs to keep its attitude and position to accomplish its missions, which are required to validate the performance of a designed controller before sea trial. Hydrodynamic maneuvering coefficients are generally obtained by experiments or computational fluid dynamics, but these coefficients suffer from uncertainty. Environmental loads such as wave excitation, current, and suction forces act on the submerged body when it moves near the free surface. Therefore, a controller for the submerged body should be robust to parameter uncertainty and environmental loads. In this paper, six-degree-of-freedom equations of motion for the submerged body are constructed. An adaptive control method based on the neural network and proportional–integral–derivative controller is used for the depth controller. Simulations are performed under various depth and environmental conditions, and the results show the effectiveness of the designed controller.     

Multi-temporal RADARSAT-2 polarimetric SAR for maize mapping supported by segmentations from high-resolution optical image

Due to its ability to penetrate the cloud, Synthetic Aperture Radar (SAR) has been a great resource for crop mapping. Previous research has verified the applicability of SAR imagery in object-oriented crop classification, however, speckle noise limits the generation of optimal segmentation. This paper proposed an innovative SAR-based maize mapping method supported by optical image, Gaofen-1 PMS, based segmentation, named as parcel-based SAR classification assisted by optical imagery-based segmentation (os-PSC). Polarimetric decomposition was applied to extract polarimetric parameters from multi-temporal RADARSAT-2 data. One Gaofen-1 image was then used for parcel extraction, which was the basic unit for SAR image analysis. The final step was a multi-step classification for final maize mapping including: the potential maize mask extraction, pure/mixed maize parcel division and an integrated maize map production. Results showed that the overall accuracy of the os-PSC method was 89.1%, higher than those of pixel-level classification and SAR-based segmentation methods. The comparison between optical- and SAR-based segmentation demonstrated that optical-based segmentation would be better at representing maize field boundaries than the SAR-based segmentation. Moreover, the parcel- and pixel-level integrated classification will be suitable for many agricultural systems with small landownership where inter-cropping is common. Through integrating advantages of the SAR and optical data, os-PSC shows promising potentials for crop mapping.     

基于密度扰动的点扩散函数计算与三维深度域模拟成像

点扩散函数(point spread function, 简称PSF)是成像系统对单位强度点散射体的响应, 基于PSF的反演方法为直接在深度域进行储层参数反演提供了新的方向.本研究旨在提出一种切实可行的PSF计算方法, 并利用PSF得到深度域模拟成像.本文从扰动理论出发, 分析了速度和密度扰动对散射场的影响, 在前人工作的基础上提出了基于密度扰动的两步正演法来计算PSF.然后, 深入研究了如何去除模拟成像过程中的结构信息污染, 如何进行相位匹配以及空间褶积的实现细节等问题, 并提出了相应的有效解决方案.模型测试表明采用本文提出的计算方法得到的PSF具有准确、污染小的特点, 并且基于PSF得到的深度域模拟成像剖面在振幅和相位上都与实际偏移成像结果匹配良好.

     

基于时空局域化dreamlet单程波算子的观测系统沉降法偏移

Dreamlet偏移成像目的是探索一类能够对地震波场和单程波传播算子同时分解和压缩的理论和方法,也即实现在压缩域的传播与成像.地震数据在时间和空间的非平稳性质,决定了要实现地震数据的有效稀疏表示,分解方法必须在时间和空间上同时具有局域化性质.Dreamlet由时间和空间局部分解原子的张量积构成,可以看作一种脉冲-小波束形式的波场分解原子.时空局域化的dreamlet单程波传播算子在对波场沿深度方向延拓时,地震数据在时间轴上总是向同一方向流动.随着深度的增加,部分用于成像浅层结构的数据归位至其空间位置后被dreamlet算子丢弃,波场的有效记录时间变短,每一步用于波场延拓的计算量也相应下降.为了充分发挥这一优势,本文介绍dreamlet传播算子与观测系统沉降法偏移相结合的理论与方法.观测系统沉降法偏移每一步都将记录到的所有数据向下延拓,沉降后的波场等效于把源和检波器都放置在目标深度所能接收的反射数据.Dreamlet观测系统沉降过程只保留用于成像观测系统下部地质结构的有效数据,自动丢弃已经用于成像观测系统上部而对下部成像没有贡献的信号.本文通过二维SEG/EAGE叠后和Marmousi叠前数据算例展示了dreamlet传播算子应用于观测系统沉降法偏移的这一特点.数值算例结果显示,在不影响成像质量的前提下,该偏移方法能够有效减少传播数据量,为发展一种快速高效的偏移方法提供了新的思路.

     

AN ANALYTICAL SOLUTION FOR CALCULATING THE INITIATION OF SEDIMENT MOTION

This paper presents an analytical solution for calculating the initiation of sediment motion and the risk of river bed movement. It thus deals with a fundamental problem in sediment transport, for which no complete analytical solution has yet been found. The analytical solution presented here is based on forces acting on a single grain in state of initiation of sediment motion. The previous procedures for calculating the initiation of sediment motion are complemented by an innovative combination of optical surface measurement technology for determining geometrical parameters and their statistical derivation as well as a novel approach for determining the turbulence effects of velocity fluctuations. This two aspects and the comparison of the solution functions presented here with the well known data and functions of different authors mainly differ the presented solution model for calculating the initiation of sediment motion from previous approaches. The defined values of required geometrical parameters are based on hydraulically laboratory tests with spheres. With this limitations the derivated solution functions permit the calculation of the effective critical transport parameters of a single grain, the calculation of averaged critical parameters for describing the state of initiation of sediment motion on the river bed, the calculation of the probability density of the effective critical velocity as well as the calculation of the risk of river bed movement. The main advantage of the presented model is the closed analytical solution from the equilibrium of forces on a single grain to the solution functions describing the initiation of sediment motion.     

Organic matter and fine grains as possible determinants of spatial and seasonal variability in bed sediment fauna: A case study from a Hercynian gravel stream

The spatial and seasonal distributions of organic matter and fine grains were tested as possible determinants of fauna distribution in bed sediment of a Hercynian gravel stream. Invertebrate densities and the amounts of fine grains and organic carbon were assessed in freeze-core samples taken along 70 cm depth profiles at three different positions in the stream channel. Sampling was conducted on five occasions of low discharge over two years. The variability in invertebrate community composition was analysed using Detrended Correspondence Analysis with posterior projection of explanatory variables; Variation Partitioning was used to estimate the independent and shared effects of the explanatory variables. We found that the best predictors of the invertebrate community were spatial variables (depth, position in the channel) and then variables influenced by seasonal patterns (surface water temperature and discharge). The influence of organic matter and fine grain content was significant only after eliminating spatial autocorrelation. High amounts of organic matter, randomly accumulated in the sediment, improved the model by explaining high fauna densities. The fine grain content was not a limiting factor to fauna at our study site. It is possible that the large amount of mica flakes in the sediment has caused the arrangement of grains with a pore space sufficient for fauna even when fine grain content was high.     

利用地震台阵识别中深源远震sP和pP震相确定震源深度

震源深度是目前最难确定的震源参数之一,基于密集的近台观测数据可以准确确定近震震源深度,但对远震的震源深度无法较好地约束。远震反射波震相sP和pP包含用于确定震源深度的有效信息,理论上可以对远震的震源深度进行有效约束。将国内测震台站中台间距远小于震中距的区域台站作为台阵,通过互相关、震相对齐、波形叠加聚束等处理,可以提升sP和pP深度震相的信噪比。利用IASP91全球模型,根据震相走时差得到较为准确的震源深度。本文计算了2017—2020年M≥6.0的64个国外远震的震源深度。通过与GCMT、GFZ、USGS等机构给出的结果相比,验证了此方法得到的震源深度的可靠性。通过讨论破裂持续时间对pP震相识别的影响,得到此方法在矩震级6.0～8.5时,在不同深度上有较好的适用性。     

深度域自适应加权多模态多任务学习声阻抗反演

以往基于深度学习的叠后地震声阻抗反演通常仅限于利用单道地震数据,当地震数据中存在较强噪声或地下介质横向变化较大时反演结果中会面临横向连续性差和层位区分不清晰的问题.本文提出了一种深度域自适应加权多模态多任务学习声阻抗反演方法,采用GPU加速运算,在阻抗剖面的估计过程中加入井位置附近的空间背景信息,并在数据输入端加入初始阻抗模型.构建的网络包括阻抗反演和地震数据重建两个任务,网络训练过程中采用自适应权重调整策略能够同时优化各自输出的损失,实现数据增强,缓解网络的过拟合,提高网络的泛化能力;相比于传统最小二乘阻抗反演方法,该方法反演速度更快.针对井数据偏少的情况,本文引入其他可用训练数据并采用联合学习策略改善反演结果.模型和实际数据测试表明该方法能够在含有噪声的地震数据和测井数据有限的情况下估计深度域声阻抗,反演结果的横向连续性得到了明显的改善.