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   

Wolf‐triggered trophic cascades and stream channel dynamics in Olympic National Park: a comment on East et al. (2017)          下载免费PDF全文

Robert L. Beschta  William J. Ripple 《地球表面变化过程与地形》2018,43(4):930-935

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地方政府土地出让干预对区域工业碳排放影响的对比分析——以中国8大经济区为例     

王博  吴天航  冯淑怡 《地理科学进展》2020,39(9):1436-1446

探讨不同区域地方政府干预对碳排放的影响差异,对于中国推进碳减排战略、协调区域经济社会发展具有重要意义。鉴于此,论文基于地方政府土地出让的视阈,以中国8大经济区为研究对象,有机耦合STIRPAT模型与CKC模型,构建形成STIRPAT拓展模型,并使用2007—2016年中国28个省(市、自治区)的工业面板数据,对比考察8大经济区政府土地出让干预对区域碳排放的影响差异。结果表明:不同经济区的地方政府土地出让干预对区域碳排放的影响存在显著差异。其中,同为CKC“倒N”型的北部沿海、南部沿海、长江中游经济区,其政府土地出让干预对区域碳排放的影响呈现出北部沿海经济区为负、南部沿海经济区为正、长江中游经济区无显著影响的差异效果;同为CKC“倒U”型的东北、西南和西北经济区,其政府土地出让干预对区域碳排放的影响,东北和西南经济区都显著为负,西北经济区未表现出显著影响;同为CKC“U”型的东部沿海和黄河中游经济区,其政府土地出让干预对碳排放也呈现出前者为负、后者为正的相反影响。研究结果可为制定碳减排差别化政策、协调区域可持续发展提供参考。  相似文献   

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing     

Peng Yue  Fan Gao  Boyi Shangguan  Zheren Yan 《International journal of geographical information science》2020,34(11):2243-2274

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.  相似文献   

无定河下游全新世古洪水研究          下载免费PDF全文

李晓刚  黄春长  庞奖励 《干旱区地理》2020,43(2):380-387

对无定河流域野外考察，在其下游苏家圪坨（SJGT）地点发现夹有古洪水滞流沉积物（SWD）的全新世剖面。古洪水SWD厚度30 cm，具有平行微薄层理特征，且直接覆盖在东汉文化层之上。室内分析结果表明古洪水SWD有着与2012年洪水SWD相似的沉积学特征，以粗粉沙为主，分选良好，磁化率和烧失量较小，说明它记录了一次无定河下游的大洪水事件。文化层光释光（OSL）测年结果显示该次古洪水事件发生在1 900~1 700 a BP。古洪水洪峰水位和洪峰流量分别为765.9 m和10 530 m³·s^-1。同时利用2012年洪水洪痕水位验证，在同一断面用相同参数恢复洪峰流量为1 030 m³·s^-1，实测洪峰流量为1 000 m³·s^-1，误差为3.09%，说明古洪水洪峰流量恢复结果合理可靠。研究结果延长了无定河下游洪水数据序列，对水资源、水能源的开发利用提供了科学依据。  相似文献   

对欧亚非大陆自由主义民主格局演变的批判性分析     

麻洪川  郗笃刚  刘建忠  张晶  丁梓越 《世界地理研究》2020,29(6):1113-1124

自由主义民主作为西方谋求世界霸权而肆意输出的意识形态，以批判性话语剖析欧亚非大陆自由主义民主状况，对揭露西方民主输出的真实目的意义重大。探究欧亚非大陆“自由”等级格局演变特征，搭建“自由”等级格局演变影响因子体系，并揭示其演变影响机制。研究发现：①“自由”国家数量总体显著增长，从欧洲南部、西部、北部，亚洲南部、东北部扩散至欧洲东部、中部和非洲南部地区；“部分自由”国家数量总体明显增加，基本分布在亚洲南部、东南部，非洲东部、西部和南部；“不自由”国家数量总体略有减少，从欧洲东部、亚洲和非洲大部分地区压缩至亚非大部分地区。②“自由”等级标准差指数表现出明显上升趋势，而其变异系数表现出波动下降趋势。③总体上，欧亚非大陆具有相似“自由”等级的国家集聚程度在不断增强，其中，高值集聚出现波动增强；局部上，冷点、次冷点区域呈现“联成一片”的态势，热点、次热点区域则有“东进”的趋势。④社会经济、外部扩散、文化宗教、政治制度、西方行为等因素影响显著，且在整体和局部影响机制上呈现明显差异。⑤“自由之家”为突出和强调西方在国际政治领域的标准制定权和话语权，其“自由”等级指数具有极强的意识形态属性和战略意图。  相似文献   

A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes     

Yibin Ren  Huanfa Chen  Tao Cheng  Yang Zhang  Ge Chen 《International journal of geographical information science》2020,34(4):802-823

ABSTRACT

The spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the ST-ResNet to form a hybrid integrated-DL model to predict the volumes of citywide spatio-temporal flows (called HIDLST). The new model can dynamically learn the temporal dependency among flows via the feedback connection in the LSTM to improve accurate captures of spatio-temporal features in the flows. We test the HIDLST model by predicting the volumes of citywide taxi flows in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. A comparative study shows that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models on prediction accuracy. Furthermore, we discuss the distribution of prediction errors and the contributions of the different spatio-temporal patterns.  相似文献   

Detecting regional dominant movement patterns in trajectory data with a convolutional neural network     

Can Yang  Győző Gidófalvi 《International journal of geographical information science》2020,34(5):996-1021

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基于区域监测站的BDS定轨策略分析     

王乐  黄观文  张勤  燕兴元  秦志伟  王利  崔博斌 《大地测量与地球动力学》2018,38(5):497-503

围绕影响轨道精度和实时性的5个要素（模糊度分类固定、测站数量、定轨弧长、太阳光压模型和多系统组合）展开研究，得出区域测站分布下的定轨优选策略。实验表明，选取中国区域27个均匀分布的地面区域监测站，利用72 h弧长观测数据，采用ECOM 5参数简化太阳光压摄动模型、BDS/GPS双系统联合定轨可达到较好的精度，其中GEO卫星轨道精度约291 cm，IGSO/MEO卫星轨道精度优于11 cm。若BDS单系统采用上述策略进行定轨，也可达到GEO卫星299 cm和IGSO/MEO卫星14.4 cm的近似等价定轨精度。  相似文献   

利用短期静态PPP结果反演海潮负荷位移     

魏国光  王琪洁  彭葳  郭良琦 《大地测量与地球动力学》2019,39(6):607-612

采用香港11个GPS测站的观测资料进行1 h、2 h、3 h和4h静态PPP解算，获得4组PPP坐标序列，利用调和分析求取11个测站处8个主要分潮的负荷位移参数（振幅和相位），将其与海潮模型计算的负荷位移参数进行对比，并比较分析PPP反演值与海潮模型值改正海潮负荷信号的效果。结果表明，垂直和水平方向上，不同PPP结果反演8个分潮的负荷位移分别具有约5 mm和7 mm的差异；PPP反演8个分潮垂向负荷位移优于全球海潮模型，但水平方向上的反演效果稍弱。  相似文献