Gas Flow Characteristics and Optimization of Gas Drainage Borehole Layout in Protective Coal Seam Mining: A Case Study from the Shaqu Coal Mine,Shanxi Province,China

With increasing demands for coal resources, coal has been gradually mined in deep coal seams. Due to high gas content, pressure and in situ stress, deep coal seams show great risks of coal and gas outburst. Protective coal seam mining, as a safe and effective method for gas control, has been widely used in major coal-producing countries in the world. However, at present, the relevant problems, such as gas seepage characteristics and optimization of gas drainage borehole layout in protective coal seam mining have been rarely studied. Firstly, by combining with formulas for measuring and testing permeability of coal and rock mass in different stress regimes and failure modes in the laboratory, this study investigated stress–seepage coupling laws by using built-in language Fish of numerical simulation software FLAC^3D. In addition, this research analyzed distribution characteristics of permeability in a protected coal seam in the process of protective coal seam mining. Secondly, the protected coal seam was divided into a zone with initial permeability, a zone with decreasing permeability, and permeability increasing zones 1 and 2 according to the changes of permeability. In these zones, permeability rises the most in the permeability increasing zone 2. Moreover, by taking Shaqu Coal Mine, Shanxi Province, China as an example, layout of gas drainage boreholes in the protected coal seam was optimized based on the above permeability-based zoning. Finally, numerical simulation and field application showed that gas drainage volume and concentration rise significantly after optimizing borehole layout. Therefore, when gas is drained through boreholes crossing coal seams during the protective coal seam mining in other coal mines, optimization of borehole layout in Shaqu Coal Mine has certain reference values.

     

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.

     

基于TRMM 3B43产品的1998—2016年黄淮海平原降水时空特征研究

以黄淮海平原为研究区,基于59个气象站点的逐月降水数据,采用相关系数（R）、相对误差（BIAS）和均方根误差（RMSE）对TRMM 3B43降水产品的精度进行系统评估,揭示黄淮海平原降水的时空变化规律。研究表明：① TRMM 3B43产品对研究区内大部分气象站点存在轻微高估现象,总体上TRMM产品和站点数据在黄淮海平原的拟合效果较好,且月尺度的精度较年尺度高;② 1998—2016年黄淮海平原年降水和秋季降水增加明显,其余季节降水呈不同程度的减少趋势;③ 研究区降水在空间上呈从南到北递减的趋势,且南北降水差距较大。研究结果为卫星降水产品在中纬度地区水资源评估、农业生产和洪旱灾害等研究提供一定的参考。     

例谈比较法在培养学生思维能力中的作用——以“欧洲西部”的教学为例

比较法是地理教学中的一种常用方法。本文选择欧洲西部有代表性的国家"英国",通过它与已学习过的、与英国有很多相似之处的"日本"进行比较教学,完成"欧洲西部"部分内容的学习。在教学的过程中,采用在对比中进行迁移、辨别概念、分析成因、探究变化、建构体系等方式,提升学生的横向思维能力、分析思维能力、推理思维能力、辩证思维能力、归纳思维能力,从而达到促进学生思维能力提升的目的。     

新冠疫情期间武汉樱花的情感效应

运用大数据文本挖掘和分析工具,对比新冠疫情期间武汉和非武汉地区居民所发布的微文,探究了武汉樱花在不同居民群体间情感效应的作用机制,研究发现：1）本地居民樱花微文的关注点多而分散,非本地居民的关注点少而集中;2）武汉樱花对不同地区居民情感的影响与疫情发展状况紧密相关;3）武汉樱花在疫情期间对武汉居民具有情感支持效应,对非武汉地区居民具有情感凝聚效应;4）疫情改变人的情感和景观解读背景,并赋予景观新的意义,新的景观意义反作用于居民,构成情感闭合回路。最后提出建议,抗疫恢复工作应多关注居民心理层面的缺失,从居民与城市、景观的情感关系入手,向武汉居民传达城市美好生活理念。     

陕西省HFRS疫情时空分异特征及影响要素研究

肾综合征出血热（HFRS）主要是由鼠类携带传播汉坦病毒而引起的一类自然疫源性传染病,严重危害着人类健康。陕西省是我国HFRS疫情最严重的省份之一,发病率居全国前列,研究其疫情时空分异和影响要素对指导当地疫情防控具有重要意义。本研究基于2005—2017年县区尺度HFRS发病率数据,采用空间自相关、热点分析等方法分析陕西省疫情时空分异特征,并利用地理探测器探究影响疫情的主要自然环境和社会经济要素。结果表明：2005—2017年陕西省HFRS发病率明显高于全国水平,同时呈现明显的时间波动和空间聚集,平原面积占比、建设用地面积占比、人口密度等因素可以解释约20%的HFRS疫情空间分异;关中平原聚集了陕西省90%以上的高发病县区,其疫情亦呈现明显的空间分异性,主要受降水量、 NDVI、土地利用类型等因素的影响。由此可知,高发病县区聚集、且自然环境和社会经济条件明显不同的关中平原是陕西省HFRS疫情流行的关键地区。因此,建议陕西省HFRS疫情防控应当重点关注降水量、植被状况以及土地利用类型,特别是在土地城镇化水平较高、人口密度较大的关中平原进行有效的防控干预。     

Laboratory Investigation into the Evaporation of Natural-Gas Condensate Oils: Hints for the Sanchi Oil Spill

The Sanchi oil tanker collision in the East China Sea on January 6th, 2018 has caused worldwide attention due to its uniqueness. A considerable amount of h     

城市地下人防三维建模及可视化应用

随着科技的发展原有的地下人防二维地形图已经无法满足人们的需要,人们更多的目光放在了实现地下人防三维智能化管理上。本文在此基础上,运用3DMAXS和Photoshop将采集的二维CAD图形与遥感影像相结合转化为三维模型,最后通过C#运用ArcGIS Engine为接口实现模型的三维场景重现,以及三维智能化管理。     

THE EFFECT OF EQUATORIAL CENTRAL PACIFIC SSTAS ON THE INTERANNUAL VARIATION OF PRECIPITATION OVER SOUTHWEST CHINA DURING SPRING

The interannual variations of rainfall over southwest China (SWC) during spring and its relationship with sea surface temperature anomalies (SSTAs) in the Pacific are analyzed, based on monthly mean precipitation data from 26 stations in SWC between 1961 and 2010, NCEP/NCAR re-analysis data, and Hadley global SST data. Sensitivity tests are conducted to assess the response of precipitation in SWC to SSTAs over two key oceanic domains, using the global atmospheric circulation model ECHAM5. The interannual variation of rainfall over SWC in spring is very significant. There are strong negative (positive) correlation coefficients between the anomalous precipitation over SWC and SSTAs over the equatorial central Pacific (the mid-latitude Pacific) during spring. Numerical simulations show that local rainfall in the northwest of the equatorial central Pacific is suppressed, and a subtropical anticyclone circulation anomaly is produced, while a cyclonic circulation anomaly in the mid-latitude western Pacific occurs, when the equatorial Pacific SSTAs are in a cold phase in spring. Anomalous northerly winds appear in the northeastern part of SWC in the lower troposphere. Precipitation increases over the Maritime Continent of the western equatorial Pacific, while a cyclonic circulation anomaly appears in the northwest of the western equatorial Pacific. A trough over the Bay of Bengal enhances the southerly flow in the south of SWC. The trough also enhances the transport of moisture to SWC. The warm moisture intersects with anomalous cold air over the northeast of SWC, and so precipitation increases during spring. On the interannual time scale, the impacts of the mid-latitude Pacific SSTAs on rainfall in SWC during spring are not significant, because the mid-latitude Pacific SSTAs are affected by the equatorial central Pacific SSTAs; that is, the mid-latitude Pacific SSTAs are a feedback to the circulation anomaly caused by the equatorial central Pacific SSTAs.     

Assessment of PPP integer ambiguity resolution using GPS,GLONASS and BeiDou (IGSO,MEO) constellations

Although integer ambiguity resolution (IAR) can improve positioning accuracy considerably and shorten the convergence time of precise point positioning (PPP), it requires an initialization time of over 30 min. With the full operation of GLONASS globally and BDS in the Asia–Pacific region, it is necessary to assess the PPP–IAR performance by simultaneous fixing of GPS, GLONASS, and BDS ambiguities. This study proposed a GPS + GLONASS + BDS combined PPP–IAR strategy and processed PPP–IAR kinematically and statically using one week of data collected at 20 static stations. The undifferenced wide- and narrow-lane fractional cycle biases for GPS, GLONASS, and BDS were estimated using a regional network, and undifferenced PPP ambiguity resolution was performed to assess the contribution of multi-GNSSs. Generally, over 99% of a posteriori residuals of wide-lane ambiguities were within ±0.25 cycles for both GPS and BDS, while the value was 91.5% for GLONASS. Over 96% of narrow-lane residuals were within ±0.15 cycles for GPS, GLONASS, and BDS. For kinematic PPP with a 10-min observation time, only 16.2% of all cases could be fixed with GPS alone. However, adding GLONASS improved the percentage considerably to 75.9%, and it reached 90.0% when using GPS + GLONASS + BDS. Not all epochs could be fixed with a correct set of ambiguities; therefore, we defined the ratio of the number of epochs with correctly fixed ambiguities to the number of all fixed epochs as the correct fixing rate (CFR). Because partial ambiguity fixing was used, when more than five ambiguities were fixed correctly, we considered the epoch correctly fixed. For the small ratio criteria of 2.0, the CFR improved considerably from 51.7% for GPS alone, to 98.3% when using GPS + GLONASS + BDS combined solutions.