鲁北地震危险区地震灾害快速评估系统

以县为评估单元,将县城和农村区分开,采用确定性的地震灾害评估方法,建立了鲁北地震危险区地震灾害快速评估系统,破坏性地震发生后,能够快速盲肠评估出危险区内的房屋破坏,人员伤亡和经济损失情况,为抗震救灾提供决策依据。     

大庆台网记录地震震相滞后问题的研究

本文主要分析了大庆台网记录地震Ｐｇ震相滞后的现象,探讨大庆台网Ｐｇ震相的滞后程度及引起震相滞后的原因。     

青海省河南县5.1级地震与震后趋势讨论

对发生在 1 999年 9月 2 7日青海省河南县 5.1级地震的考察结果进行了报道 ,并对震后趋势进行了讨论     

医用X线CT及工业X线CT对中国尖齿兽筛板结构的观察及CT在古生物学研究中的应用价值

目的是观察杨氏中国尖齿兽有无筛板骨,探讨Ｘ线ＣＴ在古生物学研究中的应用价值。方法：选择扫描参数用传统ＣＴ、高档ＣＴ及工业ＣＴ分别对杨氏中国尖齿兽头骨化石标本进行扫描。结果：杨氏中国尖齿兽脑腔、鼻腔之间存在筛板骨,高档及工业ＣＳ能清晰显示筛板结构。结论：Ｘ线ＣＴ是无创性观察化石内部结构的方法,在古生物学研究方法中占据重要位置。     

长输埋地管道振动台试验传感器布置方案研究

在进行长输埋地管道振动台试验的过程中,针对数据信息的采集量测以及传感器的布置位置进行了研究。采用三维数值模拟的方法对管-土相互作用体系进行了地震反应分析,内容包括埋地管道结构纵、横向在非一致地震动作用下的地震响应及受力变形特征。根据计算结果确定了主观测断面及辅助观测断面的位置及观测断面上传感器布置的位置,在满足基本信息采集要求的前提下,对可供采用的信息采集通道进行了优化分配,由此确定本次试验的观测断面以及传感器的具体测量部位与数目。成果对试验获得成功起到了保障作用,可为同类试验提供参考。     

An improved HASM method for dealing with large spatial data sets

Surface modeling with very large data sets is challenging. An efficient method for modeling massive data sets using the high accuracy surface modeling method (HASM) is proposed, and HASM_Big is developed to handle very large data sets. A large data set is defined here as a large spatial domain with high resolution leading to a linear equation with matrix dimensions of hundreds of thousands. An augmented system approach is employed to solve the equality-constrained least squares problem (LSE) produced in HASM_Big, and a block row action method is applied to solve the corresponding very large matrix equations. A matrix partitioning method is used to avoid information redundancy among each block and thereby accelerate the model. Experiments including numerical tests and real-world applications are used to compare the performances of HASM_Big with its previous version, HASM. Results show that the memory storage and computing speed of HASM_Big are better than those of HASM. It is found that the computational cost of HASM_Big is linearly scalable, even with massive data sets. In conclusion, HASM_Big provides a powerful tool for surface modeling, especially when there are millions or more computing grid cells.     

A fundamental theorem for eco-environmental surface modelling and its applications

We propose a fundamental theorem for eco-environmental surface modelling(FTEEM) in order to apply it into the fields of ecology and environmental science more easily after the fundamental theorem for Earth's surface system modeling(FTESM). The Beijing-Tianjin-Hebei(BTH) region is taken as a case area to conduct empirical studies of algorithms for spatial upscaling, spatial downscaling, spatial interpolation, data fusion and model-data assimilation, which are based on high accuracy surface modelling(HASM), corresponding with corollaries of FTEEM. The case studies demonstrate how eco-environmental surface modelling is substantially improved when both extrinsic and intrinsic information are used along with an appropriate method of HASM. Compared with classic algorithms, the HASM-based algorithm for spatial upscaling reduced the root-meansquare error of the BTH elevation surface by 9 m. The HASM-based algorithm for spatial downscaling reduced the relative error of future scenarios of annual mean temperature by 16%. The HASM-based algorithm for spatial interpolation reduced the relative error of change trend of annual mean precipitation by 0.2%. The HASM-based algorithm for data fusion reduced the relative error of change trend of annual mean temperature by 70%. The HASM-based algorithm for model-data assimilation reduced the relative error of carbon stocks by 40%. We propose five theoretical challenges and three application problems of HASM that need to be addressed to improve FTEEM.     

Prediction of $$\hbox {PM}_{2.5}$$PM2.5 concentration based on weighted bagging and image contrast-sensitive features

Stochastic Environmental Research and Risk Assessment - In this paper, we propose an effective method of $$\hbox {PM}_{2.5}$$ prediction based on image contrast-sensitive features and weighted...     

Spatial Variability of Nitrate and Ammonium in Pleistocene Aquifer of Central Yangtze River Basin

It becomes increasingly important and challenging for nitrogen pollution prevention to identify key controls for spatial variability of nitrogen in groundwater that could be affected by multiple factors, including anthropogenic input, groundwater flow, and local geochemistry. This study characterized spatial variability of both nitrate and ammonium in the Pleistocene aquifer of central Yangtze River Basin and assessed the effect of various factors in controlling nitrate and ammonium levels based on multiple statistical approaches (correlation, geostatistics, multiple liner regression). The results indicate that nitrate is mostly influenced by Cl⁻ that represents anthropogenic input, while Eh representing local redox state is a secondary variable influencing nitrate concentrations. The groundwater with elevated nitrate concentrations are estimated to occur mainly in areas with higher-permeability near-surface sediments which can facilitate more anthropogenic nitrate transport and less nitrate removal owing to more oxidized state. Ammonium is mostly correlated to Eh, followed by dissolved organic carbon (DOC), but only DOC improves significantly the accuracy of co-kriging prediction model. The groundwater with elevated ammonium concentrations are estimated to occur mainly in areas with more organic-rich sediments within or around the aquifer which can facilitate more ammonium release owing to natural organic matter consumption accompanying strong reducing conditions. The regional groundwater flow is not a factor significantly controlling nitrate or ammonium levels owing to flat topography and sluggish lateral flow.