Landslide intelligent prediction using object-oriented method

Landslides are one of the most serious geological disasters in the world and happen quite frequently in the Three Gorges. Landslide prediction is a very important measure of landslide prevention and cure in the Three Gorges. Traditional methods lack in sufficiently mining the various complex information from a landslide system. They often need much manual intervention and possess poor intelligence and accuracy. An intelligent method proposed in this paper for landslide prediction based on an object-oriented method and knowledge driving is hopeful to solve the above problem. The method adopted Landsat ETM+ images, 1:50,000 geological map and 1:10,000 relief map in the Three Gorges as the data origins. It firstly produced the key factors influencing landslide development and used multi-resolution segmentation algorithm to segment the image objects based on the key landslide factors of engineering rock group, reservoir water fluctuation, slope structure and slope level. Secondly, the method chose some sample objects and adopted the decision tree algorithm C5.0 to mine the landslide forecast criteria according to the factor values of each sample object. Finally, under knowledge driving the method classified the image objects and realized landslide susceptibility analysis and intelligent prediction in the Three Gorges. The method proposed in this paper is object-oriented. Results of a real-world example show that: (1) the object-oriented method possesses much more compact knowledge representation, higher efficiency, more continuous classifying result and higher prediction accuracy compared with the pixel-oriented method; (2) it possesses the overall accuracy of 87.64% and kappa coefficient of 0.8305 and is more accurate than the other seven methods (such as the pixel-oriented methods of Parallelpiped, Minimum Distance, Maximum Likelihood, Mahalanobis Distance, K-means and Isodata and the object-oriented method of Nearest Neighbor); (3) about 46.97% landslides lie in the high susceptibility region, 24.24% landslides lie in the moderate susceptibility region, 27.27% landslides lie in the low susceptibility region and 1.52% landslides lie in the very low susceptibility region. Therefore the method can effectively realize landslide susceptibility analysis and provides a new idea for landslide intelligent and accurate prediction.     

新型随机地震动模型

在研究结构的随机地震反应时，要用大量的符合场地条件的地震记录作为输入数据。但强震历史记录却不是每个地区都有的，因此根据符合场地条件的现有地震记录建立随机地震动模型具有重要意义。本文利用中国抗震规范2001版修正选取的样本波作为目标波，考虑了幅值和频率的双重非平稳性，建立了新型随机地震动模型——改进的时变ARMA模型随机地震动模型。通过使用残差的卡方检验法，对多种非平稳ARMA模型生成的模拟波进行检验；同时又比较丁模拟波与目标波的功率谱密度图和反应谱图。结果证明：此法能够更精确地反映不同场地条件地震动的频谱和幅值的真实内容，从而建立符合目标场地条件的更为有效的模拟地震动，为相关研究与工程设计架起一座桥梁。     

Adsorption characteristics of copper ion on nanoporous silica

Adsorption by nanoporous media is critically involved in many fundamental geological and geochemical processes including chemical weathering,element migration and enrichment,environmental pollution,etc.Yet,the adsorption behavior of metal ions on nanoporous materials has not been systematically investigated.In this study,MCM-41 material with a monodisperse pore size(4.4 nm)and a large BET specific surface area(839 m^2/g)was hydrothermally prepared and used as a model silica adsorbent to study the adsorption characteristics of Cu^2+as a representative metal ion.The Cu^2+adsorption capacity was found to increase with increasing suspension pH in the range from 3 to 5 and to decrease in the presence of NaNO3.At 25℃,pH=5,and a solid-to-liquid ratio of 5 g/L,the adsorption capacity was determined to be 0.29 mg/g,which can be converted to a dimensionless partition coefficient of 45,indicating a strong enriching effect of nanoporous silica.The adsorption isotherm and kinetic data were fitted to several commonly used thermodynamic,kinetic,and diffusion models.The adsorption mechanism was also studied by Fourier transform infrared spectroscopy,X-ray photoelectron spectroscopy and synchrotron-based X-ray absorption spectroscopy.The results suggest that Cu2+ion adsorption is an entropy-driven endothermal process,possibly involving both outer-sphere and inner-sphere complexes.     

河北省及邻区数字电扰动干扰分析

通过对河北省及邻区数字电扰动观测资料进行分析和环境调查,发现主要干扰因素为供电或漏电干扰。如:昌黎地震台地电阻率整点测量受到供电干扰,三河地震台受到恒远自动化仪表厂工作时段供电干扰,高碑店地震台受到商场大型设备供电干扰,广平地震台受到UPS交流电源漏电干扰,昌平地震台受到地铁运行漏电干扰等。电扰动干扰形态相似,均为超过本底值几倍、数十倍乃至上百倍的高值脉冲变化。     

Prediction of vegetation anomalies over an inland river basin in north‐western China

Vegetation cover plays an important role in linking the atmosphere, water, and land and is deemed as a key indicator in the terrestrial ecological system. Therefore, it is of great importance to monitor vegetation dynamics and understand the mechanisms of vegetation change, including that driven by climate change. This study examines (a) the evolution of vegetation dynamics over the Heihe River Basin in the typical arid zone in north‐western China using nonparametric Mann–Kendall test and Thiel Sen's slope; (b) the relationships between remotely sensed vegetation indices (normalized difference vegetation index [NDVI] and enhanced vegetation index [EVI]) and hydroclimatic variables based on correlation analysis; and (c) the prediction of vegetation anomalies using a multiple linear regression model. For the analysis, the Moderate Resolution Imaging Spectroradiometer NDVI/EVI product and the gridded daily meteorological data at a spatial resolution of 0.125° over the period 2001–2010 are considered. The results indicate that vegetation cover improved over a large proportion during 2001–2010, with a significant trend towards warm and wet, characterized by an increase in average annual temperature and precipitation by 0.042 °C/year and 5.8 mm/year, respectively. We test the feasibility of NDVI and EVI in quantifying the responses of vegetation anomaly to climate change and develop a statistical model to predict vegetation dynamics in the basin. The NDVI‐based model is found to be more reliable than the EVI‐based model, partly due to the vegetation characteristics and geomorphologic properties of the study region. The proposed model performs well when there is no lag time between meteorological factors and vegetation indices for grassland and cropland, whereas 1‐month lead time prediction is found to be best for forest. The soil water content is introduced as an extra explanatory variable, which effectively improves the prediction accuracy for different land use types. In general, the predictive ability of the proposed model is stable and satisfactory, and the model can provide useful early warning information for regional water resources management under changing climate.     

银额盆地石板泉西石炭系白山组碳酸盐岩地球化学特征及其环境意义

对银额盆地石板泉西石炭系白山组碳酸盐岩进行详细的地球化学分析,探讨各特征元素对古环境、古气候的指示意义。地球化学测试结果显示：除样品CH3外,其余样品主量元素中的Al及微量元素中的Ti、Zr、Th等典型陆源特征元素的含量较低,Y/Ho平均值为45.58,反映研究区白山组碳酸盐岩较少受陆源物质输入的影响;δCe与δEu、w（ΣREE）、（La/Sm）_N未见良好的相关性,也不具备Eu正异常,说明研究区白山组碳酸盐岩受成岩作用和热液作用的影响也较为有限。碳酸盐岩中所含微量元素主要为海水自生成因,能够反映原始沉积环境。根据特征元素对古水深、古盐度、古气候、古氧相和古水温进行研究,显示研究区白山组碳酸盐岩段虽然岩性较为单一,但其地球化学特征可以反映早晚的沉积古环境差异。研究区白山组碳酸盐岩总体经历了1个大的海侵海退旋回,沉积于正常海水,气候潮湿,弱氧化—弱还原环境,古水温为8.51~27.10℃,早期水温低,晚期水温升高。     

Characteristics of inorganic ions in precipitation at different altitudes in the Yulong Snow Mountain, China

The present study investigated the chemical composition of summer precipitation at different altitudes in Yulong Snow Mountain, hoping to obtain some basic information about the chemical characteristics of precipitation. A couple of precipitation samples at different altitudes in the Yulong Snow Mountain area in southeastern China were collected from July 26 to July 31, 2005 for chemical analyses. Sea-salt tracer analysis showed that Cl^? and Na⁺ had high proportion from marine sources, while the other ions mainly were from non-marine sources. Statistics showed that the cation concentrations ranged from 0.51 to 5.26 mg L^?1 and anion concentrations ranged from 0.99 to 4.90 mg L^?1. Further analysis showed that terrestrial ions, especially from the proximal terrestrial sources, largely affected the ion concentrations in precipitation. Ion concentrations of precipitation were correlated significantly with local atmospheric circulation at altitude 2,950–4,800 m, and also correlated closely with human activities at altitude 2,400–2,950 m. Limestone, the majority of local rocks, caused high cation concentrations in precipitation, such as Ca²⁺ and Mg²⁺. The majority of vapors in the region came from the Indian Ocean whereas parts came from the eastern Pacific. The inorganic ions in precipitation increased significantly in the Yulong Snow Mountain area during this century. Compared with most of the high altitude Mountains in China, the precipitations in the Yulong Snow Mountain area had higher concentrations of inorganic ions and low acidic ion concentrations. The conclusions can provide a basis for study of regional climate characteristics, environment and ecological characteristics of Yulong Snow Mountain area.     

滦县地磁场井下观测系统抗干扰效果

近距离铁磁性物质干扰(农田耕作、基建等)、直流供电干扰(高压直流输电、地电阻率观测等)是地磁场地面观测的主要干扰方式。在滦县地震台布设地磁场井下观测系统,检验井下观测的抗干扰效果,结果发现,地磁场井下观测可抑制近距离铁磁性物质干扰,但无法抑制直流供电干扰(可通过避让一定距离解决),该分析结果为未来地震监测手段的选取提供技术参考指标和依据。     

土-桩-钢结构相互作用体系的振动台模型试验

本项试验是研究土-结构相互作用对结构TLD减震控制影响的系列振动台模型试验的组成部分,主要目的是提供相应的对比试验数据。在这组试验中,突出的问题是TLD、上部被控结构和土层三个子系统模型基频间的协调。为了尽可能降低土层模型基频,文中采用了一种具有低剪切波速特性的土层模拟介质。最后,通过土-桩-钢结构系统的振动台模型试验,研究了土-结构相互作用对钢结构动力特性和地震反应的影响。