约束稀疏脉冲反演在大庆茂兴地区泉四段储层研究中的应用

笔者从约束稀疏脉冲反演技术的基本原理入手,重点说明稀疏脉冲反演在大庆茂兴地区泉四段储层的应用,其基本步骤和方法,如子波估算、初始模型建立和Lambda取值等,为约束稀疏脉冲反演技术的合理应用提供参考.     

几种地震波阻抗反演方法的比较分析与综合应用

地震波阻抗反演是确保地震数据、测井资料和地质信息正确综合的重要技术.对当前石油工业界常用的地震波阻抗反演方法的原理、技术关键、优缺点及应用条件做了较为详细的分析和评述,并展示了几种反演方法综合应用的实例.实例表明,遵照拟定的技术流程,能够在实际生产中较好地使用各种反演方法,并得到较为可靠的一体化综合储层预测结果,可以降低油气勘探开发的风险.     

SAR影像仿真与水下地形反演的阻尼牛顿—行作用法

研究了浅海SAR影像仿真与水下地形反演的数值方法。将仿真与反演问题模型处理为相互独立的依赖于时间的非线性偏微分方程动力系统。使反演问题模型仅依赖于SAR影像灰度值，计算可一次性完成，计算过程中无须与正问题进行数据交互，在此基础上，提出了仿真与反演的一种新的一致性优化算法：阻尼牛顿-行作用法．数值实验结果表明，算法对SAR影像仿真问题具有高效、可靠、计算稳定的特征；对水下地形反演问题也有较强的数值稳定性和较高的计算精度。本文3个算例反演出的地形与实际地形之间的相关系数都优于0．72029．     

张量组稀疏表示的高光谱图像去噪算法

提出了一种基于张量组稀疏表示的高光谱遥感影像降噪。高光谱影像数据可视为三阶张量。首先,高光谱图像被划分为小的张量分块,然后,对相似的张量分块进行聚类,并对聚类分组进行稀疏表示。基于高光谱图像的空间非局部自相似性和光谱相关性,将张量组稀疏表示模型分解为一系列无约束低秩张量的近似问题,进而通过张量分解进行求解。对模拟和真实高光谱数据进行试验,验证了该算法的有效性。     

变序字典学习AO-DL的资源三号遥感影像云去除

采用了一种压缩感知方法进行遥感影像去云。该方法以压缩感知为理论基础,在采用K-SVD字典学习与稀疏表示的正交匹配追踪算法(OMP)相结合的同时,在字典原子训练的过程中加入某种特定的排序规则,使得各个影像字典在拥有各自影像属性的同时其原子也具备相似的排列顺序,减小影像间差异的干扰,使得遥感影像受云和阴影污染区域的重建取得良好的效果。最后应用两组相同地区不同时域的资源三号卫星影像进行了试验验证。     

Random seismic noise attenuation by learning-type overcomplete dictionary based on K-singular value decomposition algorithm

The transformation of basic functions is one of the most commonly used techniques for seismic denoising,which employs sparse representation of seismic data in the transform domain. The choice of transform base functions has an influence on denoising results. We propose a learning-type overcomplete dictionary based on the K-singular value decomposition( K-SVD) algorithm. To construct the dictionary and use it for random seismic noise attenuation,we replace fixed transform base functions with an overcomplete redundancy function library. Owing to the adaptability to data characteristics,the learning-type dictionary describes essential data characteristics much better than conventional denoising methods. The sparsest representation of signals is obtained by the learning and training of seismic data. By comparing the same seismic data obtained using the learning-type overcomplete dictionary based on K-SVD and the data obtained using other denoising methods,we find that the learning-type overcomplete dictionary based on the K-SVD algorithm represents the seismic data more sparsely,effectively suppressing the random noise and improving the signal-to-noise ratio.     

基于面向对象与深度学习的榆树疏林识别方法研究

榆树疏林是浑善达克沙地中一种特殊的植被类型,它对于维持区域生态系统稳定具有重要意义,在防风固沙、涵养水源、调节气候等方面发挥着重要的作用。本文利用无人机影像与GF-2影像,对高分辨率数据源中榆树疏林的两种自动识别方法进行了研究。在面向对象方法中,首先通过计算影像对象的局部方差变化率得到了最佳分割尺度;其次采用随机森林算法对初选特征的重要性进行排序,并删除无关特征;最后分别对支持向量机（SVM）、随机森林（RF）、深度神经网络（DNN）3种分类器进行参数寻优与榆树疏林提取。此外,在ENVI5.5中基于TensorFlow框架,利用U-Net构建深度学习模型对榆树疏林进行了提取,并与面向对象方法进行对比。结果显示：① 通过面向对象方法过程的优化,最终的识别精度较以往研究有所提升,GF-2影像中SVM总体精度为90.14%,RF总体精度为 90.57%,DNN总体精度为91.14%;无人机影像中SVM总体精度为97.70%, RF与DNN总体精度为97.42%。② 深度学习方法中,GF-2影像的总体精度为91.00%,无人机影像的总体精度达到了98.43%。研究结果说明在榆树疏林提取中,无人机影像具有更高的空间分辨率,更丰富的纹理、形状等信息,能达到比GF-2影像更高的精度。面向对象方法对于2种影像都有较高的适用性;深度学习的方法在本文中更适用于无人机影像,它可以有效地减少无人机影像中的错分现象。     

煤和瓦斯突出的动态预测

以阳泉矿区某矿71404工作面的突出事件为例,利用疏系数自回归预报的动态数据处理方法对该工作面突出的具体时间的时间序列进行了分析处理。实例证明,该方法行之有效,预测精度高,它为研究煤和瓦斯突出并进行有效预测预报探索出了一条新的途径。      

稀疏约束下的MVC-NMF算法研究

最小体积约束的非负矩阵分解算法(MVC-NMF)不需要假定纯像元的存在,并且在自动提取端元的同时,能够获取每种端元所对应的丰度图,然而该方法并没有考虑丰度矩阵的稀疏特性。提出了将稀疏约束引入MVC-NMF算法中,提高了算法的精度。实验结果表明:该方法在相同迭代次数条件下比MVC-NMF算法解混的精度更高。     

Three‐dimensional potential field data inversion with L0 quasinorm sparse constraints

The quantitative explanation of the potential field data of three‐dimensional geological structures remains one of the most challenging issues in modern geophysical inversion. Obtaining a stable solution that can simultaneously resolve complicated geological structures is a critical inverse problem in the geophysics field. I have developed a new method for determining a three‐dimensional petrophysical property distribution, which produces a corresponding potential field anomaly. In contrast with the tradition inverse algorithm, my inversion method proposes a new model norm, which incorporates two important weighting functions. One is the L0 quasi norm (enforcing sparse constraints), and the other is depth‐weighting that counteracts the influence of source depth on the resulting potential field data of the solution. Sparseness constraints are imposed by using the L0 quasinorm on model parameters. To solve the representation problem, an L0 quasinorm minimisation model with different smooth approximations is proposed. Hence, the data space (N) method, which is much smaller than model space (M), combined with the gradient‐projected method, and the model space, combined with the modified Newton method for L0 quasinorm sparse constraints, leads to a computationally efficient method by using an N × N system versus an M × M one because N ? M. Tests on synthetic data and real datasets demonstrate the stability and validity of the L0 quasinorm spare norms inversion method. With the aim of obtaining the blocky results, the inversion method with the L0 quasinorm sparse constraints method performs better than the traditional L2 norm (standard Tikhonov regularisation). It can obtain the focus and sparse results easily. Then, the Bouguer anomaly survey data of the salt dome, offshore Louisiana, is considered as a real case study. The real inversion result shows that the inclusion the L0 quasinorm sparse constraints leads to a simpler and better resolved solution, and the density distribution is obtained in this area to reveal its geological structure. These results confirm the validity of the L0 quasinorm sparse constraints method and indicate its application for other potential field data inversions and the exploration of geological structures.