羊石坑汞矿床包裹体地球化学特征研究

羊石坑汞矿是80年代后期勘探的大型汞矿床,矿床位于川黔汞矿带北部,矿体以细脉、网脉浸染状为主。包裹体地球化学研究表明,成矿温度120～250℃,成矿流体盐度为10.6％(NaCl),流体沸腾发生在250℃左右,包裹体圈闭的最小压力为40×10~5Pa,成矿深度约为400m。结合成矿构造环境、构造演化、探讨了构造改造成矿机制和规律。     

中低纬过渡地区潜热影响下中尺度扰动的对称发展

本文引入第二科里奥利参数讨论了中低纬过渡地区含非绝热(潜热)作用的对称扰动波包发展问题。结果表明:这一地区扰动波包的发展不仅包含了中高纬度波包发展条件［1］，而且与潜热和基本风场梯度经向切变及波包传播的非垂直性有关。过渡地区基本风场的作用是通过第一和第二科里奥利参数完成的。     

应用成煤构造理论 杨梅山矿找煤告捷

笔者应用历史－因果论的成煤构造理论，采用历史分析法和动力分析法相结合的方法，在构造复杂的杨梅山矿区找煤，使一对老矿山增了储量，延长了寿命，使一对已报废的矿井重见曙光。     

龙门山前陆盆地形成与演化

本文将龙门山前陆盆地和龙门山冲断带作为一个地质整体进行了研究，并将构造地层学和层序地层学相结合的综合地层学的分析方法作为分析龙门山前陆盆地沉积的基础，以不整合面和其对应面将盆地充填序列分割为构造层序和层序，对应于不同成盆期和同一成盆期不同演化阶段，阐明龙门山前陆盆地充填序列和沉积体积三维空间配置形式及其在时间上的演化，初步建立了龙门山前陆盆地地层格架，进而研究龙门山冲断带逆部推覆作用对龙门山前陆盆     

重庆大学危岩锚固工程简介

重庆大学18层高层建筑物拟建在嘉陵江高陡斜坡脚下,斜坡上部陡崖为侏罗系上沙溪庙组巨厚层砂岩,岩石原生及岸剪裂隙发育,危岩直接威胁着建筑物。川地208队采用锚杆加固及涂抹砂浆水泥护坡综合防治工程,效果明显。     

不同晶形石盐沉积生长特征及对包裹体温度的影响

石盐晶体生长过程中捕获流体包裹体，利用冷冻测温法得到的均一温度信息与古水温存在良好的相关性。在部分地区，利用包裹体最大均一温度评价古气候时，温度数据与孢粉学反映的气候特征存在冲突，不同晶形的石盐沉积特征，及其晶体条纹内包裹体温度代表的地质含义需进一步探讨。笔者在分析经典测温数据的基础上，观察常温蒸发实验中漏斗晶和人字晶的形成及生长过程，分析了不同温度段均一温度与气温、水温之间的关系。结合现代气象记录数据，分析了最大均一温度评价气候时存在的局限性。研究认为：漏斗晶晶核形成与卤水表面，包裹体温度受温度和气压共同影响，漏斗晶在卤水底部绕核生长，漏斗晶外围包裹体温度对应水底温度，沉入水下后漏斗晶生长缓慢，包裹体最大温度可能代表年度最高温度，以此为指标评价气候得出的结论过于炎热；人字晶是多个漏斗晶之间的桥接部分，大部分形成于卤水蒸发将近结束之时，人字形条纹内包裹体温度受气温和地表温度共同影响，地表温度远大于气温是造成包裹体最高温度过高的主要原因。     

An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: cases with different types of spatial data

ABSTRACT

Spatial heterogeneity represents a general characteristic of the inequitable distributions of spatial issues. The spatial stratified heterogeneity analysis investigates the heterogeneity among various strata of explanatory variables by comparing the spatial variance within strata and that between strata. The geographical detector model is a widely used technique for spatial stratified heterogeneity analysis. In the model, the spatial data discretization and spatial scale effects are fundamental issues, but they are generally determined by experience and lack accurate quantitative assessment in previous studies. To address this issue, an optimal parameters-based geographical detector (OPGD) model is developed for more accurate spatial analysis. The optimal parameters are explored as the best combination of spatial data discretization method, break number of spatial strata, and spatial scale parameter. In the study, the OPGD model is applied in three example cases with different types of spatial data, including spatial raster data, spatial point or areal statistical data, and spatial line segment data, and an R “GD” package is developed for computation. Results show that the parameter optimization process can further extract geographical characteristics and information contained in spatial explanatory variables in the geographical detector model. The improved model can be flexibly applied in both global and regional spatial analysis for various types of spatial data. Thus, the OPGD model can improve the overall capacity of spatial stratified heterogeneity analysis. The OPGD model and its diverse solutions can contribute to more accurate, flexible, and efficient spatial heterogeneity analysis, such as spatial patterns investigation and spatial factor explorations.     

Improvement of spatial interpolation accuracy of daily maximum air temperature in urban areas using a stacking ensemble technique

ABSTRACT

The reliable and robust monitoring of air temperature distribution is essential for urban thermal environmental analysis. In this study, a stacking ensemble model consisting of multi-linear regression (MLR), support vector regression (SVR), and random forest (RF) optimized by the SVR is proposed to interpolate the daily maximum air temperature (T_max) during summertime in a mega urban area. A total of 10 geographic variables, including the clear-sky averaged land surface temperature and the normalized difference vegetation index, were used as input variables. The stacking model was compared to Cokriging, three individual data-driven methods, and a simple average ensemble model, all through leave-one-station-out cross validation. The stacking model showed the best performance by improving the generalizability of the individual models and mitigating the sensitivity to the extreme daily T_max. This study demonstrates that the stacking ensemble method can improve the accuracy of spatial interpolation of environmental variables in various research fields.