基于立方体元的Shear - warp体绘制加速算法

大规模三维地震数据的直接体绘制,是一个计算和数据双重密集型问题.为了提高三维图像的重建效率,提出了一种基于立方体元的Shear- warp地震数据体绘制算法.该算法通过构建立方体元在相邻的体素点之间建立联系;然后根据地震数据的特征对体元进行分类,在绘制过程中通过二叉树索引,快速定位分类结果.通过索引结果,避免了对等值体...     

基于线粒体Cytb基因的10种石珊瑚的系统发育关系

通过PCR扩增直接测序得到10种徐闻石珊瑚线粒体Cyt b基因部分序列,分析其碱基组成和变异频率,并采用Neighbor-Joining和Maximum Parsimony法构建系统发育树。结果显示:序列中A+T比例为61.0%,G+C比例为39.0%,前者明显高于后者,碱基替换主要发生在密码子第3位;滨珊瑚科分类与传统分类存在一定差异,揭示传统形态学分类可能受珊瑚骨骼生长可塑性限制,造成分类不准确;刺柄珊瑚属与蜂巢珊瑚科各属亲缘关系较近。     

粒子群神经网络混合模型在农用地分等中的应用

针对常规农用地分等模型因子权重计算存在人为干扰和神经网络模型自身优化过程中易陷入局部最优的情况,该文综合了BP神经网络非线性权重数据挖掘特性和粒子群的全局优化能力,建立了农用地分等计算的粒子群神经网络混合模型(PSO-BP网络模型),并应用于广东省揭西县农用地分等计算中,发现PSO-BP网络模型能避免定级因子权重确定的人为干扰,同时具有较高的优化效率,应用效果较好。     

Model tree approach for prediction of pile groups scour due to waves

Scour around piles could endanger the stability of the structures placed on them. Hence, an accurate estimation of the scour depth around piles is very important in coastal and marine engineering. Due to the complex interaction between the wave, seabed and pile group; prediction of the scour depth is not an easy task and the available empirical formulas have limited accuracy. Recently, soft computing methods such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) have been used for the prediction of the scour depth. However, these methods do not give enough insight about the process and are not as easy to use as the empirical equations. In this study, new formulas are given that are easy to use, accurate and physically sound. Available empirical equations for estimating the pile group scour depth such as those of Sumer et al. (1992) and Bayram and Larson (2000), are less accurate compared to the given equations. These equations are as accurate as other soft computing methods such as ANN and SVM. Moreover, in this study, safety factors are given for different levels of acceptable risks, which can be so useful for engineers.     

硅化木的特征及供观赏意义

硅化木是亿万年前的原始树木被深埋于地下并经二氧化硅交代而形成的一种木化石。它记录并见证了亿万年前的地质变迁和物种变化,兼有化石之科学内涵,美玉之华贵和奇石之美妙。本文阐述了硅化木的主要特征、成因、形成时代以及观赏和收藏意义。     

基于BP神经网络的复合污染体系中沉积物吸附阿特拉津的规律

建立了Cu2+和阿特拉津(AT)复合污染体系中沉积物各活性组分及其交互作用对AT吸附量影响的BP神经网络模型,模型预测值和实验值的相关系数达到0.97,各集合的平均偏差均小于10%。模型显示,AT在沉积物上的主要吸附位是铁氧化物。沉积物吸附AT时铁氧化物、锰氧化物、有机质之间存在显著的交互作用,铁氧化物、锰氧化物交互作用贡献率为-130%～80%,铁氧化物、有机质交互作用贡献率为5%～28%,锰氧化物、有机质交互作用贡献率为-200%～-70%。各活性组分吸附AT的能力及其交互作用受Cu2+的影响较大,其中:Cu2+对AT在铁氧化物上的吸附表现为拮抗作用,对AT在锰氧化物上的吸附表现为协同作用,而对AT在有机质上的吸附影响不显著,同时Cu2+减弱了铁氧化物-锰氧化物和锰氧化物-有机质的交互作用影响,增强了铁氧化物-有机质的交互作用影响。     

Assessing adaptability of planted trees using leaf traits: A case study with Robinia pseudoacacia L. in the Loess Plateau, China

Leaf trait patterns and their variations with climate are interpreted as an adaptive adjustment to environment.This study assessed the adaptability of planted black locust (Robinia pseudoacacia L.) based on the analysis of leaf traits and the comparison of its leaf traits with inter-specific ones existing in the same area.We measured some water and N use related leaf traits: leaf dry mass per unit area (LMA) and N,P and K concentrations based on both leaf area (Narea,Parea and Karea) and leaf mass (Nmass,Pm...     

Artificial neural network model for prediction of rock properties from sound level produced during drilling

In many rock engineering applications such as foundations, slopes and tunnels, the intact rock properties are not actually determined by laboratory tests, due to the requirements of high quality core samples and sophisticated test equipments. Thus, predicting the rock properties by using empirical equations has been an attractive research topic relating to rock engineering practice for many years. Soft computing techniques are now being used as alternative statistical tools. In this study, artificial neural network models were developed to predict the rock properties of the intact rock, by using sound level produced during rock drilling. A database of 832 datasets, including drill bit diameter, drill bit speed, penetration rate of the drill bit and equivalent sound level (L_eq) produced during drilling for input parameters, and uniaxial compressive strength (UCS), Schmidt rebound number (SRN), dry density (ρ), P-wave velocity (V_p), tensile strength (TS), modulus of elasticity (E) and percentage porosity (n) of intact rock for output, was established. The constructed models were checked using various prediction performance indices. Goodness of the fit measures revealed that recommended ANN model fitted the data as accurately as experimental results, indicating the usefulness of artificial neural networks in predicting rock properties.     

倾角导向体控制的气烟囱识别技术及其在海拉尔盆地贝尔凹陷中的应用

在调研了国内外气烟囱研究成果与思路的基础上,通过全新的地震解决方案——OpendTect平台提供的基于属性多层感知器(MLP)的人工神经网络(ANN)的方法预测气烟囱的发生概率体,并利用倾角导向体对算法进行改进,提出倾角控制下的地震气烟囱识别技术,很好地补充并发展了模式识别技术在地震勘探领域的应用。通常该技术一方面可以解释运移通道和浅层气成藏的原因;另一方面,形成的气烟囱地震数据体还可以预测源岩的发育情况;此外,对于判别断层封闭性也非常适用。最后应用这一技术对海拉尔盆地贝尔凹陷一含油区块的运移通道和成藏规律进行研究,分析该研究区的断裂发育特征和运移通道类型,总结了油源、通道、储层和盖层的空间配置关系,并建立相应的成藏模式。