室内楼层平面导航路网模型的自动生成

室内路网模型作为室内导航研究的基础,如何自动生成室内路网模型成为近年的研究热点.对于目前自动路网模型研究中出现的复杂环境适应度不够的问题,本文提出了一种室内楼层平面路网模型的自动提取方法.它将室内空间分为公共空间和专属空间两类,从而形成公共空间路径和专属空间路径,并用转换点将两类路径进行衔接;最终利用提取的公共空间路径...     

Fischer图解及其在旋回层序研究中的应用──以北京西山张夏组为例

Ｆｉｓｃｈｅｒ图解（又称为容纳空间图解）为人们研究沉积旋回在空间上的叠置规律、相对海平面变化、层序级次的划分以及地层层序对比等提供了一种客观实用的方法。本文较详细地讨论了该图解的绘制方法及应注意的问题。运用该图解，将北京西山下苇甸剖面张夏组划分为１个大的三级旋回层序和４个四级旋回层序，根据图解反映的相对海平面变化讨论了旋回层序与构造运动间的关系，提出华北板块晚寒武世之前的＂翘翘板运动＂应始于中寒武世张夏期之早期到中期。     

地表覆盖分类成果质量特征分析

自我国全面建成地理国情普查成果库之后,工作重心由全面普查变为重点监测。不论普查还是监测,准确地对地表覆盖进行分类一直是工作的重点和难点。在常态化监测阶段,把握地表覆盖分类成果的主要质量指标,归纳其诸如变化率、变化区域分布、变化类型,分析影响其成果质量的主要因素,对监测生产组织及质量控制具有非常重要的作用。     

Single-Link Cluster Method to Identify Foreshocks and Aftershocks

Based on Single-Link Cluster (SLC) analysis,a new method to identify the foreshocks andaftershocks of a strong shock from the earthquake catalogue and then to form a sequencecatalogue has been proposed in this paper.In the SLC frame,there are many chains formedby links with lengths shorter than or equal to the characteristic length Lc.It is defined thatwhen some of these chains connect with a strong shock,the seismic events on such chainsfirm a sequence with the strong shock.In this sequence,the strong shock is the main shock;the events preceding the main shock are foreshocks and those following the main shock areaftershocks.By using this method,the foreshocks and aftershocks associated with the M_s7.4Haicheng earthquake and with the strong shocks of M_s≥6.0 in the top area of Kunlun-AltunArc were identified and sequence catalogues for these strong shocks were set up.     

Shape indexes for semi-automated detection of windbreaks in thematic tree cover maps from the central United States

Windbreaks are an important ecological resource across the large expanse of agricultural land in the central United States and are often planted in straight-line or L-shaped configurations to serve specific functions. As high-resolution (i.e., <5 m) land cover datasets become more available for these areas, semi- or fully-automated methods for distinguishing windbreaks from other patches of trees are needed for use with thematic raster datasets. To address this need, we created three shape indexes: a morphology-based index that we have named the Straight and Narrow Feature Index (SNFI), a windbreak sinuosity index, and an area index indicating the occupied fractional area of a bounding box. The indexes were tested in two study areas: (1) a riparian area dominated by sinuous bands of trees but mixed with row crop agriculture and (2) an agricultural area with a mix of straight-line and L-shaped windbreaks. In the riparian area, a Kruskall–Wallis rank sum test indicated class differences for all three indexes, and pairwise comparisons indicate windbreaks and riparian trees are separable using any of the three indexes. SNFI also produced significant differences between windbreaks oriented in different directions (east–west vs. north–south). In the agricultural area, the Kruskall–Wallis rank sum test indicated differences between classes for all three indexes, and pairwise comparisons show that all class pairs have significant differences for at least one index, with the exception of L-shaped windbreaks vs. non-windbreak tree patches. We also used classification trees to objectively assign representative samples of tree patches to classes using both single indexes and multiple indexes. Classes were correctly assigned for more than 90% of the samples in both the riparian and agricultural study areas. In the riparian area, combining indexes did not improve accuracy compared to using SNFI alone, whereas in the agricultural area, combining the three indexes produced the best result. Thematic datasets derived from high-resolution imagery are becoming more available, and extracting useful information can be a challenge, partly due to the large amount of data to assess. Calculating the three shape indexes presented can assist with efficient identification of candidate windbreaks and as such, hold good promise for value-added analysis of tree function in the central United States.     

三肇凹陷葡萄花油层高分辨率层序地层划分及沉积特征研究

应用高分辨率层序地层学理论,通过对各级基准面旋回的沉积动力学分析,依据１０口井岩心和１２５６口井测井资料,把松辽盆地三肇凹陷葡萄花油层划分为１个长期基准面旋回、２个中期基准面旋回、１２个短期基准面旋回,提出了葡萄花油层南部为同沉积构造抬升控制的中部缺失而非底部缺失的层序地层新格架。通过对短期基准面旋回内储层沉积特征、单砂体展布规律的研究,认为葡萄花油层主要储层水下河道砂体比原认识更连续,且延伸较远, 分流平原、内前缘相带南移达３６ｋｍ,这对深入认识该区油藏类型、聚油规律、特别是高含水期剩余油挖潜具有重要意义,对指导整个松辽盆地北部岩性油气藏勘探与开发具有重大影响。     

观赏石的分类与审美

从观赏石是自然形成的具有观赏价值的石质艺术品这一认识出发，将观赏石分为造型石、图纹石、矿物晶体石、古生物化石、纪念石和特殊石６大类。又以观赏石的地域审美观为指导，从色泽、形态、图纹、质感、组合、使巧、命名和意境８个方面对观赏石的审美进行了探讨。     

济源西部石炭二叠系层序地层学特征

根据野外露头观测,结合勘探阶段的资料,对河南省济源西部石炭二叠系层序地层学特征进行了系统研究,探讨了聚煤作用的控制因素,首次发现河南省境域内石炭纪的河流与三角洲沉积,并指出中条山可能是石炭二叠纪重要的物源区。该区层序地层学研究对河南省石炭二叠纪古地理再造具有重要意义。     

Internal architecture of the soft sediment cover of the South-Aquitaine shelf (Bay of Biscay): A record of high frequency sea level variations?

Very high-resolution seismic profiles highlighted three main depositional sequences of several hundred meters thick on the South-Aquitaine shelf. The youngest one, the soft sedimentary cover (U1), is presented in this article. It is composed of six seismic units deposited during the Late Quaternary and corresponds to a repetition of a sequence type. The sequence type is formed in four phases: an erosional unconformity (S), a sub-unit “a”, an internal erosional unconformity (St) and a sub-unit “b”. The first phase begins with the erosional unconformity (S), which could be formed during the sea level fall. The second phase is the deposition of the sub-unit “a”, likely during the sea level lowstand and the early sea level rise. Its acoustic facies is generally chaotic with small channels and could be filled by coarse or heterogeneous sediments. The third phase is the formation of a plane and high amplitude unconformity (St) which could be correlated with a transgressive or a wave ravinement surface. The fourth phase corresponds to the deposition, probably during the sea level rise, of the sub-unit “b” which is characterized by subparallel reflections.     

Integration of high-resolution imagery and LiDAR data for object-based classification of urban area

This paper investigates the synergistic use of high-resolution multispectral imagery and Light Detection and Ranging (LiDAR) data for object-based classification of urban area. The main contribution of this paper is the development of a semi-automated object-based and rule-based classification method. In the implemented approach, the diverse knowledge about land use/land cover classes are transformed into a set of specialized rules. Further, this paper explores supervised Gaussian Mixture Models for classification, which have been primarily used for unsupervised classification. The work is carried out on test data from two different sites. Contribution of the LiDAR data resulted in a significant improvement of overall Kappa. Accuracy assessment carried out for aforementioned classification methods shows higher overall kappa for both the study sites.