一类曲线图象重建卷积反投影算法     

吴一刚  邱佩璋 《CT理论与应用研究》1992,(4)

本文给出了一类平面曲线 s=4(x,y)·coxθ+B(x,y)·sinθ上的 Radon 变换卷积反投影算法,并进行了数值模拟,其重建结果与真实数据相吻合,其中 A(x,y)和 B(x,y)满足一定条件.  相似文献   

基于卷积反投影CT重建的一种新型实用滤波函数   总被引：2，自引：0，他引：2  

杨民  路宏年  傅健 《CT理论与应用研究》2000,9(Z1):29-32

在实际的CT重建中,由于受散射、射束硬化等低频干扰因素的影响,使得重建图像产生"杯状”伪像以及图像模糊降质.针对在ICT工程实践中投影数据存在诸如此类的低频污染问题,本文提出了一新型实用滤波函数取代传统的滤波函数,得到了较为满意的重建效果.  相似文献   

卫星测高在物理大地测量应用中的若干问题   总被引：4，自引：2，他引：4  

李建成  宁津生  晁定波 《武汉大学学报(信息科学版)》1996,(1)

总结了卫星测高在物理大地测量领域的应用，描述了卫星测高数据逼近地球重力场、确定海面地形、改善卫星轨道参数以及求解重力异常的数学模型和数学原理。  相似文献   

基于VIIRS数据的油气平台提取技术研究   总被引：1，自引：0，他引：1  

李强  苏奋振  王雯玥 《地球信息科学学报》2017,19(3):398-406

油气平台作为海上油气资源勘探开发的主要设备之一,其数量与空间分布反映了一个区域油气资源的开发状况。普通光学影像易受天气状况影响,而雷达数据成本较高,这给油气平台的检测带来了一定的困难。由于油气平台作业过程中,需要灯光照明,同时伴气的燃烧也产生很强的灯光,因此可以通过检测灯光来实现油气平台的提取。针对VIIRS数据具有强夜间光探测能力,本文提出了一种卷积运算临界值法对海上油气平台进行提取。首先对2期不同时相的VIIRS数据进行卷积运算,对亮像元进行了增强处理,对背景像元进行弱化处理,从而明确区分疑似目标与背景,然后以0值为分界点,对疑似目标进行提取,最后利用油气平台相对静止的特性,通过邻域分析,实现油气平台的提取。结果表明,本文提出的卷积运算临界值法可以有效地提取油气平台,提取准确率约为85.4%,同时可以有效地减少由于经验阈值对提取结果造成的误差。  相似文献   

Efficient discrete modelling of composite structures for rockfall protection     

《Computers and Geotechnics》2017

This paper presents a discrete framework for the modelling of composite structures for rockfall protection. The model is applied to analyse the dynamic response of a cylindrical damping module upon impact of a boulder. The damping module consists of a cylindrical wire mesh, two steel rings, a boundary rope, a geotextile lining and a granular filling material. The chain-link wire mesh, the steel rings and the boundary rope are represented with deformable cylinder elements. The geotextile lining is incorporated into the openings of the wire mesh by using deformable facets. The filling material is represented using spherical particles.  相似文献   

基于双流残差卷积神经网络的养殖鳗鲡(Anguilla)摄食强度评估研究     

李凯  江兴龙  许志扬  林茜 《海洋与湖沼》2023,54(4):1207-1216

为实现对养殖鳗鲡(Anguilla)摄食强度的准确评估,提出了一种基于双流残差卷积神经网络的鳗鲡摄食强度评估方法,该方法针对传统双流网络(Two-stream)中存在的问题做出了相应的改进。首先针对传统双流网络存在网络结构较浅,无法提取到充分的鳗鲡摄食行为特征的问题,选择使用ResNet50网络进行替换,以提取到更具代表性的特征。其次针对传统双流网络最后的分类结果是把空间流和时间流的得分取平均值融合而获得,这种方式较为简单,且其空间流和时间流网络为独立进行训练,容易导致网络出现学习不到鳗鲡摄食行为的时空关联特征的问题,选择使用特征层融合方式对空间流和时间流网络提取获得的特征进行融合,让网络能够并行进行训练,以提取到时空信息间的关联特征。试验结果表明:文内提出的基于双流残差卷积神经网络的鳗鲡摄食强度评估方法准确率达到98.6%,与单通道的空间流和时间流网络相比,准确率分别提升了5.8%和8.5%,与传统的双流网络相比准确率也提升了3.2%。  相似文献   

转换波Kirchhoff叠前时间偏移的成像优化方案          下载免费PDF全文

岳玉波  李振春  钱忠平  孙鹏远  杨雪霖  薛贵仁 《地球物理学报》2018,61(3):1188-1195

叠前时间偏移是转换波数据处理流程中的重要技术环节.由于下行P波和上行S波对应着不同类型的传播速度和传播机理,使得转换波偏移的诸多实施环节和实现技巧均有别于常规的纵波偏移.本文就VTI介质转换波Kirchhoff叠前时间偏移实现过程中的几个关键技术环节进行研究分析,并给出了相应的实施方案.首先,依据常速假设推导了简化的转换波时间偏移振幅加权函数,在保证偏移的高效计算的同时,兼顾了成像振幅的准确性;其次,依据直射线近似假设,推导了转换波偏移的最大无假频频率计算公式,确保了算子假频的有效滤除;接下来,设计使用了依据共转换点对称的偏移孔径,保证了偏移孔径的优化选取.此外,本文还提出利用三次卷积插值进行成像网格上参数值的插值运算,以避免传统线性插值算法的非平滑性对偏移剖面中高频信息的不良影响.多个算例的应用效果证明,本文所给出的关键技术环节的实施方案可以有效的提升转换波叠前时间偏移的成像效果.  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   

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   

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