林甸M S 5.1地震前地震活动图像分析和综合预报方法

通过对黑龙江亚板块及其临近区域的不同时段地震活动图像研究分析,发现了中强震前地震活动图像的一些特点,再回归到地震动力学环境中去,建立了理论解释模型和两条判据假说。以此为基础并结合近年来地震活动图像综合分析后,我们在2005年下半年黑龙江省地震趋势会商意见中提出:松嫩盆地是5级以上中强震的主要孕震区。2005年7月25日林甸Ms 5．1地震对此给予了验证。本文对此过程阐述并作进一步分析,希望这种综合预报方法对地震预报工作有所帮助。     

Long baroclinic Rossby waves with periods of about 500d near 20°N in the northwest Pacific Ocean

On the basis of maps of sea level anomalies data set from October 1992 to January 2004, pronounced low frequency variations with periods of about 500 d are detected in the area near 20°N from 160°W to 130°E. A linear two-layer model is employed to explain the mechanism. It is found that the first-mode long baroclinic Rossby waves at 20°N in the northwest Pacific propagate westward in the form of free waves at a speed of about 10.3 cm/s. This confirms that the observed low frequency variabilities appear as baroclinic Rossby waves. It further shows that these low frequency variabilities around 20°N in the northwest Pacific can potentially be predicted with a lead up to 900 d.     

数字技术背景下地图产品的变革

电子地图一边吸收传统地图学理论的精髓,同时引进新的技术和方法,摆脱了传统地图制图的局限和束缚,成为一种地理学研究与应用的新的语言形式。通过对传统地图同电子地图产品的比较来阐述地图产品在数字技术背景下产品模式、制图技术及应用领域等方面的变革。     

Same space – different perspectives: comparative analysis of geographic context through sketch maps and spatial video geonarratives

ABSTRACT

The importance of including a contextual underpinning to the spatial analysis of social data is gaining traction in the spatial science community. The challenge, though, is how to capture these data in a rigorous manner that is translational. One method that has shown promise in achieving this aim is the spatial video geonarrative (SVG), and in this paper we pose questions that advance the science of geonarratives through a case study of criminal ex-offenders. Eleven ex-offenders provided sketch maps and SVGs identifying high-crime areas of their community. Wordmapper software was used to map and classify the SVG content; its spatial filter extension was used for hot spot mapping with statistical significance tested using Monte Carlo simulations. Then, each subject’s sketch map and SVG were compared. Results reveal that SVGs consistently produce finer spatial-scale data and more locations of relevance than the sketch maps. SVGs also provide explanation of spatial-temporal processes and causal mechanisms linked to specific places, which are not evident in the sketch maps. SVG can be a rigorous translational method for collecting data on the geographic context of many phenomena. Therefore, this paper makes an important advance in understanding how environmentally immersive methods contribute to the understanding of geographic context.     

Fossilisation of a silica diagenesis reaction front on the mid-Norwegian margin

The diagenetic transformation of biogenic silica from opal-A to opal-CT was recognised on seismic reflection data over an area of 78 × 10³ km² on the mid-Norwegian margin. The opal-A/CT diagenetic boundary appears as a positive, high amplitude reflection that generally cross-cuts the hosting stratigraphy. We demonstrate that it is not a sea bottom simulating reflection (BSR) and also that is not in thermal equilibrium with the present day isotherms. We present arguments that three styles of deformation associated with the opal-A/CT reflection – polygonal faulting, regional anticlines and synclines and differential compaction folding – indicate that the silica diagenesis reaction front is fossilised at a regional scale. Isochore maps demonstrate the degree of conformity between the opal-A/CT reflection and three seismic horizons of Late Miocene to Early Pliocene age that potentially represent the paleo-seabed when ‘fossilisation’ of the reaction front took place. The seismic interpretational criteria for recognition of a fossilised diagenetic front are evaluated and the results of our study are integrated with previous studies from other basins of the NE Atlantic in order to determine if the arrest of silica diagenesis was diachronous along this continental margin.     

A New Approach for Border Detection of the Dumluca (Turkey) Iron Ore Area: Wavelet Cellular Neural Networks

Anomaly analysis is used for various geophysics applications such as determination of geophysical structure's location and border detections. Besides the classical geophysical techniques, artificial intelligence based image processing algorithms have been found attractive for geophysical anomaly analysis. Recently, cellular neural networks (CNN) have been applied to geophysical data and satisfactory results are reported. CNN provides fast and parallel computational capability for geophysical image processing applications due to its filtering structure. The behavior of CNN is defined by two template matrices that are adjusted by a properly supervised learning algorithm. After training stage for geophysical data, Bouguer anomaly maps can be processed and analyzed sequentially. In this paper, CNN learning and processing capability have been improved, combining Wavelet functions and backpropagation learning algorithms. The new architecture is denoted as Wavelet-Cellular Neural networks (Wave-CNN) and it is employed to analyze Bouguer anomaly maps which are important to extract useful information in geophysics. At first, Wave-CNN performance is tested on synthetic geophysical data, which are created by a computer environment. Then, Bouguer anomaly maps of the Dumluca iron ore field have been analyzed and results are reported in comparison to real drilling results.     

Self-organised clustering for road extraction in classified imagery

The extraction of road networks from digital imagery is a fundamental image analysis operation. Common problems encountered in automated road extraction include high sensitivity to typical scene clutter in high-resolution imagery, and inefficiency to meaningfully exploit multispectral imagery (MSI). With a ground sample distance (GSD) of less than 2 m per pixel, roads can be broadly described as elongated regions. We propose an approach of elongated region-based analysis for 2D road extraction from high-resolution imagery, which is suitable for MSI, and is insensitive to conventional edge definition. A self-organising road map (SORM) algorithm is presented, inspired from a specialised variation of Kohonen's self-organising map (SOM) neural network algorithm. A spectrally classified high-resolution image is assumed to be the input for our analysis. Our approach proceeds by performing spatial cluster analysis as a mid-level processing technique. This allows us to improve tolerance to road clutter in high-resolution images, and to minimise the effect on road extraction of common classification errors. This approach is designed in consideration of the emerging trend towards high-resolution multispectral sensors. Preliminary results demonstrate robust road extraction ability due to the non-local approach, when presented with noisy input.     

中国国家自然地图集Internet版的设计和研制

本文系统阐述了国家自然地图集Ｉｎｔｅｒｎｅｔ版的设计和研制过程中的若干问题,包括系统的概念设计、系统开发中的ＷｅｂＧＩＳ技术和策略,系统的主要功能和特色,以及系统维护和更新方案等。本系统从概念设计来看分为服务器端软件模块、浏览器端软件模块、国家自然地图集数据库支持等几个部分。系统开发中采取的ＷｅｂＧＩＳ技术和策略有:兼顾服务器端和客户端的综合型ＷｅｂＧＩＳ策略,基于Ｊａｖａ Ａｐｐｌｅｔ的 ＷｅｂＧＩＳ浏览器开发技术,基于Ｍａｐ Ｏｂｊｅｃｔ(ＭＯ)的ＷｅｂＧＩＳ服务器开发技术等３个方面。在系统的主要功能特色方面,本文从以下５个方面进行了论述:科学有序的内容结构、清晰精练并带导航的界面、富有特色的信息查询功能、多重表达的地图可视化效果、一定的空间信息分析和制图功能。在系统维护和更新方案上,本文从网络技术、数据更新、功能开发３个方面介绍了计划,并对今后的发展趋势进行了展望。     

Application of GIS Processing Techniques for Producing Mineral Prospectivity Maps—A Case Study: Mesothermal Au in the Swayze Greenstone Belt,Ontario, Canada

A Geographic Information System (GIS) is used to prepare and process digital geoscience data in a variety of ways for producing gold prospectivity maps of the Swayze greenstone belt, Ontario, Canada. Data used to produce these maps include geologic, geochemical, geophysical, and remotely sensed (Landsat). A number of modeling methods are used and are grouped into data-driven (weights of evidence, logistic regression) and knowledge-driven (index and Boolean overlay) methods. The weights of evidence (WofE) technique compares the spatial association of known gold prospects with various indicators (evidence maps) of gold mineralization, to derive a set of weights used to produce the final gold prospectivity map. Logistic regression derives statistical information from evidence maps over each known gold prospect and the coefficients derived from regression analysis are used to weight each evidence map. The gold prospectivity map produced from the index overlay process uses a weighting scheme that is derived from input by the geologist, whereas the Boolean method uses equally weighted binary evidence maps.The resultant gold prospectivity maps are somewhat different in this study as the data comprising the evidence maps were processed purposely differently for each modeling method. Several areas of high gold potential, some of which are coincident with known gold prospects, are evident on the gold prospectivity maps produced using all modeling methods. The majority of these occur in mafic rocks within high strain zones, which is typical of many Archean greenstone belts.