Cu and Fe skarns are the world’s most abundant and largest skarn type deposits, especially in China, and Au-rich skarn deposits have received much attention in the past two decades and yet there are few papers focused on schematic mineral deposit models of Cu–Fe–Au skarn systems. Three types of Au-rich deposits are recognized in the Edongnan region, Middle–Lower Yangtze River metallogenic belt: ~140 Ma Cu–Au and Au–Cu skarn deposits and distal Au–Tl deposits; 137–148 Ma Cu–Fe; and 130–133 Ma Fe skarn deposits. The Cu–Fe skarn deposits have a greater contribution of mantle components than the Fe skarn deposits, and the hydrothermal fluids responsible for formation of the Fe skarn deposits involved a greater contribution from evaporitic sedimentary rocks compared to Cu–Fe skarn deposits. The carbonate-hosted Au–Tl deposits in the Edongnan region are interpreted as distal products of Cu–Au skarn mineralization. A new schematic mineral deposit model of the Cu–Fe–Au skarn system is proposed to illustrate the relationship between the Cu–Fe–Au skarn mineralization, the evaporitic sedimentary rocks, and distal Au–Tl deposits. This model has important implications for the exploration for carbonate–hosted Au–Tl deposits in the more distal parts of Cu–Au skarn systems, and Fe skarn deposits with the occurrence of gypsum-bearing host sedimentary rocks in the MLYRB, and possibly elsewhere. 相似文献
Local place names are frequently used by residents living in a geographic region. Such place names may not be recorded in existing gazetteers, due to their vernacular nature, relative insignificance to a gazetteer covering a large area (e.g. the entire world), recent establishment (e.g. the name of a newly-opened shopping center) or other reasons. While not always recorded, local place names play important roles in many applications, from supporting public participation in urban planning to locating victims in disaster response. In this paper, we propose a computational framework for harvesting local place names from geotagged housing advertisements. We make use of those advertisements posted on local-oriented websites, such as Craigslist, where local place names are often mentioned. The proposed framework consists of two stages: natural language processing (NLP) and geospatial clustering. The NLP stage examines the textual content of housing advertisements and extracts place name candidates. The geospatial stage focuses on the coordinates associated with the extracted place name candidates and performs multiscale geospatial clustering to filter out the non-place names. We evaluate our framework by comparing its performance with those of six baselines. We also compare our result with four existing gazetteers to demonstrate the not-yet-recorded local place names discovered by our framework. 相似文献
Based on oceanographic survey data in June 2012 in the Lembeh Strait, the zooplankton ecological characteristics such as species composition, individual abundance, dominant species and distribution were analyzed. The results showed that 183 species(including 4 sp.) had been recognized, most of them belonged to copepoda.Cnidaria followed with 43 species(including 1 sp.) were identified. The average abundance of zooplankton was(150.47±58.91) ind./m~3. As to the horizontal distribution, the abundance of the zooplankton was higher in the southern waters than in the northern waters. The dominant species in the study area were Lensia subtiloides,Sagitta enflata, Lucifer intermedius, Oikopleura rufescens, Diphyes chamissoni, Creseis acicula, Subeucalanus subcrassus, Temora discaudata, Aglaura hemistoma, Doliolum denticulatum, Canthocalanus pauper, Oikopleura longicauda and Nanomia bijuga. Zooplankton biodiversity indexes were higher in study area than previous study in the other regions. The findings from this study provide important baseline information for future research and monitoring programs. 相似文献
Exploring the spatial relationships between various geological features and mineralization is not only conducive to understanding the genesis of ore deposits but can also help to guide mineral exploration by providing predictive mineral maps. However, most current methods assume spatially constant determinants of mineralization and therefore have limited applicability to detecting possible spatially non-stationary relationships between the geological features and the mineralization. In this paper, the spatial variation between the distribution of mineralization and its determining factors is described for a case study in the Dingjiashan Pb–Zn deposit, China. A local regression modeling technique, geological weighted regression (GWR), was leveraged to study the spatial non-stationarity in the 3D geological space. First, ordinary least-squares (OLS) regression was applied, the redundancy and significance of the controlling factors were tested, and the spatial dependency in Zn and Pb ore grade measurements was confirmed. Second, GWR models with different kernel functions in 3D space were applied, and their results were compared to the OLS model. The results show a superior performance of GWR compared with OLS and a significant spatial non-stationarity in the determinants of ore grade. Third, a non-stationarity test was performed. The stationarity index and the Monte Carlo stationarity test demonstrate the non-stationarity of all the variables throughout the area. Finally, the influences of the degree of non-stationary of all controlling factors on mineralization are discussed. The existence of significant non-stationarity of mineral ore determinants in 3D space opens up an exciting avenue for research into the prediction of underground ore bodies.