1902年新疆阿图什8(1/4)级地震形变特征与发震模式初探

在收集、分析前人资料及野外考察的基础上 ,重点研究了 190 2年阿图什 8 级地震形变特征与区域地震构造环境的关系 ,并结合震中区的地球物理探测成果 ,对该地震的发震模式进行了探讨。认为 ,阿图什大震的发生是塔里木与南天山两大块体相向对冲挤压的结果。较大的震源深度 ,使得震中区变形以较大规模和范围的崩塌、滑坡、地裂缝等重力地质现象为主 ,浅部薄皮活动构造亦发生了同震破裂及褶皱变形     

中国大陆的垂直形变速率梯度及地震活动

在GIS平台上 ,以 195 1～ 1990年中国大陆垂直形变速度图为基础求出的垂直形变速率梯度 ,其变化情况基本上反映了中国大陆地壳的垂直剪应变及相应的剪应力的大小。东部的华北块体垂直形变速率梯度值明显地高于东北和华南地区 ,西部地区的青藏块体和天山地块的梯度较高 ,它们是现代构造活动较强的块体。斜贯中国大陆的察隅 -通化北东向高梯度带和其西侧的银川 -乌丽北东向高梯度带是现代地壳垂直差异活动强烈的地带。现代强震主要孕育发生在高梯度异常区及其附近 ,垂直形变速率梯度异常对地震危险区预测有一定的应用价值     

高原和开阔山地风资源的估算

本文着重介绍了在高海拔地区如何利用短期梯度考察资料估算风源资的一种计算方案。     

考虑降水时间相关性的地面观测-雷达-卫星遥感逐时降水融合方法研究

以广东省北部山区2018年汛期强降水时段4月23—28日龙舟水、5月7—11日龙舟水和9月16—17日台风“山竹”3次典型暴雨过程的逐时降水为研究对象,研究基于XGBoost算法与地统计学理论的地面观测-前两个时刻逐时降水-雷达-卫星遥感的多源逐时降水融合模型,充分考虑相邻时刻降水的时间相关性,得到空间分辨率为1 km的逐时降水融合数据。此外,分别利用XGBoost与随机森林(RF)算法进行不考虑降水时间相关性的地面观测-雷达-卫星遥感逐时降水融合对比试验,并对试验结果进行精度评价。结果表明：(1) 在3次暴雨过程中,三种融合模型的逐时降水融合结果具有类似的空间分布;(2) 与XGBoost和RF逐时降水融合结果相比,融合了降水时间相关性的逐时降水融合结果在不同暴雨过程的准确性均有明显改进,3次暴雨过程的决定系数(R2)平均提高了7.89%和23.27%;(3) XGBoost逐时降水融合模型的精度整体上优于RF逐时降水融合模型,3次暴雨过程的R2分别提高了7.1%、4.3%和31.4%。     

Mapping land cover gradients through analysis of hyper-temporal NDVI imagery

The green cover of the earth exhibits various spatial gradients that represent gradual changes in space of vegetation density and/or in species composition. To date, land cover mapping methods differentiate at best, mapping units with different cover densities and/or species compositions, but typically fail to express such differences as gradients. Present interpretation techniques still make insufficient use of freely available spatial-temporal Earth Observation (EO) data that allow detection of existing land cover gradients. This study explores the use of hyper-temporal NDVI imagery to detect and delineate land cover gradients analyzing the temporal behavior of NDVI values. MODIS-Terra MVC-images (250 m, 16-day) of Crete, Greece, from February 2000 to July 2009 are used. The analysis approach uses an ISODATA unsupervised classification in combination with a Hierarchical Clustering Analysis (HCA). Clustering of class-specific temporal NDVI profiles through HCA resulted in the identification of gradients in landcover vegetation growth patterns. The detected gradients were arranged in a relational diagram, and mapped. Three groups of NDVI-classes were evaluated by correlating their class-specific annual average NDVI values with the field data (tree, shrub, grass, bare soil, stone, litter fraction covers). Multiple regression analysis showed that within each NDVI group, the fraction cover data were linearly related with the NDVI data, while NDVI groups were significantly different with respect to tree cover (adj. R² = 0.96), shrub cover (adj. R² = 0.83), grass cover (adj. R² = 0.71), bare soil (adj. R² = 0.88), stone cover (adj. R² = 0.83) and litter cover (adj. R² = 0.69) fractions. Similarly, the mean Sorenson dissimilarity values were found high and significant at confidence interval of 95% in all pairs of three NDVI groups. The study demonstrates that hyper-temporal NDVI imagery can successfully detect and map land cover gradients. The results may improve land cover assessment and aid in agricultural and ecological studies.     

一种高分辨率遥感图像舰船检测方法研究

本文针对传统舰船检测方法在复杂海况条件下检测结果不佳,易造成大量虚警的问题,提出了在利用自适应分割算法进行舰船快速筛选的基础上采用方向梯度直方图(HOG)特征提取舰船的边缘结构信息,通过监督学习进行舰船检测虚警滤除的新算法。与现有算法相比,这种算法克服了由云、浪造成的舰船目标虚警滤除问题,在保证鲁棒性的同时兼顾了算法效率,并使用该方法对遥感图像进行了检测试验。     

Estimating spatio-temporal air temperature in London (UK) using machine learning and earth observation satellite data

Urbanisation generates greater population densities and an increase in anthropogenic heat generation. These factors elevate the urban–rural air temperature (T_a) difference, thus generating the Urban Heat Island (UHI) phenomenon. T_a is used in the fields of public health and epidemiology to quantify deaths attributable to heat in cities around the world: the presence of UHI can exacerbate exposure to high temperatures during summer periods, thereby increasing the risk of heat-related mortality. Measuring and monitoring the spatial patterns of T_a in urban contexts is challenging due to the lack of a good network of weather stations. This study aims to produce a parsimonious model to retrieve maximum T_a (T_max) at high spatio-temporal resolution using Earth Observation (EO) satellite data. The novelty of this work is twofold: (i) it will produce daily estimations of T_max for London at 1 km² during the summertime between 2006 and 2017 using advanced statistical techniques and satellite-derived predictors, and (ii) it will investigate for the first time the predictive power of the gradient boosting algorithm to estimate T_max for an urban area. In this work, 6 regression models were calibrated with 6 satellite products, 3 geospatial features, and 29 meteorological stations. Stepwise linear regression was applied to create 9 groups of predictors, which were trained and tested on each regression method. This study demonstrates the potential of machine learning algorithms to predict T_max: the gradient boosting model with a group of five predictors (land surface temperature, Julian day, normalised difference vegetation index, digital elevation model, solar zenith angle) was the regression model with the best performance (R² = 0.68, MAE = 1.60 °C, and RMSE = 2.03 °C). This methodological approach is capable of being replicated in other UK cities, benefiting national heat-related mortality assessments since the data (provided by NASA and the UK Met Office) and programming languages (Python) sources are free and open. This study provides a framework to produce a high spatio-temporal resolution of T_max, assisting public health researchers to improve the estimation of mortality attributable to high temperatures. In addition, the research contributes to practice and policy-making by enhancing the understanding of the locations where mortality rates may increase due to heat. Therefore, it enables a more informed decision-making process towards the prioritisation of actions to mitigate heat-related mortality amongst the vulnerable population.     

基于逆散射成像条件的最小二乘逆时偏移

最小二乘逆时偏移（LSRTM）相对于常规逆时偏移（RTM）具有分辨率更高、振幅更准确、噪音更少等优势,可以对复杂的地质构造进行有效的成像.这种迭代更新反演成像方法十分依赖目标函数的梯度质量和计算效率.当地质模型中存在强反射界面或者记录中存在折射波时,基于常规互相关成像条件（CCC）的最小二乘逆时偏移梯度会包含很强的低频噪音,从而使反演的收敛速度和成像质量降低.为此,本文在最小二乘逆时偏移的梯度中引进了逆散射成像条件来压制这种低频噪音,并以此提出基于逆散射成像条件（ISC）的最小二乘逆时偏移方法.数值模拟结果表明,两者计算耗时基本一致,但逆散射成像条件能高效压制梯度中的低频噪音,从而使反演过程中收敛加速,成像质量得到显著提高.     

The North-South Seismic Belt:Vertical Deformation Velocity Gradient Research

The vertical deformation gradient can reflect the rate of vertical change in unit distance, and the vertical deformation velocity gradient can reflect the strength of the earth''s crust tectonic activities. In this paper, using long period leveling data combined with GPS data, the vertical deformation gradient values are calculated. Leveling data and GPS data are two different means of monitoring deformation, but the result is approximately the same vertical deformation gradient. The results show that the spatial distribution of the vertical deformation velocity gradient and tectonic distribution has an obvious correlation. The most significant gradient anomalies along the North-South Seismic Belt are Xianshuihe fault, Longmenshan fault and Xiaojiang-Zemuhe fault, while the second gradient anomalies in the northeastern Qinghai-Tibetan plateau are Zhuanglanghe fault and Lenglongling fault. The Menyuan M_S6.4 earthquake in 2016 occurred in this abnormal area. However, according to the vertical deformation high gradient area distribution, there is also the possibility of an earthquake occurrence in the Tianzhu and Jingtai area. The area of convergence of three major fault zones is the strongest tectonically active region of the North-South Seismic Belt.