非负矩阵分解基向量分析及其在矿产预测中的应用

针对矿产预测数据具有复杂性以及矿质异常信息具有稀疏性的问题,基于非负矩阵分解的非负性和降维的特点,结合稀疏性,提出一种基于NMF基向量分析的矿产预测数据处理方法,并对基向量和原变量以及基向量之间的关系进行分析。广东新寮岽铜多金属矿区数据实验结果表明,NMF方法在不同特征值和相似稀疏度的条件下的基向量形态基本稳定,在保留找矿信息的同时可有效地实现对矿产预测数据的稀疏化。NMF方法对于矿产预测具有重要的实际意义。     

交叉偶极子声测井多频率反演地层三阶弹性常数和地应力研究

基于声弹性理论,本文详细论述了利用交叉偶极子多频率相速度反演地层三阶弹性常数和地应力的理论及信噪比对反演结果的影响.定性和定量分析表明在速度应力方程中,不含三阶弹性常数项与含三阶弹性常数项相比对速度的影响可以忽略,由此只利用一次测井资料同时反演地应力和三阶弹性常数是一个严重病态的问题或一个不确定性问题.针对不同的反演模型,详细考察了信噪比对各自简化模型和精确模型反演结果的影响.数值模拟结果表明,利用一次测井采用精确模型反演全部四个参数方法受信噪比影响严重.而利用不同流体压力下的测井资料进行反演时可以采用简化模型取代精确模型以便简化计算和提高反演速度,因为简化模型和精确模型有着几乎相同的精度;同时,分步反演的方法可以较好地反演出原位地应力及三阶弹性常数.     

横向各向同性介质弹性波多分量叠前逆时偏移

随着油气勘探程度的提高,隐蔽油气藏在增储上产方面起到了重要作用,因此发展基于各向异性介质的多分量偏移方法是非常必要的.本文基于横向各向同性（VTI）介质,从二维弹性波速度\|应力方程出发,通过在时间上的二阶差分和空间上的交错网格高阶差分对方程进行离散,得到弹性波交错网格高阶差分的多分量逆时偏移算子.在激发时间成像条件的应用过程中引入Poynting矢量进行成像并消除逆时偏移所引起的低频干扰,在此基础上实现了VTI介质中二维弹性波叠前多分量逆时深度偏移.理论模型的偏移处理表明,该方法能够对地层进行准确成像,并可以消除逆时偏移所引起的低频噪声.     

基于最近邻支撑向量特征线融合算法的核爆地震识别

为增强核爆地震模式分类器的泛化能力以提高对核爆炸事件的准确识别能力,论文提出了一种选择支撑向量样本集来表征训练样本集的最近邻支撑向量特征线分类算法,用以训练时扩展核爆地震的训练样本库,提高分类器的泛化能力.该算法用于核爆炸和地震的识别结果发现,和最近邻特征线分类器相比,提出的算法降低了计算复杂度,但识别能力却有些许降低.对新算法的分析发现,纯粹的支撑向量集不能完全代表原始样本空间集,支撑向量比例在其中有重要作用,为发挥支撑向量比例的作用以提高核爆分类器的识别能力,提出了最近邻支撑向量特征线融合算法.最后以核爆地震数据库对上述算法进行了检验和分析,理论分析和识别结果证实,在相同的训练样本选择条件下,最近邻支撑向量特征线融合算法对于核爆炸的识别来说具有较好的泛化能力,正确识别率达到90.3%,且优于支持向量机算法和最近邻特征线算法.     

任意空间取向TI弹性张量解析表述

本文理论给出任意空间取向TI(ATI)四阶弹性张量的解析表述,其以VTI弹性常数及其简单组合为系数,包括各向同性项、TI对称轴方向矢量分量的二次项和四次项,其中TI对称轴方向矢量可以在固定坐标系定义, 也可以相对三维倾斜界面甚至相对波传播方向.相比四阶张量变换法和Bond变换法,ATI弹性张量能简洁而透明地为本构关系和波动方程提供四阶张量的所有元素. ATI弹性张量能为诸多方面的理论研究提供支撑.     

Late Cenozoic stress states around the Bolu Basin along the North Anatolian Fault, NW Turkey

This study defines the Late Cenozoic stress regimes acting around the Bolu Basin along the North Anatolian Fault in northwestern Turkey. The inferred regional stress regime, obtained from the inversion of measured fault-slip vectors as well as focal mechanism solutions, is significant and induces the right-lateral displacement of the North Anatolian Fault. The field observations have also revealed extensional structures in and around the Bolu Basin. These extensional structures can be interpreted as either a local effect of the regional transtensional stress regime or as the result of the interaction of the fault geometries of the dextral Duzce Fault and the southern escarpment of the North Anatolian Fault, bordering the Bolu Basin in the north and in the south, respectively.The inversion of slip vectors measured on fault planes indicates that a strike-slip stress regime with consistent NW- and NE-trending σ_Hmax(σ₁) and σ_Hmin(σ₃) axes is dominant. Stress ratio (R) values provided by inversion of slip vectors measured on both major and minor faults and field observations show significant variations of principal stress magnitudes within the strike-slip stress regime resulting in older transpression to younger transtension. These two stress states, producing dextral displacement along NAF, are coaxial with a consistent NE-trending σ₃ axis. The earthquake focal mechanism inversions confirm that the transtensional stress regime has continued into recent times, having identical horizontal stress axis directions, characterized by NW and NE-trending σ₁ and σ₃ axes, respectively. A locally consistent NE-trending extensional, normal faulting regime is also seen in the Bolu Basin. The stress-tensor change within the strike-slip stress regime can be explained by variations in horizontal stress magnitudes that probably occurred in Quaternary times as a result of the westward extrusion of the Anatolian block.     

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs¹³⁷ activity given the measurements taken in the region of Briansk following the Chernobyl accident.     

基于智能反分析的水布垭电站地下厂房机窝施工方案优化

采用正交设计、三维数值模拟、遗传算法与支持向量机,建立了清江水布垭电站地下厂房的智能反分析流程。按照上述流程,利用前6层开挖的现场监测信息对有关岩层的力学参数进行反分析,根据反分析的参数对后续的机窝施工进行数值模拟计算和方案优化。推荐的机窝施工方案采用槽挖方式,同时应用锚桩、软岩置换、锚喷支护等措施,兼顾了高边墙和机窝岩台的稳定性。优化结果对厂房施工具有重要的指导作用。     

基于不同窗口纹理特征的SVM土壤盐渍化信息提取方法与精度分析研究

以塔里木盆地北缘绿洲--渭干河-库车河三角洲绿洲为例,借助ENVI遥感软件,利用ETM+数据,探讨了该绿洲土壤盐渍化信息提取的方法.传统的遥感图像分类方法多数在解决问题上存在精度不高、分类效率较低、不确定性强的缺陷,所以,选择好的分类方法对于提取盐渍化信息是至关重要的.近年来,将SVM应用于遥感图像分类已成为新的发展趋势.文章提出了基于纹理特征的支持向量机(Support Vector Machine,SVM)的分类方法,得出以下结论:分别结合3×3,5×5,7×7,9×9,11×11,13×13窗口纹理特征和光谱的SVM分类精度都很高,达到93%以上.并且在验证分类精度时,发现结合光谱和9×9窗口纹理信息的SVM分类的结果更符合实际情况.所以说加入纹理特征后使得光谱信息比较接近的3类地物(重度、中度、轻度盐渍地)的区分性增大,从而使精度提高.因此,基于纹理特征的SVM分类方法更有利于遥感图像分类和盐渍化信息监测,是地物遥感信息提取的有效途径.     

基于对象关系数据库的矢栅一体化查询与操作研究

在分析矢量栅格数据特点的基础上,提出一系列矢栅一体化查询函数,基于PostgreSQL/PostGIS数据库,实现了支持栅格数据存储与组织的扩展数据类型,并对栅格数据类型与PostGIS矢量数据一体化查询函数进行了扩展实现。最后,通过实例验证了矢栅一体化查询函数的表达能力和解决实际问题的应用前景。