排序方式： 共有10条查询结果，搜索用时 15 毫秒
1
1.
2.
3.
Mohammad Hossein Sowlat Kazem Naddafi Masud Yunesian Peter L. Jackson Saeedeh Lotfi Abbas Shahsavani 《洁净——土壤、空气、水》2013,41(12):1143-1151
Source apportionment of particulate matter <10 µm in diameter (PM10), having considerable impacts on human health and the environment, is of high priority in air quality management. The present study, therefore, aimed at identifying the potential sources of PM10 in an arid area of Ahvaz located in southwest of Iran. For this purpose, we collected 24‐h PM10 samples by a high volume air sampler. The samples were then analyzed for their elemental (Al, As, B, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, Hg, K, Mg, Mn, Na, Ni, Pb, Se, Si, Sn, Sr, Li, Ti, V, Zn, Mo, and Sb) and ionic (NH, Cl?, NO, and SO) components using inductively coupled plasma optical emission spectrometry and ion chromatography instruments, respectively. Eight factors were identified by positive matrix factorization: crustal dust (41.5%), road dust (5.5%), motor vehicles (11.5%), marine aerosol (8.0%), secondary aerosol (9.5%), metallurgical plants (6.0%), petrochemical industries and fossil fuel combustion (13.0%), and vegetative burning (5.0%). Result of this study suggested that the natural sources contribute most to PM10 particles in the area, followed closely by the anthropogenic sources. 相似文献
4.
5.
Hyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data. To overcome this limitation, we propose a Structured Sparse regularized Nonnegative Matrix Factorization (SS-NMF) method based on the following two aspects. First, we incorporate a graph Laplacian to encode the manifold structures embedded in the hyperspectral data space. In this way, the highly similar neighboring pixels can be grouped together. Second, the lasso penalty is employed in SS-NMF for the fact that pixels in the same manifold structure are sparsely mixed by a common set of relevant bases. These two factors act as a new structured sparse constraint. With this constraint, our method can learn a compact space, where highly similar pixels are grouped to share correlated sparse representations. Experiments on real hyperspectral data sets with different noise levels demonstrate that our method outperforms the state-of-the-art methods significantly. 相似文献
6.
地震波走时广泛应用于静校正、层析成像、Kirchhoff偏移成像、地震定位等研究.复杂地表条件是影响走时计算精度的重要因素.近年来,发展的曲线坐标系程函方程为精细刻画起伏地表条件下的地震波走时场特征提供了新的思路.然而,基于有限差分程函方程的求解方法不可避免地受到震源奇异性的影响,即震源附近波前的曲率较大,此时使用平面波近似假设的差分格式会导致较大误差.而震源误差会随着波前的传播到达整个计算区域,从而影响整个区域的求解精度.针对该问题,本文借鉴因式分解的思想,推导建立了曲线坐标系因式分解程函方程,并针对性地发展了其数值求解方法,从根源上解决了复杂模型走时计算中的震源奇异性问题.数值实例表明因式分解法能够有效降低震源误差,显著提高起伏地表走时计算的精度和效率,为起伏地表地震波走时计算提供更佳的选择,在复杂模型的地震资料处理中展现出广泛的应用前景. 相似文献
7.
Wave‐equation based methods, such as the estimation of primaries by sparse inversion, have been successful in the mitigation of the adverse effects of surface‐related multiples on seismic imaging and migration‐velocity analysis. However, the reliance of these methods on multidimensional convolutions with fully sampled data exposes the ‘curse of dimensionality’, which leads to disproportional growth in computational and storage demands when moving to realistic 3D field data. To remove this fundamental impediment, we propose a dimensionality‐reduction technique where the ‘data matrix’ is approximated adaptively by a randomized low‐rank factorization. Compared to conventional methods, which need for each iteration passage through all data possibly requiring on‐the‐fly interpolation, our randomized approach has the advantage that the total number of passes is reduced to only one to three. In addition, the low‐rank matrix factorization leads to considerable reductions in storage and computational costs of the matrix multiplies required by the sparse inversion. Application of the proposed method to two‐dimensional synthetic and real data shows that significant performance improvements in speed and memory use are achievable at a low computational up‐front cost required by the low‐rank factorization. 相似文献
8.
从三维非均匀介质中的波动方程出发，利用拟微分算子理论，Ｐａｄｅ逼近方法及因式分解技巧，获得了非均匀介质的三维高阶深度偏移方程，相应地提出了逐次低阶方程方法、低阶方程组方法及分裂方法等３种求解方法．与二维情形不同，以上每一种方法在数值求解时均存在由测线坐标ｙ的出现而带来的困难．为了克服这一困难，我们提出了差分算子分解方法，避免了近年来人们竞相研究的ｘ－ｙ方向微分算子分裂带来的分裂误差，保持了应有的相容性，解决了这一令人烦恼的问题． 相似文献
9.
为了解2021年南京市新冠疫情期间城市大气污染物浓度的变化和成因,利用南京大学SORPES站点2021年7月1日—2021年8月30日大气污染物在线监测数据,分析疫情前、中、后颗粒物及气态污染物的浓度变化,针对臭氧(O3)的关键前体物,挥发性有机物(Volatile Organic Compounds,VOCs)采用正定矩阵因子分解法模型(Positive Matrix Factorization,PMF)、拉格朗日粒子输送与扩散模型(Lagrangian Particle Distribution Model,LPDM)分析其污染来源。结果表明:疫情封闭期间,南京市PM2.5质量浓度较疫情前降低了40％～50％,组分中硝酸盐、有机物质量浓度降幅最为显著,分别下降了34.0％和16.5％。臭氧体积浓度不降反升,城中区域增幅最显著站点可达50％左右。其气态前体物氮氧化物(NOx)及VOCs浓度变化呈相反态势,分别较疫情前降低28％、升高49.6％。模型及卫星遥感结果表明,疫情期间南京市臭氧属于协同偏VOCs控制区。气团溯源结果显示,南京市受本地及周边区域传输的共同影响,疫情封闭期间省外上海方向、省内苏州-无锡-镇江-南通方向的气团贡献增大。PMF解析了南京市本地VOCs主要来源于机动车排放源、植物源、溶剂源、工业生产源以及油气挥发源,其中机动车源占比变幅最大,疫情封闭期间下降了15.1％,疫情后上升了4.3％。其次为油气挥发源、溶剂源,这两项污染源疫情封闭期间分别上升了11.2％、1.7％,疫情后则分别下降了4.8％、4.3％。 相似文献
10.
空间与谱间相关性分析的NMF高光谱解混 总被引：2，自引：1，他引：1
非负矩阵分解(NMF)技术是高光谱像元解混领域的研究热点。为了充分利用高光谱图像中丰富的空间与光谱相关性特征,改善基于NMF的高光谱解混算法性能,提出一种结合了空间与谱间相关性分析的NMF解混算法。算法针对NMF的通用性和局部极小问题,引入并结合高光谱图像两种典型的相关性特征,具体包括:基于马尔可夫随机场(MRF)模型,建立描述相邻像元空间相关特征的约束;通过复杂度映射技术,建立描述相邻波段谱间相关(光谱分段平滑)特征的约束;并将上述两种约束同时引入NMF解混目标函数中。实验结果表明,对于一般自然地物场景或人造地物场景,相对于分段平滑和稀疏约束的非负矩阵分解(PSNMFSC)、交互投影子梯度的非负矩阵分解(APSNMF)和最小体积约束的非负矩阵分解(MVCNMF)这3种代表性NMF解混参考算法,该算法可进一步提高高光谱解混精度;对于空间相关或谱间相关特征中某一种不显著的特殊场景,也具有更好的适应能力。通过将空间相关和谱间相关特征相结合,较全面地反映了高光谱数据与解混相关的重要特征,能够对绝大多数真实高光谱数据进行高精度解混,对高光谱解混及后续应用领域相关研究均具有参考价值。 相似文献
1