局地化方法在集合转换卡尔曼滤波同化的适用性研究

为了探索协方差局地化（Covariance Localization,CL）方法在集合转换卡尔曼滤波（Ensemble Transform Kalman Filter,     

塔里木河下游优势草本植物与地下水埋深的关系

对塔里木河下游区域的草本群落进行了调查,获取了其分布状况和群落特征资料,并记录相应的地下水埋深,探讨了区域内距离河流不同处地下水埋深与草本群落特征之间的关系。结果表明:(1)地下水埋深越大,草本群落的生物多样性越小,覆盖度越低,生态结构趋于简单,其中地下水埋深<6 m处草本群落的种类组成和物种多样性相对较高,>7 m处草本植物分布很少,部分地区也受到微地形和地下水埋深的双重影响;(2)在大尺度空间上,草本群落随地下水的分布特点与其他荒漠地区的分布规律基本一致,随着地下水埋深的增加,草本群落的演替次序为:高水位芦苇(Phragmites communis)群落、中水位鹿角草(Glossogyne tenuifolia)群落、中低水位骆驼刺(Alhagi sparsif)群落,不同地下水埋深对应的草本群落之间具有一定的差异性、相似性和连续性;(3)塔里木河下游优势草本物种的生态位宽度均不大,主要是受水环境条件的限制,芦苇与骆驼刺的生态位宽度相对较大,在区域内分布较广。     

Modified multispectral space for color representation

IntroductionIn color reproduction and communication, met-amer exists inherently when the conventionalcondition matching used. Therefore , the colordifference introduced makes the accuracy of colorrepresentation be lowered.In order to eli minatemetamer CIE developed the scheme so-calledspectra matching in Publication 15 .2 and speci-fied that using band-pass filters with 5 nminter-val to sample and then reconstruct the originalcolor signal ,thus the color difference will disap-pear[1].Spectra…     

An adaptive approach to selecting a flow‐partition exponent for a multiple‐flow‐direction algorithm

Most multiple‐flow‐direction algorithms (MFDs) use a flow‐partition coefficient (exponent) to determine the fractions draining to all downslope neighbours. The commonly used MFD often employs a fixed exponent over an entire watershed. The fixed coefficient strategy cannot effectively model the impact of local terrain conditions on the dispersion of local flow. This paper addresses this problem based on the idea that dispersion of local flow varies over space due to the spatial variation of local terrain conditions. Thus, the flow‐partition exponent of an MFD should also vary over space. We present an adaptive approach for determining the flow‐partition exponent based on local topographic attribute which controls local flow partitioning. In our approach, the influence of local terrain on flow partition is modelled by a flow‐partition function which is based on local maximum downslope gradient (we refer to this approach as MFD based on maximum downslope gradient, MFD‐md for short). With this new approach, a steep terrain which induces a convergent flow condition can be modelled using a large value for the flow‐partition exponent. Similarly, a gentle terrain can be modelled using a small value for the flow‐partition exponent. MFD‐md is quantitatively evaluated using four types of mathematical surfaces and their theoretical ‘true’ value of Specific Catchment Area (SCA). The Root Mean Square Error (RMSE) shows that the error of SCA computed by MFD‐md is lower than that of SCA computed by the widely used SFD and MFD algorithms. Application of the new approach using a real DEM of a watershed in Northeast China shows that the flow accumulation computed by MFD‐md is better adapted to terrain conditions based on visual judgement.     

Windowed nearest neighbour method for mining spatio-temporal clusters in the presence of noise

In a spatio-temporal data set, identifying spatio-temporal clusters is difficult because of the coupling of time and space and the interference of noise. Previous methods employ either the window scanning technique or the spatio-temporal distance technique to identify spatio-temporal clusters. Although easily implemented, they suffer from the subjectivity in the choice of parameters for classification. In this article, we use the windowed kth nearest (WKN) distance (the geographic distance between an event and its kth geographical nearest neighbour among those events from which to the event the temporal distances are no larger than the half of a specified time window width [TWW]) to differentiate clusters from noise in spatio-temporal data. The windowed nearest neighbour (WNN) method is composed of four steps. The first is to construct a sequence of TWW factors, with which the WKN distances of events can be computed at different temporal scales. Second, the appropriate values of TWW (i.e. the appropriate temporal scales, at which the number of false positives may reach the lowest value when classifying the events) are indicated by the local maximum values of densities of identified clustered events, which are calculated over varying TWW by using the expectation-maximization algorithm. Third, the thresholds of the WKN distance for classification are then derived with the determined TWW. In the fourth step, clustered events identified at the determined TWW are connected into clusters according to their density connectivity in geographic–temporal space. Results of simulated data and a seismic case study showed that the WNN method is efficient in identifying spatio-temporal clusters. The novelty of WNN is that it can not only identify spatio-temporal clusters with arbitrary shapes and different spatio-temporal densities but also significantly reduce the subjectivity in the classification process.     

Clustering of temporal event processes

A temporal point process is a sequence of points, each representing the occurrence time of an event. Each temporal point process is related to the behavior of an entity. As a result, clustering of temporal point processes can help differentiate between entities, thereby revealing patterns of behaviors. This study proposes a hierarchical cluster method for clustering temporal point processes based on the discrete Fréchet (DF) distance. The DF cluster method is divided into four steps: (1) constructing a DF similarity matrix between temporal point processes; (2) constructing a complete linkage hierarchical tree based on the DF similarity matrix; (3) clustering the point processes with a threshold determined by locating the local maxima on the curve of the pseudo-F statistic (an index which measures the separability between clusters and the compactness in clusters); and (4) identifying inner patterns for each cluster formed by a series of dense intervals, each of which contains at least one event of all processes of the cluster. The contributions of the article are: (1) the proposed DF cluster method can cluster temporal point processes into different groups and (2) more importantly, it can identify the inner pattern of each cluster. Two synthetic data sets were created to illustrate the DF distance between temporal point process clusters (the first data set) and validate the proposed DF cluster method (the second data set), respectively. An experiment and a comparison with a method based on dynamic time warping show that DF cluster successfully identifies the preconfigured patterns in the second synthetic data set. The cluster method was then applied to a population migration history data set for the Northern Plains of the United States, revealing some interesting population migration patterns.     

地质遗迹资源保护与开发的社区参与模式——以华蓥山大峡谷地质公园为例

以华蓥山大峡谷地质公园为例,应用问卷调查和登门访谈的方法,对地质公园内居民的社区参与现状和意愿进行调查,分析他们对地质遗迹资源保护与开发的认知态度,明确他们在资源开发中的利益诉求。调查表明,在资源保护和开发中社区参与存在的主要问题是,居民参与度低,参与机会有限,有强烈的参与决策、规划、商业经营的愿望,并希望在参与中分享资源开发的利益,参与过程得到政府、企业的保障。本文在现有的经营体制下,从资源保护(含环境保护)、规划决策、商业经营、利益分配、社会参与保障等五个方面,构建了居民在地质遗迹资源保护和利用中的社区参与模型体系,为我国地质遗迹资源保护和开发中的社区参与问题研究和实践提供了参考。     

渤海海底地震仪探测试验及初步成果

利用大容量气枪枪阵震源和海底地震仪在渤海首次开展人工地震深部地球物理探测试验。本次试验布设一条NWW-SEE向垂直构造走向的勘测线,共投放海底地震仪51台,回收成功50台。试验结果和数据分析表明,所使用的枪阵有足够的能量输出,海底地震仪记录震相丰富,可识别到Ps,Pg,PmP,Pn等多种震相。初至波层析成像结果表明,9 km以上地层速度结构存在明显的横向不均匀性,渤中地区新生代沉积基底埋深5～6 km,结晶基底埋深约9 km,郯庐断裂带内存在"U"型下凹的相对低速体并有向下切割的趋势。此次试验是我国在渤海深部探测中的成功示范,有效填补了渤海海域深地震测深数据的空白,为渤海深部地壳结构研究及含油气盆地形成演化研究提供了重要的基础资料。     

天山西部季节性积雪密度及含水率的特性分析

利用Snow Fork雪特性分析仪采集积雪物理特性(积雪深度、积雪密度、体积含水率)数据,分析了天山积雪雪崩站稳定积雪期和非稳定积雪期雪物理特性的时间变化特征及其在垂直剖面上的廓线分布.结果表明: 1)稳定期积雪深度随时间缓慢减小,体积含水率垂直廓线随积雪深度变化呈单峰曲线,峰值距雪表面约33 cm,雪密度垂直廓线为中部大、积雪表层和底部较小;2) 非稳定积雪期积雪迅速沉陷,体积含水率相对于雪层温度的变化有滞后效应,滞后时间约为2 h,雪密度垂直廓线与稳定期相同.整个非稳定积雪期的日平均雪密度与日平均含水率、日平均温度均呈显著正相关.     

A comprehensive survey of lignin geochemistry in the sedimentary organic matter along the Kapuas River (West Kalimantan, Indonesia)

In this first study of lignin geochemistry in the world’s longest river on an island, surface sediments were collected along the Kapuas River, three lakes in the upper river, a tributary in the lower river and a separate river during June-July 2007 and December 2007-January 2008. The samples were analyzed for lignin-derived phenols and bulk elemental and stable carbon isotope compositions. Λ values (the sum of eight lignin phenols, expressed as mg/100 mg organic carbon (OC)) ranged from 0.13 to 3.70. Ratios of syringyl/vanillyl (S/V) and cinnamyl/vanillyl (C/V) ranged from 0.34 to 1.18 and 0.28 to 1.40, respectively, indicating the presence of non-woody angiosperm tissues. The high vanillic acid to vanillin (Ad/Al)v (0.71-2.01) and syringic acid to syringaldehyde (Ad/Al)s (0.72-2.12) ratios indicate highly degraded lignin materials. In the upper Kapuas River, highly degraded soil materials discharged from lands that were barren as a result of deforestation activities were detected in the locations directly in those vicinities. The middle Kapuas River showed rapid organic matter degradation, probably due to the presence of fresh terrestrial and phytoplankton organic matter fueling the biogeochemical cycling. The Kapuas Kecil River, one of the two branches in the lower reach of the Kapuas River, showed higher levels and diagenesis of sedimentary organic matter due to input from anthropogenic sources and increased marine organic matter near the mouth. This study shows that different stretches along the river exhibit different levels and composition of sedimentary organic matter, as well as different carbon dynamics, which is directly attributable to the varying landscapes and quality of organic matter.