Theoretical link between rainfall and flood magnitude          下载免费PDF全文

Robert E. Criss 《水文研究》2018,32(11):1607-1615

The rainfall–run‐off convolution integral is analytically solved for several models for the elementary hydrograph. These solutions can be combined with available rainfall frequency analyses to predict flood flows along streams for different recurrence intervals, using no free parameters for gauged streams and one estimable parameter for ungauged streams. Extreme discharge magnitudes at gauged sites can be typically estimated within a factor of two of actual records, using no historical data on extreme flows. The flow predictions reproduce several important characteristics of the flood phenomenon, such as the slope of the regression line between observed extreme flows and basin area on the conventional logQ versus logA plot. Importantly, for the models and data sets investigated, the storm duration of greatest significance to flooding was found to approximate the intrinsic transport timescale of the particular watershed, which increases with basin size. Thus, storms that deliver extraordinary amounts of rainfall over a particular time interval will most greatly activate basins whose time constants approximately equal that interval. This theoretical finding is supported by examination of the regional hydrological response to the massive storms of September 14, 2008, and April 28–30, 2017, which caused extraordinary record flooding of basins of about 5–100 km² and 500–4,000 km², respectively, but produced few records in basins that were larger or smaller than those ranges.  相似文献   

基于卷积神经网络的恒星光谱型和光度型的分类模型          下载免费PDF全文

洪舒欣  邹志强  徐灵哲 《天文学报》2021,62(5):48

恒星光谱分类是天文学中一个重要的研究问题.对于已经采集到的海量高维恒星光谱数据的分类,采用模式匹配方法对光谱型分类较为成功,但其缺点在于标准恒星模版之间的差异性在匹配实际观测数据中不能体现出来,尤其是当需要进行光谱型和光度型的二元分类时模版匹配法往往会失败.而采用谱线特征测量的光度型分类强烈地依赖谱线拟合的准确性.为了解决二元分类的问题,介绍了一种基于卷积神经网络的恒星光谱型和光度型分类模型(Classification model of Stellar Spectral type and Luminosity type based on Convolution Neural Network, CSSL CNN).这一模型使用卷积神经网络来提取光谱的特征,通过注意力模块学习到了重要的光谱特征,借助池化操作降低了光谱的维度并压缩了模型参数的数量,使用全连接层来学习特征并对恒星光谱进行分类.实验中使用了大天区面积多目标光纤光谱天文望远镜(Large Sky Area Multi-Object Fiber Spectroscopy Telescope, LAMOST)公开数据集Data Release 5 (DR5,用了其中71282条恒星光谱数据,每条光谱包含了3000多维的特征)对该模型的性能进行验证与评估.实验结果表明,基于卷积神经网络的模型在恒星的光谱型分类上准确率达到92.04%,而基于深度神经网络的模型(Celestial bodies Spectral Classification Model, CSC Model)只有87.54%的准确率; CSSL CNN在恒星的光谱型和光度型二元分类上准确率达到83.91%,而模式匹配方法MKCLASS仅有38.38%的准确率且效率较低.  相似文献   

基于Matlab的三次卷积法在数字地形数据内插中的应用     

李水兵  李培现 《测绘信息与工程》2011,36(3):36-38

利用三次卷积法对数字地形数据进行内插,获得了足够的三维地形数据,解决了由于格网地形数据有限,必须采用内插的方法以获得足够的三维数据问题,实现了三维地形数据内插和三维模拟,取得了较好的效果,为复杂地形的三维地形建模提供了一种新的方法。  相似文献   

改进全卷积网络方法的高分二号影像农村道路提取     

李朝奎  曾强国  方军  吴馁  武凯华 《遥感学报》2021,25(9):1978-1988

针对目前利用高分遥感数据提取农村道路的研究与应用少,提取结果精准度不够的问题,提出了结合空洞卷积和ASPP（Atrous Spatial Pyramid Pooling）结构的改进全卷积农村道路提取网络模型DC-Net（Dilated Convolution Network）。该模型基于全卷积的编解码结构来提取道路深度特征信息,同时针对农村道路细长的特点,在解编码层之间加入了以空洞卷积为基础的ASPP（Atrous Spatial Pyramid Pooling）结构来提取道路的多尺度特征信息,在不牺牲特征空间分辨率的同时扩大了特征感受野FOV（Field-of-View）,从而提高细窄农村道路的识别率。以长株潭城市群郊区部分区域为试验对象,以高分二号国产卫星遥感影像为实验数据,将本文提出的方法与经典的几种全卷积网络方法进行实验结果对比分析。实验结果表明：（1）本文所提出的道路提取模型DC-Net在农村道路的提取上具有可行性,整体提取平均精度达到98.72%,具有较高的提取精度;（2）对比几种经典的全卷积网络模型在农村道路提取上的效果,DC-Net在农村道路提取的精度和连结性、以及树木和阴影的遮挡方面,均表现出了较好的提取结果;（3）本文提出的改进全卷积网络道路提取模型能够有效地提取高分辨率遥感影像中农村道路的特征信息,总体提取效果较好,为提高基于国产高分影像的农村道路提取精度提供了一种新的思路和方法。  相似文献   

深度域地震资料解释探讨     

郝晓红  晏玉环 《海洋地质译丛》2014,(2):20-24

叠前深度偏移在速度复杂或构造复杂地区的成像具有优势,目前已经成为地震数据处理领域的标准流程.对叠前偏移处理数据进行后续的解释主要有两种思路：时深转换到时间域进行解释、直接在深度域进行解释.在深度域进行解释有很多优点,但也存在一些难点：褶积模型的适用性、井震标定的适用性以及深度校正问题.在前人研究和工作实践的基础上,提出了一种在深度域进行解释的方法,尽量适应目前解释系统、工作习惯、工作思路的方法.  相似文献   

A linear systems approach to watershed transport simulation          下载免费PDF全文

James N. Carleton 《水文研究》2015,29(6):1198-1212

Models that simulate loadings of pollutants from agricultural landscapes to surface waters often operate at time scales that are relatively coarse (e.g. daily) compared with how fast water moves in streams, suggesting a commensurate physical scale that is substantially larger than typical agricultural fields. In general, as pollutants enter water and move downstream, longitudinal dispersive effects and travel time de‐synchronization tend to cause flattening and broadening of concentration peaks—an effect with implications for potential impacts on ecological and human health, and for which adequate representation is thus important for risk assessment. In‐stream transport is often approximated in practice using numerical implementation of the one‐dimensional advection–dispersion equation (ADE), with streams discretized into linked homogeneous segments. However, when a daily time step is employed, limitations inherent in the finite difference methodology may constrain simulated dispersion in lotic waters to unrepresentative or unrealistic magnitudes. In this paper, a convolution‐based approach to surface water transport is suggested as an alternative to the ADE, for use in combination with daily input loading models. This approach offers the advantage of greater flexibility in representing longitudinal mixing by using impulse response functions (IRF) to represent inter‐segment transport. Networks of stream segments are represented using nested convolutions, implemented using forward and inverse discrete Fourier transform to simplify calculations. Enhanced representational flexibility arises from the freedom afforded the modeller in selecting each segment's IRF, which may be chosen to represent dispersive regimes ranging from pure advection (plug flow) to compete mixing, and beyond to the sort of long‐tailed mixing characterized by fractal inverse frequency power‐law scaling. The approach is explored in proof‐of‐concept exercises that make use of atrazine monitoring data sets collected over common time periods from upstream and downstream locations within the same watersheds. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.  相似文献   

山西潞安环能某矿三维地震数据二次处理技术应用     

杜宏 《中国煤炭地质》2012,(5):55-59

基于2007年某煤矿三维地震提交的地质报告中存在的疑问,对该采区开展了二次处理技术研究,在分析原处理流程中发现原资料消除线形干扰不够干净;另外复杂的地震地质条件,导致静校正问题突出。针对干扰问题,在保真的基础上通过T～X域相干分析,提取相干噪音,达到剔除线形干扰的目;针对静校正问题采用了层析静校正及迭代剩余静校正技术,以解决长波长静校正问题;另外为提高地震数据处理精度和地震成像质量,避免出现假破碎带、伪构造等情况,在二次处理中还采用了地表一致性反褶积加零相位反褶积组合技术、高精度速度分析及动校正技术、叠前时间偏移技术等,有效消除了原地震报告中的错误解释。  相似文献   

基于多尺度区域卷积神经网络的露头孔洞自动提取     

王庆  曾齐红  张友焱  邵燕林  魏薇  邓帆 《现代地质》2021,35(4):1147-1154

碳酸盐岩储层解释与评价的一个重要内容是确定其孔隙空间特征,野外露头是地下储层的真实刻画,对野外露头进行孔洞的自动化提取与其参数定量表征具有重要意义。在深度学习Mask-RCNN模型基础上进行改进,提出一种基于多尺度的区域卷积神经网络孔洞检测新方法,并通过两种方式进行准确度分析,第一种是将该方法的孔洞提取结果与OSTU分割法、分水岭分割法、BP神经网络法、支持向量机法以及Mask-RCNN的孔洞提取结果进行比较,其结果显示该方法有更高的检测准确度;第二种是通过该方法提取的孔洞结果计算洞数量、面孔率和洞面积均值三个孔洞特征参数,以人工提取结果为参照,比较得到洞数量准确度在88%以上,面孔率准确度在93%以上,洞面积均值准确度在93%以上。最后将提出的方法应用于峨边先锋灯二段的数字露头剖面孔洞自动识别,并分层计算孔洞参数,定量分析其分布特征,为该露头碳酸盐岩储层评价提供了依据。  相似文献   

Reply to comment by M. Hagerty and S. Sherburn on 'External modulation of volcanic activity'     

J. Neuberg 《Geophysical Journal International》2002,148(3):695-696

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基于二维三次卷积插值算法的波前构建射线追踪   总被引：1，自引：0，他引：1  

韩复兴  孙建国  杨昊 《吉林大学学报(地球科学版)》2008,38(2):336-341

针对波前构建法在实现波前射线路径追踪过程中所遇到的速度以及速度导数的插值问题,提出一种更精确的二维三次卷积插值算法,将该算法应用到波前构建法中,得到比较精确的射线路径。实例证明,所采用的插值算法在应用到波前构建的过程中,在求解非网格节点的速度及导数时与双三次多项式插值算法相比能够更加快速准确地获得射线路径。  相似文献