植被波谱空间尺度效应及尺度转换方法初步研究

基于遥感图像的地物识别需要大量地物波谱数据的支持,而现有地物波谱库中收集到的同一地物的波谱,由于其测量尺度和方法的差异,波谱也存在很大差异.以冬小麦为例,首先介绍了材料波谱、端元波谱和像元波谱这3种不同尺度波谱的概念,并以实验数据分析了不同测量尺度下波谱的差别,以此说明波谱尺度转换的必要性.然后利用物理模型和统计模型建立不同测量尺度下的波谱转换关系.分别验证了SAILH模型和线性光谱混合模型在波谱转换中的精度.研究表明,在大尺度上采用统计模型,在小尺度上采用非线性的物理模型可以解释不同尺度观测植被波谱之间的差异.     

Study on the connectivity of heavy oil reservoirs by ultraviolet spectrum technique in the western part of the QHD32-6 oilfield

As a new method, the ultraviolet spectrum technique is applied to studying the connectivity of biode-gradable heavy oil reservoirs. The similarity of crude oils can be judged according to the extinction coefficient （E） because aromatic hydrocarbons and non-hydrocarbons have conjugated bonds and obvious absorption in the ultraviolet range, and different materials have different characteristics and additives. The relationship diagram is made in terms of the extinction coefficients （E） of the samples by taking E as the Y-axis and wavelength as the X-axis. The connectivity of oil reservoirs is estimated according to the curve positions and characteristic fingerprints of the sampies. The connectivity of part of the reservoirs in the western part of the QHD32-6 oilfield was studied with this method. The results showed that the connectivity of samples from wells F7 and F8 in the Nm-2 oil reservoir zone is good, that of samples from wells F17 and F20 in the Nm-1 oil reservoir zone also is good, and that of samples from wells F17, F19, and F20 is poor.     

基于光谱和纹理的SVM矿化蚀变信息提取研究

针对传统矿化信息提取方法单一,利用光谱或纹理、信息量相对较少、需要大量样本的缺陷,利用基于光谱和纹理的支持向量机(SVM)原理,建立矿化信息提取模型.选择青海泽库县析界日地区作为典型研究区.首先提取研究区光谱和纹理信息,选取训练样本;然后求解最优超平面,进而确定决策函数;最后泛化推广识别其他待识别的样本.通过所提取的遥感蚀变异常信息与重砂异常点叠加分析,叠加基本吻合;从野外实地验证来看,均发现了不同程度的矿化现象,并指出了5个重点异常区.     

浅谈煤田地震勘探中影响纵向分辨率的主要因素

针对野外数据采集参数对分辨率的影响,对资料处理过程中静校正、反褶积、速度分析、Q补偿及谱白化等几个重要模块原理进行分析,指出在高速岩层中数据采集,应采用小药量激发,小道距,小排列,全三维,不组合、单点数字检波器接收。高密度多点采集,能够充分保护有效地质信息的高频成份,在更大范围内满足高分辨地震勘探的要求。资料处理过程中,在保证同相叠加和高信噪比的前提下,采用提高分辨率的模块以弥补地震波在传播过程中损失的高频成份。实践证明通过高保真、宽频带数据采集和高信噪比前提下的高分辨率数据处理,完全能够为矿井采掘提供高精度地震勘探资料。     

新鲜煤和氧化煤自燃倾向性的FTIR对比分析

利用傅立叶变换红外光谱（FTIR）分析技术,对乌达矿区低温氧化和新鲜煤样进行了红外光谱测试,通过两类煤样的特征官能团吸光度的对比分析．揭示了煤在氧化前后特征官能团的变化规律：氧化后的煤样中脂肪族和芳烃CH减少,酸酐C=O从无到有,芳烃骨架C=C基本不变,进而研究了其对煤自燃倾向性的影响。结果表明脂肪族和羟基含量越高,煤自燃倾向性越大。     

地震信号的相位恢复及误差分析

常见的几种地震子波有:零相位子波、最小相位子波、最大相位子波和混合相位子波。以不同类型的信号为激发子波,对不同地层模型合成地震道。仅用原始合成地震道记录的振幅谱来恢复原始记录,讨论不同方法下,由于激发子波的不同,对比分析原始记录与恢复记录的差异,及其恢复效果。     

高光谱遥感数据三次光滑样条滤噪

分析了高光谱遥感数据噪声滤波的作用和现状，实现了一种基于三次光滑样条函数的高光谱遥感数据噪声滤波算法，解决了目前高光谱遥感数据噪声滤波算法自适应效果不理想的问题。通过对AVIRIS数据试验，证明此方法简单易行，计算精度高，具有良好的局部自适应拟合能力，并能有效地滤除数据中的噪声，改善数据质量。     

笼状水合物拉曼光谱特征与结构水合数的耦合关系

为了探讨不同气体组分和环境介质对形成笼状结构类型水合物和水合数的影响, 开展了一元体系 (CH4、CO2、C3H8 )和二元体系(CH4 +CO2、CH4 +C3H8、CH4 +N2 )的水合物生成结晶充填过程、结晶构型和动力学特性分析, 并对生成的水合物进行了拉曼光谱分析。结果表明单组分甲烷充填小孔穴 512和大孔穴 512 62 形成Ⅰ型笼状结构水合物(SⅠ), 二氧化碳和丙烷只占据大孔穴 512 64 形成Ⅱ型笼状结构水合物 (SⅡ ); 而二元混合组分中小孔穴中只充填有甲烷, 而没有CO2、N2 和C3H8。应用反褶积的ν1 对称谱带测定了CH4 分子在Ⅰ型结构大孔穴和小孔穴中的相对占有率,并根据谱带的面积比(对应于小孔穴与大孔穴)计算了平衡条件下甲烷水合物孔穴占有率及其耦合的水合数, 认为气体分子的大小不仅影响它所充填的孔穴形态和类型, 而且影响水合物生成的结构类型和水合数。     

Evaluation of the Reduction in Uncertainty Obtained by Conditioning a 3D Stochastic Channel to Multiwell Pressure Data

A stochastic channel embedded in a background facies is conditioned to data observed at wells. The background facies is a fixed rectangular box. The model parameters consist of geometric parameters that describe the shape, size, and location of the channel, and permeability and porosity in the channel and nonchannel facies. We extend methodology previously developed to condition a stochastic channel to well-test pressure data, and well observations of the channel thickness and the depth of the top of the channel. The main objective of this work is to characterize the reduction in uncertainty in channel model parameters and predicted reservoir performance that can be achieved by conditioning to well-test pressure data at one or more wells. Multiple conditional realizations of the geometric parameters and rock properties are generated to evaluate the uncertainty in model parameters. The ensemble of predictions of reservoir performance generated from the suite of realizations provides a Monte Carlo estimate of the uncertainty in future performance predictions. In addition, we provide some insight on how prior variances, data measurement errors, and sensitivity coefficients interact to determine the reduction in model parameters obtained by conditioning to pressure data and examine the value of active and observation well data in resolving model parameters.