基于PSR-WSVM模型的边坡位移预测     

李建新  刘小生  肖钢  周文  刘仁志 《大地测量与地球动力学》2020,40(6):577-580

为建立高精度的边坡位移预测模型，采用相空间重构（PSR）将边坡位移时间序列数据转换为多维数据，同时构造小波核函数改进的支持向量机模型，建立PSR-WSVM模型并应用于边坡位移预测。将PSR-WSVM模型预测结果与传统支持向量机（SVM）模型、小波支持向量机（WSVM）模型和基于相空间重构的支持向量机（PSR-SVM）模型预测结果进行对比，通过平均绝对误差（MAE）、平均绝对误差百分比（MAPE）和均方根误差（RMSE）3个精度评价指标验证PSR-WSVM模型的可行性。工程实例结果表明，PSR-WSVM模型预测结果的3个精度评价指标都优于另外3种模型，边坡位移预测的精度明显提升。  相似文献   

黔北正安地区五峰组—龙马溪组页岩气地质特征及有利区预测     

贺永忠  向坤鹏  安亚运  易成兴  杨忠琴  于宁 《中国地质调查》2020,7(3):21-29

为明确黔北正安地区上奥陶统五峰组至下志留统龙马溪组的页岩气地质特征,充分应用页岩气地质调查、重磁电、二维地震、地质调查井及测试分析等工作方法,开展黔北正安地区1:5万页岩气基础地质调查,对五峰组至龙马溪组富有机质页岩的分布、沉积环境、有机地球化学、岩石矿物、储集性能及含气性等特征进行分析研究,结果表明: 研究区五峰组—龙马溪组富有机质页岩为深水陆棚相沉积,主要分布有安场向斜、斑竹向斜和泥高向斜,厚度14.5~55 m,埋深0~3 200 m; 有机碳(TOC)含量1.0%~4.0%,有机质镜质体反射率(R_o)为1.82%~2.23%,有机质类型以Ⅰ型干酪根为主; 岩石主要由石英、长石和黏土矿物组成,脆性矿物含量高,一般大于50%; 孔隙度为2.03%~3.89%,渗透率为0.35×10^-5~1.86×10^-5μm²,表现为低孔、特低渗的特征; 最高含气量为2.88 m³/t,显示出较好的含气性特征。综合分析和评价圈出3个页岩气聚集有利区,分别为安场区块、斑竹区块和泥高区块,这为研究区页岩气进一步勘探开发指明了方向。  相似文献   

Prediction of the South China Sea Dipole Using SSA-MEM     

Qiong Xia  Enbo Wei 《大气与海洋》2018,56(4):240-253

ABSTRACT

A dipole structure appears in the sea surface height off the central coast of Vietnam during boreal summer in the South China Sea. This dipole, which possesses a chlorophyll signature associated with higher phytoplankton concentrations arising from nutrient upwelling, is important for the productivity of local fisheries. Multi-satellite sea level anomalies are used to investigate the life cycle of the dipole structure. By applying empirical orthogonal function (EOF) analysis, the third EOF mode (EOF 3) is found to represent the major variations of the dipole structure. By removing the temporal noise of EOF 3, a South China Sea dipole index is defined. This index captures the life cycle of the dipole including its generation, mature strength, and final termination. Both one-dimensional and two-dimensional forecasts are generated using a statistical forecasting method that combines singular-spectrum analysis and the maximum entropy method. The appearance of the dipole structure can be predicted with an accuracy of 78% at one-month lead times and an accuracy of 61% at one-year lead times.  相似文献   

一种基于熵权法的小波去噪复合评价指标   总被引：2，自引：0，他引：2  

容静  刘立龙  康昊华  李松青  周吕 《大地测量与地球动力学》2018,38(7):689-694

传统的评价指标在真值未知的情况下不能满足小波去噪质量评价的要求。为此，借助变化率特征重新构建均方根误差变化量和平滑度变化量两个指标，利用熵权法定权将归一化后的两个指标线性组合，所得到的新指标即为复合评价指标。该方法借助指标的变化率随分解层数的增加表现出明显的收敛特性来确定去噪最优分解层数。实验表明，该方法能够在真值未知的情况下准确地指导小波分解，确定去噪最优分解层数，从而达到最优去噪效果。  相似文献   

室内用户语义位置预测研究     

王培晓  王海波  傅梦颖  吴升 《地球信息科学学报》2018,20(12):1689-1698

位置预测技术可以提前预知用户下一时刻的位置,在基于位置的服务（Location-based Service,LBS）领域中发挥着极其重要的作用。现有的位置预测技术大多仅使用用户的地理轨迹,仅使用地理轨迹挖掘出来的用户移动模式易受地理特性的限制缺乏深层次的语义信息。本文基于某商场群体用户的室内轨迹数据和语义信息预测用户下一个时刻语义位置。语义位置预测包括停留区域识别、停留区域语义匹配、语义位置建模。在停留区域识别阶段,为减少室内停留时间不固定对停留区域识别的影响,本研究提出了一种新型的时空凝聚层次聚类算法（Spatial-Temporal Agglomerative Nesting, ST-AGNES),该算法具有思想简单、超参数少、自动生成聚类个数等优点。在语义匹配阶段,引入了吸引度规则,充分利用停留区域所有轨迹点与室内高密度的商铺名称信息做匹配。最后,采用长短型记忆神经网络模型（Long Short-Term Memory,LSTM）挖掘群体用户的语义位置模式并预测用户未来的语义位置,实验预测正确率达到61.3%。  相似文献   

Seismic electric signals in seismic prone areas          下载免费PDF全文

Nicholas V.Sarlis  Panayiotis A.Varotsos  Efthimios S.Skordas  Seiya Uyeda  Jacques Zlotnicki  Toshiyasu Nagao  Anatoly Rybin  Mary S.Lazaridou-Varotsos  Konstantina A.Papadopoulou 《地震科学（英文版）》2018,31(1):44-51

The Varotsos-Alexopoulos-Nomicos(VAN) method of short-term earthquake prediction was introduced in the 1980s. The VAN method enables estimation of the epicenter, magnitude and occurrence time of an impending earthquake by observing transient changes of the electric field of the Earth termed seismic electric signals(SES). Here, we present a few examples of SES observed in various earthquake prone areas worldwide.  相似文献   

Piecewise prediction model for watershed‐scale erosion and sediment yield of individual rainfall events on the Loess Plateau,China          下载免费PDF全文

Yu Guo‐Qiang  Zhang Mao‐Sheng  Li Zhan‐Bin  Li Peng  Zhang Xia  Cheng Sheng‐Dong 《水文研究》2014,28(21):5322-5336

Establishing a universal watershed‐scale erosion and sediment yield prediction model represents a frontier field in erosion and soil/water conservation. The research presented here was conducted on the Chabagou watershed, which is located in the first sub‐region of the hill‐gully area of the Loess Plateau, China. A back‐propagation artificial neural model for watershed‐scale erosion and sediment yield was established, with the accuracy of the model, then compared with that of multiple linear regression. The sensitivity degree of various factors to erosion and sediment yield was quantitatively analysed using the default factor test. On the basis of the sensitive factors and the fractal information dimension, the piecewise prediction model for erosion and sediment yield of individual rainfall events was established and further verified. The results revealed the back‐propagation artificial neural network model to perform better than the multiple linear regression model in terms of predicting the erosion modulus, with the former able to effectively characterize dynamic changes in sediment yield under comprehensive factor conditions. The sensitivity of runoff erosion power and runoff depth to the erosion and sediment yield associated with individual rainfall events was found to be related to the complexity of surface topography. The characteristics of such a hydrological response are thus closely related to topography. When the fractal information dimension is greater than the topographic threshold, the accuracy of prediction using runoff erosion power is higher than that of using runoff depth. In contrast, when the fractal information dimension is smaller than the topographic threshold, the accuracy of prediction using runoff depth is higher than that of using runoff erosion power. The developed piecewise prediction model for watershed‐scale erosion and sediment yield of individual rainfall events, which introduces runoff erosion power and runoff depth using the fractal information dimension as a boundary, can be considered feasible and reliable and has a high prediction accuracy. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

山东大尹格庄金矿床深部三维定量成矿预测     

陈进  毛先成  邓浩 《地球学报》2020,41(2):179-191

大尹格庄金矿是胶西北招平断裂带中段的大型金矿床,但随着逐渐的开采其保有资源量不断下降,急需在矿床的深边部区域开展接续资源找矿工作。本文以大尹格庄金矿为研究对象,以区域成矿理论和找矿勘查模型为指导,利用三维地质建模技术构建了相关地质体的三维模型,在此基础上,采用定量化的方法提取地质体与成矿相关的控矿因素指标和矿化指标,并建立了三维定量成矿预测模型,并应用该模型圈定了3个找矿靶区。研究表明,该方法能够适用于大中型矿山的深边部找矿工作,其三维及定量化的预测结果能够为后续的勘探及开采工作提供重要参考。  相似文献   

级配碎石填料大三轴试验及累积塑性应变预测模型     

杨志浩  岳祖润  冯怀平  叶朝良  马德良 《岩土力学》2020,41(9):2993-3002

级配碎石作为重载铁路基床表层的主要填料，其受列车荷载的影响最大。因此，研究级配碎石在循环荷载作用下的动力行为及累积塑性应变演化特征变得尤为重要。首先，通过制备不同细粒含量的级配碎石填料，开展一系列大型动三轴试验，探究细粒含量、围压及动应力幅值对循环荷载作用下试样累积塑性应变的耦合影响机制。其次，基于塑性安定理论，确定不同应力水平下试样的动力行为，得到考虑围压及细粒含量参数的塑性蠕变状态临界动应力计算模型。最后，结合试验数据，建立考虑应力水平及细粒含量参数的塑性蠕变动力行为累积塑性应变预测模型，并明确各参数的物理意义。其研究成果可为既有重载铁路路基健康状态评估及考虑强度、变形综合控制的路基结构设计提供参考。  相似文献   

一种基于GA-BP-MC神经网络的高铁桥墩沉降预测模型     

冯绍权  花向红  陶武勇  宣伟  吴伟  续东 《测绘通报》2019,(7):50-53,82

提出一种基于马尔科夫链修正的遗传BP神经网络预测模型（GA-BP-MC），利用遗传算法的全局寻优能力初始化BP神经网络权值和阈值，初步建立GA-BP神经网络预测模型，结合马尔科夫链的无后效性修正模型预测值，形成高精度GA-BP-MC神经网络变形预测模型。结合高铁桥墩沉降数据，分别与BP神经网络、GA-BP神经网络预测模型进行对比，结果表明，该预测模型精度最高。  相似文献