A model‐based data‐driven dictionary learning for seismic data representation          下载免费PDF全文

Can Evren Yarman  Rajiv Kumar  James Rickett 《Geophysical Prospecting》2018,66(1):98-123

Planar waves events recorded in a seismic array can be represented as lines in the Fourier domain. However, in the real world, seismic events usually have curvature or amplitude variability, which means that their Fourier transforms are no longer strictly linear but rather occupy conic regions of the Fourier domain that are narrow at low frequencies but broaden at high frequencies where the effect of curvature becomes more pronounced. One can consider these regions as localised “signal cones”. In this work, we consider a space–time variable signal cone to model the seismic data. The variability of the signal cone is obtained through scaling, slanting, and translation of the kernel for cone‐limited (C‐limited) functions (functions whose Fourier transform lives within a cone) or C‐Gaussian function (a multivariate function whose Fourier transform decays exponentially with respect to slowness and frequency), which constitutes our dictionary. We find a discrete number of scaling, slanting, and translation parameters from a continuum by optimally matching the data. This is a non‐linear optimisation problem, which we address by a fixed‐point method that utilises a variable projection method with ?₁ constraints on the linear parameters and bound constraints on the non‐linear parameters. We observe that slow decay and oscillatory behaviour of the kernel for C‐limited functions constitute bottlenecks for the optimisation problem, which we partially overcome by the C‐Gaussian function. We demonstrate our method through an interpolation example. We present the interpolation result using the estimated parameters obtained from the proposed method and compare it with those obtained using sparsity‐promoting curvelet decomposition, matching pursuit Fourier interpolation, and sparsity‐promoting plane‐wave decomposition methods.  相似文献   

支持向量机在GPS高程异常中的应用     

谢波  刘连旺 《测绘科学》2011,36(1):172-174

本文论述了支持向量机的回归算法,提出了基于支持向量机的GPS高程异常拟合方法,并在MATLAB中编制了相应的支持向量机程序,建立了相应的GPS高程异常模型.以实例数据讨论了基于支持向量机的GPS高程异常分析方法.研究表明:用支持向量机技术建立GPS高程异常模型是可行的和有效的.  相似文献   

基于居民点密度的人口密度空间化   总被引：5，自引：0，他引：5  

闫庆武  卞正富  张萍  王红 《地理与地理信息科学》2011,27(5):95-98

提出了一种基于居民点密度的人口密度空间化方法。以江苏省人口数据空间化为例,在采用核密度估计法得到江苏省镇(乡、街道)居民点密度后,运用基于居民点密度的人口密度空间化计算公式获得江苏省1 000m×1 000m的人口密度格网图,结果表明:江苏省人口分布的热点地区正是在建的三大都市圈(苏锡常都市圈、南京都市圈和徐州都市圈),其中苏锡常都市圈的人口分布呈现各向异性,而南京和徐州都市圈的人口分布则表现为各向同性;人口密度不仅取决于居民点密度,还与平均每个居民点的人口数密切相关,居民点密度大的区域的人口密度不一定大,居民点密度小的区域的人口密度也不一定小。  相似文献   

基于GIS和洛伦兹曲线的辽宁省海岛空间分布特征探析          下载免费PDF全文

赵东洋  雷利元  尤广然  刘明  席小慧  毕远溥 《海洋学研究》2017,35(1):73-79

为合理开发利用和保护辽宁海岛资源,优化海岛产业结构和布局,以辽宁纳入中国海域海岛标准名录的633个海岛为研究对象,在对其地理坐标、陆域面积等基础数据进行数理统计的基础上,通过GIS的空间分析和绘制洛伦兹曲线、计算基尼系数,对辽宁海岛的空间分布特征进行了分析与研究。结果表明:辽宁海岛属集聚型分布类型,在空间上呈组团与带状分布,按聚集程度来看,形成了黄海北部长山群岛集聚区、辽东半岛南部浅海集聚带与辽东湾西部浅海集聚带三个区域;约60%的海岛处在离大陆岸线直线距离在10 km以内的范围内,近陆程度高;海岛陆域面积分布不均衡,基尼系数达0.9。  相似文献   

Multi-scale support vector algorithms for hot spot detection and modelling   总被引：2，自引：2，他引：0  

Alexei Pozdnoukhov  Mikhail Kanevski 《Stochastic Environmental Research and Risk Assessment (SERRA)》2008,22(5):647-660

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs¹³⁷ activity given the measurements taken in the region of Briansk following the Chernobyl accident.  相似文献   

线性向量半参数模型与标量半参数模型最小二乘估计的比较     

张松林  张昆  王新洲 《大地测量与地球动力学》2006,26(4):10-13

研究了最小二乘准则下线性向量半参数模型的核估计、近邻估计及样条估计3种估计方法,分析了向量半参数模型与标量半参数模型在这几种估计方法中的差异。  相似文献   

随深度指数变化的视电阻率解     

梁子斌  钱家栋 《高原地震》1998,(2)

从横向均匀介质满足的基本方程出发,得到视电阻率核函数的一阶非线性微分方程,通过方程求解,并利用滤波系数法容易得到电阻率随深度任意变化的视电阻率问题。当各层介质电性结构随深度呈指数变化时,还可得到各层之间核函数的递推关系,这对实际介质的正反演问题都有重要意义和应用价值。  相似文献   

基于偏最小二乘回归的冬季北太平洋风暴轴指数的特征诊断     

闫恒乾  洪梅  张韧  朱伟军  马晨晨  余丹丹 《气象科学》2018,38(5):596-605

针对现有风暴轴指数分析大多采用相关分析等较为简单方法,难以对风暴轴指数变化有效诊断分析的问题,引入偏最小二乘回归(Partial Least Square Regression,PLS)的线性方法和核偏最小二乘回归方法(Kernel Partial Least Square Regression,KPLS),对冬季北太平洋风暴轴指数变化进行了特征诊断研究,并与传统的线性无偏最小二乘回归结果进行了试验比对。结果表明:偏最小二乘回归方法的诊断结果能够更好地反映风暴轴内部变化规律,并有效降低诊断误差。对于PNYI(北太平洋风暴轴纬度指数),采用r0. 2的因子筛选方案(r为因子与风暴轴指数的相关系数)并应用KPLS算法时,预测效果最佳;对于PNXI(北太平洋风暴轴经度指数)和PNII(北太平洋风暴轴强度指数),采用全因子方案并应用KPLS算法时,预测效果最佳。  相似文献   

基于核空间的多光谱遥感图像分类方法   总被引：5，自引：1，他引：5  

刘伟强  胡静  夏德深 《国土资源遥感》2002,13(3):44-47,57

提出了多光谱遥感图像分类方法中解决非线性问题的一种思路。通过引入核空间理论,将在输入空间中不能线性分类的问题映射到一个可以进行线性分类的高维空间,并利用核函数避免了在高维空间中运算的复杂度,较好地解决了非线性分类问题。利用这种思路,本文对一种比较简单的分类算法———自适应最小距离分类方法加以改进,并将其应用于多光谱遥感图像的分类中,提出了一种核函数的选择策略。  相似文献   

中国大宗商品期货交割库空间布局及影响因素   总被引：1，自引：1，他引：0  

陈超  马春光 《地理科学》2017,37(1):125-129

运用核密度估计、空间自相关分析、地理探测器等方法,分析了中国大宗商品期货交割库的空间分布与聚类特征及其影响因素,结果表明：大宗商品期货交割库数量呈现不均匀分布态势,主要分布于东南沿海地区;大宗商品期货交割库核密度空间分布存在显著的差异,核密度高值分布区主要集中在东部沿海地区,而西北部地区则呈现极低密度状态;大宗商品期货交割库存在明显的空间集聚分布特征,主要在安徽、浙江、福建一带呈明显的集聚效应;大宗商品期货交割库空间分异的主要影响因素为区域综合运输能力和进出口总额。  相似文献