The biaxial compression mechanical properties of crushed rock

Crushed rock subgrade, as one of the roadbed-cooling methods, has been widely used in the Qinghai-Tibet Railway. Much attention has been paid on the cooling effect of crushed rock; however, the mechanical properties of crushed rock are somehow neglected. Based on the discrete element method, biaxial compression test condition for crushed rock is compiled in FISH language in PFC^2D, and the natural shape of crushed rock is simulated with super particle "cluster". The effect of particle size, crushed rock strength and confining pressure level on overall mechanical properties of the crushed rock aggregate are respectively analyzed. Results show that crushed rock of large particle size plays an essential framework role, which is mainly responsible for the deformation of crushed rock aggregate. The strength of gravel has a great influence on overall mechanical properties which means that strength attenuation caused by the freeze thaw cycles cannot be ignored. The stress-strain curves can be divided into two stages including shear contraction and shear expansion at different confining pressures.     

低纬和中高纬度火山爆发与我国旱涝的联系

根据５００年旱涝等级资料，采用时序迭加方法，分析了低纬和中高纬火山爆发对我国降水的影响。此外，还对１６００－１９７９年南方涛动指数的变化进行了类似的分析。结果表明，低纬和中高纬火山爆发发后全国旱涝分布型和部分地区降水变化趋势有很大差异。爆发当年华北就可能明显变旱，而次年长江流域才出现明显的降水异常。计算不明，１９９１年皮纳图博火山及去仙岳火山爆发与江淮特大洪涝有直接联系的可能性不大。     

青藏高原地表反照率计算研究

根据改进的甚高分辨率扫描辐射仪（ＡＶＨＲＲ）５个观测波段的光谱特征，经多次试验，设计了一组从卫星观测的地－气系统的辐射测值中提取晴空资料的多通道门槛值判识法和提取月平均反照率的合成法；并对１９９２年ＮＯＡＡ－１２卫星获取的ＡＶＨＲＲ资料进行计算处理，分析提取晴空数据，在此基础上按卫星轨道覆盖周期合成计算得到的晴空行星反照率和地表反照率，并且计算了逐月的地表反照率。对计算结果做了初步分析和认真比较。     

The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area,China

Landslide susceptibility mapping is an indispensable prerequisite for landslide prevention and reduction. At present, research into landslide susceptibility mapping has begun to combine machine learning with remote sensing and geographic information system (GIS) techniques. The random forest model is a new integrated classification method, but its application to landslide susceptibility mapping remains limited. Landslides represent a serious threat to the lives and property of people living in the Zigui–Badong area in the Three Gorges region of China, as well as to the operation of the Three Gorges Reservoir. However, the geological structure of this region is complex, involving steep mountains and deep valleys. The purpose of the current study is to produce a landslide susceptibility map of the Zigui–Badong area using a random forest model, multisource data, GIS, and remote sensing data. In total, 300 pre-existing landslide locations were obtained from a landslide inventory map. These landslides were identified using visual interpretation of high-resolution remote sensing images, topographic and geologic data, and extensive field surveys. The occurrence of landslides is closely related to a series of environmental parameters. Topographic, geologic, Landsat-8 image, raining data, and seismic data were used as the primary data sources to extract the geo-environmental factors influencing landslides. Thirty-four layers of causative factors were prepared as predictor variables, which can mainly be categorized as topographic, geological, hydrological, land cover, and environmental trigger parameters. The random forest method is an ensemble classification technique that extends diversity among the classification trees by resampling the data with replacement and randomly changing the predictive variable sets during the different tree induction processes. A random forest model was adopted to calculate the quantitative relationships between the landslide-conditioning factors and the landslide inventory map and then generate a landslide susceptibility map. The analytical results were compared with known landslide locations in terms of area under the receiver operating characteristic curve. The random forest model has an area ratio of 86.10%. In contrast to the random forest (whole factors, WF), random forest (12 major factors, 12F), decision tree (WF), decision tree (12F), the final result shows that random forest (12F) has a higher prediction accuracy. Meanwhile, the random forest models have higher prediction accuracy than the decision tree model. Subsequently, the landslide susceptibility map was classified into five classes (very low, low, moderate, high, and very high). The results demonstrate that the random forest model achieved a reasonable accuracy in landslide susceptibility mapping. The landslide hazard zone information will be useful for general development planning and landslide risk management.     

多普勒雷达资料在暴雨临近预报中的应用

以天气雷达体积扫描的资料为基础,采用交叉相关法建立了一种对暴雨的临近预报方法。该方法的基本思路是：将第一时刻取得的雷达回波图像,向任一方向移过一定的距离,然后计算此图像与第二时刻图像之间的交叉相关系数,直到找到极大值,利用该极大值所对应的位移来预报下一时刻降水的位置。并利用“973”中国暴雨试验获得的观测资料对该方法进行了验证,验证结果表明：交叉相关法对暴雨有一定的预报能力,预报雨带的移动方向与雷达实际观测的移动方向相吻合,但其预报能力随着预报时间的增加而减弱。     

江西贵溪盆地罗塘群沉积体系特征及其地质意义

早白垩世晚期江西贵溪盆地的控盆断裂活动加强,在盆地边缘的不同地带及盆地中心发育有不同的沉积体系,野外露头岩相特征和剖面的研究表明,贵溪盆地罗塘群主要发育有冲积扇体系、扇三角洲体系、辫状河三角洲体系、湖泊沉积体系及辫状河沉积体系,沉积体系特征主要受控于盆缘断裂活动,不同的沉积体系出现在盆地演化的不同阶段,并由不同的岩相组成.沉积体系的特征、分布及组合型式反映了陆相断陷红盆沉积相带窄、横向及纵向上相变快的特点.     

Impact of satellite observed microwave SST on the simulation of tropical cyclones

The impact of realistic representation of sea surface temperature (SST) on the numerical simulation of track and intensity of tropical cyclones formed over the north Indian Ocean is studied using the Weather Research and Forecast (WRF) model. We have selected two intense tropical cyclones formed over the Bay of Bengal for studying the SST impact. Two different sets of SSTs were used in this study: one from TRMM Microwave Imager (TMI) satellite and other is the weekly averaged Reynold’s SST analysis from National Center for Environmental Prediction (NCEP). WRF simulations were conducted using the Reynold’s and TMI SST as model boundary condition for the two cyclone cases selected. The TMI SST which has a better temporal and spatial resolution showed sharper gradient when compared to the Reynold’s SST. The use of TMI SST improved the WRF cyclone intensity prediction when compared to that using Reynold’s SST for both the cases studied. The improvements in intensity were mainly due to the improved prediction of surface latent and sensible heat fluxes. The use of TMI SST in place of Reynold’s SST improved cyclone track prediction for Orissa super cyclone but slightly degraded track prediction for cyclone Mala. The present modeling study supports the well established notion that the horizontal SST gradient is one of the major driving forces for the intensification and movement of tropical cyclones over the Indian Ocean.     

Analysis of decade-long time series of GPS-based polar motion estimates at 15-min temporal resolution

We present results from the generation of 10-year-long continuous time series of the Earth’s polar motion at 15-min temporal resolution using Global Positioning System ground data. From our results, we infer an overall noise level in our high-rate polar motion time series of 60 \(\upmu \hbox {as}\) (RMS). However, a spectral decomposition of our estimates indicates a noise floor of 4 \(\upmu \hbox {as}\) at periods shorter than 2 days, which enables recovery of diurnal and semidiurnal tidally induced polar motion. We deliberately place no constraints on retrograde diurnal polar motion despite its inherent ambiguity with long-period nutation. With this approach, we are able to resolve damped manifestations of the effects of the diurnal ocean tides on retrograde polar motion. As such, our approach is at least capable of discriminating between a historical background nutation model that excludes the effects of the diurnal ocean tides and modern models that include those effects. To assess the quality of our polar motion solution outside of the retrograde diurnal frequency band, we focus on its capability to recover tidally driven and non-tidal variations manifesting at the ultra-rapid (intra-daily) and rapid (characterized by periods ranging from 2 to 20 days) periods. We find that our best estimates of diurnal and semidiurnal tidally induced polar motion result from an approach that adopts, at the observation level, a reasonable background model of these effects. We also demonstrate that our high-rate polar motion estimates yield similar results to daily-resolved polar motion estimates, and therefore do not compromise the ability to resolve polar motion at periods of 2–20 days.     

Strength of Stacking Technique of Ensemble Learning in Rockburst Prediction with Imbalanced Data: Comparison of Eight Single and Ensemble Models

Rockburst is a common dynamic geological hazard, severely restricting the development and utilization of underground space and resources. As the depth of excavation and mining increases, rockburst tends to occur frequently. Hence, it is necessary to carry out a study on rockburst prediction. Due to the nonlinear relationship between rockburst and its influencing factors, artificial intelligence was introduced. However, the collected data were typically imbalanced. Single algorithms trained by such data have low recognition for minority classes. In order to handle the problem, this paper employed stacking technique of ensemble learning to establish rockburst prediction models. In total, 246 sets of data were collected. In the preprocessing stage, three data mining techniques including principal component analysis, local outlier factor and expectation maximization algorithm were used for dimension reduction, outlier detection and outlier substitution, respectively. Then, the pre-processed data were split into a training set (75%) and a test set (25%) with stratified sampling. Based on the four classical single intelligent algorithms, namely k-nearest neighbors (KNN), support vector machine (SVM), deep neural network (DNN) and recurrent neural network (RNN), four ensemble models (KNN–RNN, SVM–RNN, DNN–RNN and KNN–SVM–DNN–RNN) were built by stacking technique of ensemble learning. The prediction performance of eight models was evaluated, and the differences between single models and ensemble models were analyzed. Additionally, a sensitivity analysis was conducted, revealing the importance of input variables on the models. Finally, the impact of class imbalance on the prediction accuracy and fitting effect of models was quantitatively discussed. The results showed that stacking technique of ensemble learning provides a new and promising way for rockburst prediction, which exhibits unique advantages especially when using imbalanced data.

     

Respiration of Recently-Fixed Plant Carbon Dominates Mid-Winter Ecosystem CO2 Production in Sub-Arctic Heath Tundra

Arctic ecosystems could provide a substantial positive feedback to global climate change if warming stimulates below-ground CO₂ release by enhancing decomposition of bulk soil organic matter reserves.Ecosystem respiration during winter is important in this context because CO₂ release from snow-covered tundra soils is a substantial component of annual net carbon (C) balance, and because global climate models predict that the most rapid rises in regional air temperature will occur in the Arctic during winter. In this manipulative field study, the relative contributions of plant and bulk soil organic matter C pools to ecosystem CO₂ production in mid-winter were investigated. We measured CO₂ efflux rates in Swedish sub-arctic heath tundra from control plots and from plots that had been clipped in the previous growing season to disrupt plant activity. Respiration derived from recently-fixed plant C (i.e., plant respiration, and respiration associated with rhizosphere exudates and decomposition of fresh litter) was the principal source of CO₂ efflux, while respiration associated with decomposition of bulk soil organic matter was low, and appeared relatively insensitive to temperature. These results suggest that warmer mid-winter temperatures in the Arctic may have a much greater impact on the cycling of recently-fixed, plant-associated C pools than on the depletion of tundra bulk soil C reserves, and consequently that there is a low potential for significant initial feedbacks from arctic ecosystems to climate change during mid-winter.