Moving averages for Gaussian simulation in two and three dimensions   总被引：6，自引：0，他引：6  

Dean S. Oliver 《Mathematical Geology》1995,27(8):939-960

The square-root method provides a simple and computationally inexpensive way to generate multidimensional Gaussian random fields. It is applied by factoring the multidimensional covariance operator analytically, then sampling the factorization at discrete points to compute an array of weighted averages that can be convolved with an array of random normal deviates to generate a correlated random field. In many respects this is similar to the LUdecomposition method and to the one-dimensional method of moving averages. However it has been assumed that the method of moving averages could not be used in higher dimensions, whereas direct application of the matrix decomposition approach is too expensive to be practical on large grids. In this paper, I show that it is possible to calculate the square root of many two- and three dimensional covariance operators analytically so that the method of moving averages can be applied directly to the problem of multidimensional simulation. A few numerical examples of nonconditional simulation on a 256×256 grid that show the simplicity of the method are included. The method is fast and can be applied easily to nested and anisotropic variograms.  相似文献   

采用复合样条有限元方法求解板的动力初值问题     

毕继红  郭淑卿  李忠献 《地震工程与工程振动》2002,22(6):25-30

结构动力分析是工程设计中的重要组成部分，传统结构动力分析不能全面反映结构动力的初值特征，而Gurtin变分原理被认为是目前唯一能全面反映结构动力初值特征的变分原理。本文应用以位移为参变量的Gurtin变分原理，采用复合样条有限元的方法，即在时间域及空间域的y方向采用三次B样条函数，而在空间域的x方向采用多项式逼近广义位移，从而建立了精度较高的计算板动力初值问题的样条有限元模型。数值计算结果表明，本文所建立的方法能有效地求解板的动力初值问题，且计算精度高。  相似文献   

华南武夷山区桃溪旋卷变质核杂岩构造的基本特征及形成机制探讨   总被引：9，自引：3，他引：6  

张顺金  黄昌旗等 《福建地质》2000,19(4):188-196

核溪变质核杂岩构造位于华南的武夷山脉南段。该变质核杂岩构造由顺层塑性流变的内核、分划性右行斜落韧性变形的中间性、脆韧性右行滑落剪切变形的具薄皮构造特征的盖层组成“三层结构”。其间均为不同构造层的剥离断层（带）分划,各剥离层的上盘在下滑的同时右行走滑的特征也很明显,是一个具右行旋卷特征的变质核杂岩构造。该变质核杂岩构造于加里东期发育雏形,强烈隆升并定位于印支期,对燕山期及其以后的构造发展仍有一个控制作用。  相似文献   

膨胀卷积与金字塔表达的深度学习模型用于农村建筑物提取     

王雪  梁珂  隋立春  钟棉卿  朱剑锋 《测绘通报》2022,(4):61-65

由于农村建筑物结构多样、空间分布复杂等特征,自动提取面临较多困难。针对该问题,本文提出采用膨胀卷积和金字塔池化表达的神经网络模型用于遥感影像中农村建筑物自动提取。在膨胀卷积神经网络模块中,通过改变孔尺寸的大小,获取不同感受野的特征信息;在金字塔表达方面,每个模块输入不同尺度的信息,且同时下采样的倍率也不同,获取多维的金字塔尺度特征;最终将提取的浅层及深层尺度特征信息进行融合,构建一个改进的适用于农村建筑物目标自动提取的深度学习模型。试验结果表明,与FCN-8s和DeepLab模型提取的结果相比,本文方法在农村建筑物提取中表现较好的性能,提取精度明显提高,且更好保留了目标边界细节信息,减少了噪声。  相似文献   

福建天马定点形变台形变异常特征研究   总被引：1，自引：0，他引：1  

李华  黄声明  陈新泽 《大地测量与地球动力学》2005,25(4):59-62

应用褶积滤波的方法对福建天马跨断层短水准观测点近15年（1986～2000年）的观测资料进行了计算、处理和分析,归纳出了福建及其沿海地区中强震发生前该断层两盘的相对运动变化特征。  相似文献   

卫星遥感融合中通量守恒重采样方法与其它常用方法的比较     

田林  张亭禄  陈树果  王晓菲 《中国海洋大学学报(自然科学版)》2014,44(6):103-108

对多卫星传感器数据进行融合,首先要将多个传感器数据通过重采样算法重新投影到标准网格上。本文运用一种基于多边形切割算法的通量守恒重采样算法对图像数据进行重采样,并将该算法与3种常用的重采样算法(最邻近插值法、双线性插值法、三次卷积插值法)在信息保真方面的性能进行了比较。将所比较的重采样方法应用于两幅具有代表性的图像,其中一幅为人造图像,用于定性比较各种采样方法在图像缩放中的采样精度;另一幅为某机场卫星遥感图像,用于评价各种重采样方法在旋转图像方面采样的性能,并以定量参数(相关系数及光谱真实性)比较各种采样方法。结果表明,通量守恒重采样法对原始图像的信息保真效果最好,更适用于卫星遥感图像数据融合中的重采样。  相似文献   

扇束卷积反投影法的程序优化   总被引：6，自引：2，他引：6  

刘晓平 《CT理论与应用研究》1996,5(1):35-37

本文给出在扇束卷积反投影法中为提高运算速度，减少加权反投影运算量的一种程序优化方法和实验结果，并分别给出被测物体正反时针旋转时加权反投影的两种公式。  相似文献   

Application on Anomaly Detection of Geoelectric Field Based on Deep Learning     

WEI Lei  AN Zhanghui  FAN Yingying  CHEN Quan  YUAN Lihu  HOU Zeyu 《中国地震研究》2020,34(3):358-377

The deep learning method has made nurnerials achievements regarding anomaly detection in the field of time series. We introduce the speech production model in the field of artificial intelligence, changing the convolution layer of the general convolution neural network to the residual element structure by adding identity mapping, and expanding the receptive domain of the model by using the dilated causal convolution. Based on the dilated causal convolution network and the method of log probability density function, the anomalous events are detected according to the anomaly scores. The validity of the method is verified by the simulation data, which is applied to the actual observed data on the observation staion of Pingliang geoeletric field in Gansu Province. The results show that one month before the Wenchuan M_S8.0, Lushan M_S7.0 and Minxian-Zhangxian M_S6.6 earthquakes, the daily cumulative error of log probability density of the predicted results in Pingliang Station suddenly decreases, which is consistent with the actual earthquake anomalies in a certain time range. After analyzing the combined factors including the spatial electromagnetic environment and the variation of micro fissures before the earthquake, we explain the possible causes of the anomalies in the geoelectric field of before the earthquake. The successful application of deep learning in observed data of the geoelectric field may behefit for improving the ultilization rate both the data and the efficiency of detection. Besides, it may provide technical support for more seismic research.  相似文献   

Seismic modelling for reservoir studies: a comparison between convolutional and full-waveform methods for a deep-water turbidite sandstone reservoir     

Hamed Amini  Colin MacBeth  Asghar Shams 《Geophysical Prospecting》2020,68(5):1540-1553

Two seismic modelling approaches, that is, two-dimensional pre-stack elastic finite-difference and one-dimensional convolution methods, are compared in a modelling exercise over the fluid-flow simulation model of a producing deep-water turbidite sandstone reservoir in the West of Shetland Basin. If the appropriate parameterization for one-dimensional convolution is used, the differences in three-dimensional and four-dimensional seismic responses from the two methods are negligible. The key parameters to ensure an accurate seismic response are a representative wavelet, the distribution of common-depth points and their associated angles of incidence. Conventional seismic images generated by the one-dimensional convolutional model suffer from lack of continuity because it only accounts for vertical resolution. After application of a lateral resolution function, the convolutional and finite-difference seismic images are very similar. Although transmission effects, internal multiples and P-to-S conversions are not included in our convolutional modelling, the subtle differences between images from the two methods indicates that such effects are of secondary nature in our study. A quantitative comparison of the (normalized root-mean-square) amplitude attributes and waveform kinematics indicates that the finite-difference approach does not offer any tangible benefit in our target-oriented seismic modelling case study, and the potential errors from one-dimensional convolution modelling are comparatively much smaller than the production-induced time-lapse changes.  相似文献   

结合超像素和卷积神经网络的国产高分辨率遥感影像云检测方法   总被引：1，自引：0，他引：1  

徐启恒  黄滢冰  陈洋 《测绘通报》2019,(1):50-55

由于国产高分辨率卫星遥感影像波段少、光谱范围窄，导致传统云检测方法精度低。本文提出了基于卷积神经网络的高分辨率遥感影像云检测方法。首先采用主成分分析非监督预训练网络结构，获取待测遥感影像云特征；然后采用超像素分割方法进行影像分割；最后将检测结果影像块拼接，完成整幅影像云检测。试验效果评价表明，基于卷积神经网络的高分辨率遥感影像云检测方法不受光谱范围限制，云检测精度高，误判较少，适合国产高分辨遥感影像云检测。  相似文献