Multiscale imaging of complex structures from multifold wide-aperture seismic data by frequency-domain full-waveform tomography: application to a thrust belt   总被引：4，自引：0，他引：4  

C. Ravaut  S. Operto  L. Improta  J. Virieux  A. Herrero  P. Dell'Aversana 《Geophysical Journal International》2004,159(3):1032-1056

  相似文献   

青西油田碳酸盐岩裂缝性储层地球物理预测技术的应用     

师永民撒利明  陈广城田鑫李虹  王斌婷 《应用地球物理》2005,2(2):119-126

白云岩成岩收缩晶间孔、洞、缝与构造网状缝相互沟通可以组成良好的油气储层，但是这种复杂裂缝—孔隙型储层分布随机性强、发育程度和差异性大，储层预测难度大。本文以中国西部酒泉盆地青西油田下白垩统下沟组湖相白云岩裂缝—孔隙型储层为例，提出地球物理综合预测碳酸盐岩裂缝的方法。本文描述了综合地球物理方法预测碳酸盐岩裂缝储层的实施和应用效果。  相似文献   

Shallow seismic trapping structure in the San Jacinto fault zone near Anza, California   总被引：3，自引：0，他引：3  

M. A. Lewis  Z. Peng †  Y. Ben-Zion  F. L. Vernon 《Geophysical Journal International》2005,162(3):867-881

  相似文献   

High-resolution imaging of the Bear Valley section of the San Andreas fault at seismogenic depths with fault-zone head waves and relocated seismicity   总被引：1，自引：0，他引：1  

Jeff McGuire  Yehuda Ben-Zion 《Geophysical Journal International》2005,163(1):152-164

  相似文献   

Exploration of remotely sensed forest structure and ultrasonic range sensor metrics to improve empirical snow models          下载免费PDF全文

Andrés Varhola  Nicholas C. Coops  Younes Alila  Markus Weiler 《水文研究》2014,28(15):4433-4448

Current methods to estimate snow accumulation and ablation at the plot and watershed levels can be improved as new technologies offer alternative approaches to more accurately monitor snow dynamics and their drivers. Here we conduct a meta‐analysis of snow and vegetation data collected in British Columbia to explore the relationships between a wide range of forest structure variables – obtained from Light Detection and Ranging (LiDAR), hemispherical photography (HP) and Landsat Thematic Mapper – and several indicators of snow accumulation and ablation estimated from manual snow surveys and ultrasonic range sensors. By merging and standardizing all the ground plot information available in the study area, we demonstrate how LiDAR‐derived forest cover above 0.5 m was the variable explaining the highest percentage of absolute peak snow water equivalent (SWE) (33%), while HP‐derived leaf area index and gap fraction (45° angle of view) were the best potential predictors of snow ablation rate (explaining 57% of variance). This study reveals how continuous SWE data from ultrasonic sensors are fundamental to obtain statistically significant relationships between snow indicators and structural metrics by increasing mean r² by 20% when compared to manual surveys. The relationships between vegetation and spectral indices from Landsat and snow indicators, not explored before, were almost as high as those shown by LiDAR or HP and thus point towards a new line of research with important practical implications. While the use of different data sources from two snow seasons prevented us from developing models with predictive capacity, a large sample size helped to identify outliers that weakened the relationships and suggest improvements for future research. A concise overview of the limitations of this and previous studies is provided along with propositions to consistently improve experimental designs to take advantage of remote sensing technologies, and better represent spatial and temporal variations of snow. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

机载LiDAR地形特征信息快速提取     

刘智敏  杨安秀  宿殿鹏  刘盼 《大地测量与地球动力学》2018,38(11):1186-1190

为更有效地获取地形特征信息，提出一种机载LiDAR地形特征信息快速提取算法。首先，通过构建二次曲面拟合模型，建立实测LiDAR地形数据与拟合曲面的几何规则；然后，采用LM算法迭代参数寻优，获得最优化结果下的地形拟合参数，计算拟合时间及拟合精度；最后，以地形拟合模型为基础，进行地形特征信息的快速提取。通过机载LiDAR实测数据验证，当最优搜索半径为2 m时，地形曲面的拟合时间仅为0.02 s，RMSE仅为5.09 cm。该算法保证了地形特征信息提取效率和精度，能够有效满足机载LiDAR科学研究和工程应用的技术需求。  相似文献   

eLoran系统新型信号波形设计及其性能评估          下载免费PDF全文

李婉清  刘中伟  李实锋  叶清琳 《全球定位系统》2020,45(2):13-20

增强型罗兰导航系统(eLoran)作为全球卫星导航系统(GNSS)的备份系统,是国家定位导航授时(PNT)安全的重要基础设施.针对目前标准eLoran信号存在易受交叉干扰、天波干扰,通信数据传输速率低等的问题,本文基于标准罗兰信号体制提出了两种波形改进方法(衰减函数法与对称波形法)并对新型波形进行性能评估.实验结果表明,两种方法能够有效缩短波形持续时间,加速后沿波形下降,减小发射机功耗.对称波形法能够大幅减小波形持续时间,但缩短波形持续时间也将改变原信号波形的频谱特性,利用衰减函数法可以最大程度保证信号的频谱性能.综合分析可知,新型波形能够有效利用时域资源,空余的时间可用于增加传输信号,进而提高数据调制技术的性能.  相似文献   

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures          下载免费PDF全文

J. L. McCreight  A. G. Slater  H. P. Marshall  B. Rajagopalan 《水文研究》2014,28(3):933-957

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

基于深度残差网络的机载LiDAR点云分类     

赵传  郭海涛  卢俊  余东行  张保明 《测绘学报》2020,49(2):202-213

机载LiDAR点云的分类是利用其进行城市场景三维重建的关键步骤之一。为充分利用现有的图像领域性能较好的深度学习网络模型,提高点云分类精度,并降低训练时间和对训练样本数量的要求,本文提出一种基于深度残差网络的机载LiDAR点云分类方法。首先提取归一化高程、表面变化率、强度和归一化植被指数4种具有较高区分度的点云低层次特征;然后通过设置不同的邻域大小和视角,利用所提出的点云特征图生成策略,得到多尺度和多视角点云特征图;再将点云特征图输入到预训练的深度残差网络,提取多尺度和多视角深层次特征;最后构建并训练神经网络分类器,利用训练的模型对待分类点云进行预测,经后处理得到分类结果。利用ISPRS三维语义标记竞赛的公开标准数据集进行试验,结果表明,本文方法可有效区分建筑物、地面、车辆等8类地物,分类结果的总体精度为87.1%,可为城市场景三维重建提供可靠的信息。  相似文献   

无人机机载LiDAR在沿海滩涂大比例尺地形测绘中的应用     

郭春海  张英明  丁忠明 《测绘通报》2019,(9):155-158

以南京市以下长江12.5 m深水航道建设工程中的白茆沙整治建筑物工程的年度监测为例，介绍了利用无人机航摄和机载LiDAR技术进行大比例尺高精度的滩涂地理信息数据采集的关键技术。研究案例表明，采用无人机搭载数码相机和LiDAR联合进行数据采集，在小区域、通行困难地区的高分辨率影像和地面高程数据快速获取方面具有明显优势，为沿海滩涂开发中的工程用图测绘工作提供了一种新的有效快捷的技术手段。  相似文献