基于GeoBeans 的Web GIS 开发与实现

本文在介绍国产Web GIS软件GeoBeans的原理与特点的基础上，探讨了基于GeoBeans的Web GIS开发与实现方法，给出了在某矿田地质信息发布系统中的运用实例。     

孟津县小麦冻害与气象要素调查分析

孟津县2004～2005年冬季持续低温和春季寒流,使小麦遭受冬、春季双重冻害.通过调查小麦冻害苗情,结合气象要素资料分析,找出了小麦冻害的主要影响因素,并制定出消除或减缓冻害的技术措施.     

民乐、山丹6.1级地震异常及台站实现短临预报的思考

在综合分析了民乐-山丹6.1级地震前张掖中心台范围内震兆异常特征的基础上,总结了本次地震前该台的短临预报过程及存在的问题,对今后台站如何搞好地震短临预报工作进行了探讨.     

地震活动标度ΔF震前时间扫描异常浅探

运用地震活动标度ΔF对大华北地区MS≥6.0级地震震前资料进行时间扫描,结果表明,地震活动标度ΔF大多在震前1～2年出现超出一倍均方差的异常;运用该方法还对河南及邻区ML≥5.0级地震震前资料进行了探索,同样,中等地震震前也有一定的异常反映。     

Study of statistically correcting model CMAQ-MOS for forecasting regional air quality

Based on analysis of the air pollution observational data at 8 observation sites in Beijing including outer suburbs during the period from September 2004 to March 2005, this paper reveals synchronal and in-phase characteristics in the spatial and temporal variation of air pollutants on a city-proper scale at deferent sites; describes seasonal differences of the pollutant emission influence between the heating and non-heating periods, also significantly local differences of the pollutant emission influence between the urban district and outer suburbs, i.e. the spatial and temporal distribution of air pollutant is closely related with that of the pollutant emission intensity. This study shows that due to complexity of the spatial and temporal distribution of pollution emission sources, the new generation Community Multi-scale Air Quality (CMAQ) model developed by the EPA of USA produced forecasts, as other models did, with a systematic error of significantly lower than observations, albeit the model has better capability than previous models had in predicting the spatial distribution and variation tendency of multi-sort pollutants. The reason might be that the CMAQ adopts average amount of pollutant emission inventory, so that the model is difficult to objectively and finely describe the distribution and variation of pollution emission sources intensity on different spatial and temporal scales in the areas, in which the pollution is to be forecast. In order to correct the systematic prediction error resulting from the average pollutant emission inventory in CMAQ, this study proposes a new way of combining dynamics and statistics and establishes a statistically correcting model CMAQ-MOS for forecasts of regional air quality by utilizing the relationship of CMAQ outputs with corresponding observations, and tests the forecast capability. The investigation of experiments presents that CMAQ-MOS reduces the systematic errors of CMAQ because of the uncertainty of pollution emission inventory and improves the forecast level of air quality. Also this work employed a way of combining point and area forecasting, i.e. taking the products of CMAQ for a center site to forecast air pollution for other sites in vicinity with the scheme of model products "reanalysis" and average over the "area".     

The diurnal and seasonal characteristics of urban heat island variation in Beijing city and surrounding areas and impact factors based on remote sensing satellite data

Based on the land surface temperature (LST), the land cover classification map,vegetation coverage, and surface evapotranspiration derived from EOS-MODIS satellite data, and by the use of GIS spatial analytic technique and multivariate statistical analysis method, the urban heat island (UHI) spatial distribution of the diurnal and seasonal variabilities and its driving forces are studied in Beijing city and surrounding areas in 2001. The relationships among UHI distribution and landcover categories, topographic factor, vegetation greenness, and surface evapotranspiration are analyzed. The results indicate that: (i) The significant UHI occur in Beijing city areas in the four seasons due to high heat capacity and multi-reflection of compression building, as well as with special topographic features of its three sides surrounded by mountains,especially in the summer. The UHI spatial distribution is corresponding with the urban geometry structure profile. The LST difference is approximately 4-6℃ between Beijing city and suburb areas, comparatively is 8- 10℃ between Beijing city area and outer suburb area in northwestern regions. (ii) The UHI distribution and intensity in daytime are different from nighttime in Beijing city area, the nighttime UHI is obvious. However, in the daytime, the significant UHI mainly appears in the summer, the autumn takes second place, and the UHI in the winter and the spring seem not obvious. The surface evapotranspiration in suburb areas is larger than that in urban areas in the summer, and high latent heat exchange is evident, which leads to LST difference between city area and suburb area. (iii) The reflection of surface landcover categories is sensitive to the UHI, the correlation between vegetation greenness and UHI shows obviously negative.The scatterplot shows that there is the negative correlation between NDVI and LST (R2 = 0.6481).The results demonstrate that the vegetation greenness is an important factor for reducing the UHI,and large-scale construction of greenbelts can considerably reduce the UHI effect.     

北祁连山白山子花岗闪长岩成岩时代

寒山大型金矿是近年来在北祁连山西段发现的与侵人岩有关的构造蚀变岩型金矿。笔者首次利用锆石U-Pb方法测得白山子花岗闪长岩的形成年龄为370±25 Ma，属于华力西期。寒山金矿的成矿主要在213.95~339 Ma间。在多期热液叠加，多期成矿作用中，早期成矿的热液很可能是白山子花岗闪长岩（370±25 Ma) ,寒山辉长岩(347.1±6. 4 Ma）共同提供的。由于该区有较多的中酸性岩体存在，它们可能为金的主要来源，因而，确定这些侵入体的形成年龄，对于在该区寻找蚀变岩型金矿不仅有重要的理论意义，而且有重要的现实意义。     

GIS支持下的小城镇土地定级--以福建省天宝镇区为例

小城镇土地定级是小城镇土地管理的基础性工作,以福建省天宝镇区土地定级为例,就地理信息系统技术在城镇土地定级中的应用进行了探讨。着重论述了GIS环境下土地定级基础图件库和基础属性数据库的建立、评价单元的生成、单元分值和单元总分值的计算及成果图的生成等属性数据和空间数据的处理过程。     

基于多模态感知的水下目标检测应用构想

水下目标检测在海洋生物研究、考古探索、军事防御等多领域广泛应用,随着人工智能快速发展,水下目标检测也朝着无人化、智能化发展。深度学习采用神经网络挖掘信息特征,在速度和精度上均表现出优异的性能,成为了计算机视觉技术的主流算法,然而水下环境复杂,将其应用于水下图像目标检测仍存在较大的挑战。水下目标各模态信息互补,特征丰富,有利于目标检测识别,因此结合应用场景调研现有技术,然后设计基于深度学习的多模态水下目标检测系统,同时对比分析了现有关键技术的优缺点,最后对多模态目标检测系统未来发展进行总结与展望,具有重要意义。     

基于YOLO算法的海洋中尺度涡三维结构构建

海洋中尺度涡是一种常见的中尺度海洋现象,研究海洋中尺度涡的分布及运动特性对航运、气候、军事等具有重要作用,海洋中尺度涡的识别是海洋学和计算机科学领域的一个热门研究课题。运用深度学习的方法和框架,对中尺度涡的二维识别和三维结构构建展开研究分析。首先,获取全球海洋再分析数据并进行流线可视化,构建涡旋流线数据集;其次,利用YOLO v5s卷积神经网络对涡旋流线数据集进行训练,并对南海区域中尺度涡进行有效检测。实验结果表明,YOLO v5s训练后得到最优模型经过测试,平均检测精度均值达到了86.10%;最后,根据涡旋检测结果,对检测出的同时刻不同深度的涡旋判断是否属于同一涡旋,确定后进行该涡旋的三维结构构建。