利用1981—2019年5—9月新疆105个国家基本气象站日最高气温观测资料及美国国家环境预报中心和大气研究中心(National Centers for Environmental Prediction/National Center for Atmospheric Research,NCEP/NCAR)逐日再分析资料,分析...利用1981—2019年5—9月新疆105个国家基本气象站日最高气温观测资料及美国国家环境预报中心和大气研究中心(National Centers for Environmental Prediction/National Center for Atmospheric Research,NCEP/NCAR)逐日再分析资料,分析新疆区域性高温天气过程的时空变化特征及环流形势。结果表明:(1)1981—2019年新疆共出现100次区域性高温天气过程,主要发生在6—8月,其中7月最多、8月次之、6月最少;区域性高温天气过程主要出现在伊犁河谷平原地区、北疆准噶尔盆地南缘、南疆塔里木盆地及东疆平原地区。(2)进入21世纪后,新疆高温天气过程发生次数呈增加趋势,强度明显增强;过程开始时间有提前趋势,结束时间有推后趋势;过程累计日数则呈现“增加、减少、增加”的阶段性变化趋势。(3)造成新疆区域性高温天气过程的500 hPa环流形势主要分为4类,分别为伊朗副高东伸型(占54.0%)、叠加型(占32.0%)、新疆脊型(占12.0%)、西太副高西伸型(占2.0%)。展开更多
Rockburst is a phenomenon in which free surfaces are formed during excavation,which subsequently causes the sudden release of energy in the construction of mines and tunnels.Light rockburst only peels off rock slices ...Rockburst is a phenomenon in which free surfaces are formed during excavation,which subsequently causes the sudden release of energy in the construction of mines and tunnels.Light rockburst only peels off rock slices without ejection,while severe rockburst causes casualties and property loss.The frequency and degree of rockburst damage increases with the excavation depth.Moreover,rockburst is the leading engineering geological hazard in the excavation process,and thus the prediction of its intensity grade is of great significance to the development of geotechnical engineering.Therefore,the prediction of rockburst intensity grade is one problem that needs to be solved urgently.By comprehensively considering the occurrence mechanism of rockburst,this paper selects the stress index(σθ/σc),brittleness index(σ_(c)/σ_(t)),and rock elastic energy index(Wet)as the rockburst evaluation indexes through the Spearman coefficient method.This overcomes the low accuracy problem of a single evaluation index prediction method.Following this,the BGD-MSR-DNN rockburst intensity grade prediction model based on batch gradient descent and a multi-scale residual deep neural network is proposed.The batch gradient descent(BGD)module is used to replace the gradient descent algorithm,which effectively improves the efficiency of the network and reduces the model training time.Moreover,the multi-scale residual(MSR)module solves the problem of network degradation when there are too many hidden layers of the deep neural network(DNN),thus improving the model prediction accuracy.The experimental results reveal the BGDMSR-DNN model accuracy to reach 97.1%,outperforming other comparable models.Finally,actual projects such as Qinling Tunnel and Daxiangling Tunnel,reached an accuracy of 100%.The model can be applied in mines and tunnel engineering to realize the accurate and rapid prediction of rockburst intensity grade.展开更多
文摘利用1981—2019年5—9月新疆105个国家基本气象站日最高气温观测资料及美国国家环境预报中心和大气研究中心(National Centers for Environmental Prediction/National Center for Atmospheric Research,NCEP/NCAR)逐日再分析资料,分析新疆区域性高温天气过程的时空变化特征及环流形势。结果表明:(1)1981—2019年新疆共出现100次区域性高温天气过程,主要发生在6—8月,其中7月最多、8月次之、6月最少;区域性高温天气过程主要出现在伊犁河谷平原地区、北疆准噶尔盆地南缘、南疆塔里木盆地及东疆平原地区。(2)进入21世纪后,新疆高温天气过程发生次数呈增加趋势,强度明显增强;过程开始时间有提前趋势,结束时间有推后趋势;过程累计日数则呈现“增加、减少、增加”的阶段性变化趋势。(3)造成新疆区域性高温天气过程的500 hPa环流形势主要分为4类,分别为伊朗副高东伸型(占54.0%)、叠加型(占32.0%)、新疆脊型(占12.0%)、西太副高西伸型(占2.0%)。
基金funded by State Key Laboratory for GeoMechanics and Deep Underground Engineering&Institute for Deep Underground Science and Engineering,Grant Number XD2021021BUCEA Post Graduate Innovation Project under Grant,Grant Number PG2023092.
文摘Rockburst is a phenomenon in which free surfaces are formed during excavation,which subsequently causes the sudden release of energy in the construction of mines and tunnels.Light rockburst only peels off rock slices without ejection,while severe rockburst causes casualties and property loss.The frequency and degree of rockburst damage increases with the excavation depth.Moreover,rockburst is the leading engineering geological hazard in the excavation process,and thus the prediction of its intensity grade is of great significance to the development of geotechnical engineering.Therefore,the prediction of rockburst intensity grade is one problem that needs to be solved urgently.By comprehensively considering the occurrence mechanism of rockburst,this paper selects the stress index(σθ/σc),brittleness index(σ_(c)/σ_(t)),and rock elastic energy index(Wet)as the rockburst evaluation indexes through the Spearman coefficient method.This overcomes the low accuracy problem of a single evaluation index prediction method.Following this,the BGD-MSR-DNN rockburst intensity grade prediction model based on batch gradient descent and a multi-scale residual deep neural network is proposed.The batch gradient descent(BGD)module is used to replace the gradient descent algorithm,which effectively improves the efficiency of the network and reduces the model training time.Moreover,the multi-scale residual(MSR)module solves the problem of network degradation when there are too many hidden layers of the deep neural network(DNN),thus improving the model prediction accuracy.The experimental results reveal the BGDMSR-DNN model accuracy to reach 97.1%,outperforming other comparable models.Finally,actual projects such as Qinling Tunnel and Daxiangling Tunnel,reached an accuracy of 100%.The model can be applied in mines and tunnel engineering to realize the accurate and rapid prediction of rockburst intensity grade.
