In this study,the moment tensor of transversely isotropic shale was analyzed using a discrete element method-acoustic emission model(DEM-AE model).Firstly,the failure modes of the shale obtained from the acoustic emis...In this study,the moment tensor of transversely isotropic shale was analyzed using a discrete element method-acoustic emission model(DEM-AE model).Firstly,the failure modes of the shale obtained from the acoustic emission(AE) events and physical experiments were compared.Secondly,the relationships between AE events and seismic magnitudes,and AE events and the resulting cracks were analyzed.Finally,a moment tensor T-k chart describing the seismic source was introduced to demonstrate the differences in the transversely isotropic shale.The results showed that,for different anisotropy angles,a linear logarithmic relationship existed between the cumulative AE events and the seismic magnitude in the concentration area of the AE events.A normal distribution was observed for the number of AE events as the seismic magnitude changed from small to large.The moment tensor T-k chart indicated that the number and proportion of linear tension cracks in the shale were highest.When θ = 30°,the peak seismic magnitude was at a minimum.The average seismic magnitude in the concentration area of the AE events was also relatively small.Points close to the U=-1/3V line and the number of cracks included in a single AE event were at a minimum,and the corresponding peak stress also reached its lowest level.In contrast,when θ=90°,all related parameters were contrary to the above θ = 30° case.The DEM-AE model and the moment tensor T-k chart are suitable for analyzing the distribution of shale cracks appearing during the loading process.This study can provide constructive references for future research on the fracturing treatment of shale.展开更多
大型活动散场期间的地铁车站客流属于可预知的非常规客流,采用常规客流的统计预测方法难以准确预测其客流需求。基于深度学习,将历史客流规律、大型活动数据与实时自动售检票系统数据相结合,提出了一种适用于大型活动散场期间地铁车站...大型活动散场期间的地铁车站客流属于可预知的非常规客流,采用常规客流的统计预测方法难以准确预测其客流需求。基于深度学习,将历史客流规律、大型活动数据与实时自动售检票系统数据相结合,提出了一种适用于大型活动散场期间地铁车站的短时客流预测模型。首先对历史客流数据进行了拆分及降噪处理,并分析了活动客流特征。之后,基于深度学习框架构建多层结构的卷积神经网络,拟合活动客流特征与客流时空分布的映射关系,并选取Adam(adaptive moment estimation)算法优化训练过程,以适用于活动散场时客流集中进站的情况。最后,以北京地铁奥林匹克公园站为例,利用实测数据验证了模型的准确性。预测结果表明:建立的Adam-CNN(convolution neural network)模型相对于常用时间序列方法自回归滑动平均和传统神经网络SGD-CNN模型具有更高的精度,能够为大型活动的组织提供更为有力的支持。展开更多
基于标准化后的高分辨率气候代用资料,应用高阶矩分析方法检测近2000年来气候极端异常演变特征;同时结合滤波方法进行具有物理背景的层次分离,进而研究了各时间层次气候极端异常变化信息及其贡献.结果表明:1)在100年以上的时间层次上,...基于标准化后的高分辨率气候代用资料,应用高阶矩分析方法检测近2000年来气候极端异常演变特征;同时结合滤波方法进行具有物理背景的层次分离,进而研究了各时间层次气候极端异常变化信息及其贡献.结果表明:1)在100年以上的时间层次上,可能存在千年左右的气候变化振荡周期,而且20世纪是近2000年来气候极端异常现象最为活跃的时段,可能对应于气候极端异常现象活跃期.2)对于20—60年这一时间层次,公元300—1100年间气候极端异常现象比较明显,而公元1100—1980年间相对比较缓和;该层次对20世纪的气候异常没有显著贡献.世纪以上和20—60年时间层次均揭示出在近2000年的气候变化中,公元1100年前后可能是一个气候极端异常现象演变的关键转折时期.3)在年际尺度上(小于20年),北京石花洞石笋微层厚度时间序列中发生气候极端异常现象的年份与出现E1Ni o事件和La Ni a事件的年份有非常好的对应关系(仅讨论公元1960—1980年).4)高阶矩分析方法对于检测气候极端异常分布及演变规律有较好的应用前景.展开更多
基金Financial support for this work is provided by the National Natural Science Foundation of China (no.51474208)the National Key Research and Development Program of China (2016YFC0600904)+1 种基金a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)The fnancial support provided by China Scholarship Council (CSC,Grant no.201606420013)
文摘In this study,the moment tensor of transversely isotropic shale was analyzed using a discrete element method-acoustic emission model(DEM-AE model).Firstly,the failure modes of the shale obtained from the acoustic emission(AE) events and physical experiments were compared.Secondly,the relationships between AE events and seismic magnitudes,and AE events and the resulting cracks were analyzed.Finally,a moment tensor T-k chart describing the seismic source was introduced to demonstrate the differences in the transversely isotropic shale.The results showed that,for different anisotropy angles,a linear logarithmic relationship existed between the cumulative AE events and the seismic magnitude in the concentration area of the AE events.A normal distribution was observed for the number of AE events as the seismic magnitude changed from small to large.The moment tensor T-k chart indicated that the number and proportion of linear tension cracks in the shale were highest.When θ = 30°,the peak seismic magnitude was at a minimum.The average seismic magnitude in the concentration area of the AE events was also relatively small.Points close to the U=-1/3V line and the number of cracks included in a single AE event were at a minimum,and the corresponding peak stress also reached its lowest level.In contrast,when θ=90°,all related parameters were contrary to the above θ = 30° case.The DEM-AE model and the moment tensor T-k chart are suitable for analyzing the distribution of shale cracks appearing during the loading process.This study can provide constructive references for future research on the fracturing treatment of shale.
文摘大型活动散场期间的地铁车站客流属于可预知的非常规客流,采用常规客流的统计预测方法难以准确预测其客流需求。基于深度学习,将历史客流规律、大型活动数据与实时自动售检票系统数据相结合,提出了一种适用于大型活动散场期间地铁车站的短时客流预测模型。首先对历史客流数据进行了拆分及降噪处理,并分析了活动客流特征。之后,基于深度学习框架构建多层结构的卷积神经网络,拟合活动客流特征与客流时空分布的映射关系,并选取Adam(adaptive moment estimation)算法优化训练过程,以适用于活动散场时客流集中进站的情况。最后,以北京地铁奥林匹克公园站为例,利用实测数据验证了模型的准确性。预测结果表明:建立的Adam-CNN(convolution neural network)模型相对于常用时间序列方法自回归滑动平均和传统神经网络SGD-CNN模型具有更高的精度,能够为大型活动的组织提供更为有力的支持。
文摘基于标准化后的高分辨率气候代用资料,应用高阶矩分析方法检测近2000年来气候极端异常演变特征;同时结合滤波方法进行具有物理背景的层次分离,进而研究了各时间层次气候极端异常变化信息及其贡献.结果表明:1)在100年以上的时间层次上,可能存在千年左右的气候变化振荡周期,而且20世纪是近2000年来气候极端异常现象最为活跃的时段,可能对应于气候极端异常现象活跃期.2)对于20—60年这一时间层次,公元300—1100年间气候极端异常现象比较明显,而公元1100—1980年间相对比较缓和;该层次对20世纪的气候异常没有显著贡献.世纪以上和20—60年时间层次均揭示出在近2000年的气候变化中,公元1100年前后可能是一个气候极端异常现象演变的关键转折时期.3)在年际尺度上(小于20年),北京石花洞石笋微层厚度时间序列中发生气候极端异常现象的年份与出现E1Ni o事件和La Ni a事件的年份有非常好的对应关系(仅讨论公元1960—1980年).4)高阶矩分析方法对于检测气候极端异常分布及演变规律有较好的应用前景.
