基于三角洲复合砂体构型的流动单元划分及剩余油分布模式

以涠西南凹陷W油田为例,为加深对储层非均质性的认识,开展了基于复合砂体构型的流动单元研究,总结了剩余油分布模式.基于油田稀井网资料,通过井震结合、动静结合,并参考类似储层地质知识库特点,开展了复合砂体构型解剖;利用筛选的孔隙度、渗透率和流动带指数(FZI)把研究区储层分为四类流动单元,并在复合砂体内部刻画了流动单元的分...     

黔东南大雾气候特征

利用1961~2007年黔东南州16个地面气象观测站逐日大雾日数资料,对黔东南州大雾日数的日、年、季分布特点、长期变化趋势、年代际的变化特征等进行分析。结果表明:20世纪60年代平均雾日最多,80年代最少,进入21世纪后具有逐渐增多的趋势;以秋季雾日最多,冬季次之,春季最少;以11月为最多,2月为最少。并且大雾日数有准40年的周期,在大雾多发期存在着准5年的周期性。大雾主要分布在黔东南州的中部,东南部和西北部相对较少。     

一种基于特征分类辨识的SAR图像目标检测方法

该文给出了一种基于特征分类辨识的合成孔径雷达（Synthetic Aperture Radar,SAR）图像目标检测方法。用恒虚警（Constant False Alarm Rate,CFAR）和扩展分形（Extended Fractal,EF）方法对SAR图像进行目标检测后用面积和峰值能量比算子辨识目标和背景杂波,去除一部分虚警,用小波域主成分分析（Principal Component Analysis,PCA）对每个检测窗口内的图像提取特征向量,用支持向量机（Support Vector Machine,SVM）对提取得到的特征向量进行分类,辨识目标和背景杂波,完成目标检测。使用ADTS数据对该方法进行验证和分析,实验结果表明,经过特征分类辨识后,在检测率不变的情况下,虚警数目显著降低。因此,该方法是一种有效的SAR图像目标检测方法。     

条斑紫菜栽培过程中有害藻类的分离与鉴定

针对条斑紫菜栽培过程中出现的有害藻类问题,进行了叶状体和丝状体有害藻类分离与鉴定等相关研究。通过形态学分析和分子生物学鉴定,发现叶状体中的杂藻主要有大型绿藻(浒苔、曲浒苔)、大型红藻(红毛菜)以及微型绿藻和硅藻,丝状体中的杂藻主要为褐藻、微型绿藻和硅藻。针对部分微型杂藻进行了抑制剂处理实验,结果表明低浓度的抗生素可以有效抑制部分硅藻和绿藻的生长,二氧化锗对硅藻生长具有显著的抑制效果。研究结果为系统、全面认识条斑紫菜栽培过程中的有害藻类提供了科学依据。     

Three-dimensional quantitative analysis on granular particle shape using convolutional neural network

To identify all desired shape parameters of granular particles with less computational cost, this study proposes a three-dimensional convolutional neural network (3D-CNN) based model. Datasets are made of 100 ballast and 100 Fujian sand particles, and the shape parameters (i.e., aspect ratio, roundness, sphericity, and convexity) obtained by conventional methods are used to label all particles. For the model training, by feeding the slice images of particles into the model, the contour of particles is automatically extracted, thereby the shape parameters can be learned by the model. Thereafter, the model is applied to predict shape parameters of new particles as model testing. All results indicate the model trained based on slice images cut from three orthogonal planes presents the highest prediction accuracy with an error of less than 10%. Meanwhile, the accuracy for concave and angular particles can be guaranteed. The rotation-equivariant of the model is confirmed, in which the predicted values of shape parameters are roughly independent of orientations of the particle when cutting slice images. Superior to conventional methods, all desirable shape parameters can be obtained by one unified 3D-CNN model and its prediction is independent of particle complexity and the number of triangular facets, thus saving computation cost.