Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.     

石油测井三维数据场断层可视化算法研究

在总结前人关于限制Delaunay三角剖分、地质断层理论基础上,提出了一套全新的断层处理方法。本文提供的断层处理方法的最大优势在于能以统一模式处理各种类型的断层（正断层、逆断层、平移断层）,有效地解决了含逆断层、非凸包内边界地质体重构的难题。文中介绍的方法均已在Forward．NET软件平台两维、三维绘图中实践。     

Fully Automatic Visualisation of Overlapping Sets

Visualisation of taxonomies and sets has recently become an active area of research. Many application fields now require more than a strict classification of elements into a hierarchy tree. Euler diagrams, one of the most natural ways of depicting intersecting sets, may provide a solution to these problems.
In this paper, we present an approach for the automatic generation of Euler-like diagrams. This algorithm differs from previous approaches in that it has no undrawable instances of input, allowing it to be used in systems where the output is always required. We also improve the readability of Euler diagrams through the use of Bézier curves and transparent coloured textures. Our approach has been implemented using the Tulip platform. Both the source and executable program used to generate the results are freely available.     

用于不均衡数据集的挖掘方法

传统的分类算法大多是基于数据集中各类的样本数是基本均衡的假设的,而实际应用场合中面临的往往是不均衡数据。针对不均衡数据集,利用传统的分类方法往往不能获得良好的性能,因而研究用于处理不均衡数据集的分类方法就显得相当重要,本文对相关的研究做了综述。     

一种从不完备数据集中发现规则的RS方法

Rough集(Rough SetS、Rs)理论被广泛应刑于数据分类问题,该文用基于RS的方法从不完备数据集中产生确定和可能的规则集,提出了一种新的规则发现算法,可以同时从不完备数据集中产生规则和估计缺失值,并指出了进一步的研究方向。     

Fitting Related Sets of Straight-line Data

Abstract

Many physical experiments give rise to sets of curves related by the requirement that, although certain of the curve parameters may vary from curve to curve, others should be the same for all of the curves. To get the “best” values of the common parameters, one would like to fit all of the curves simultaneously by the appropriate theoretical expressions. This paper deals with this problem, presenting an algorithm for its solution, in the case that the curves are straight lines with common slope and “best” fit is defined in the uniform (or minimax) sense.     

Chamber Recognition in Cave Data Sets

Quantitative analysis of cave systems represented as 3D models is becoming more and more important in the field of cave sciences. One open question is the rigorous identification of chambers in a data set, which has a deep impact on subsequent analysis steps such as size calculation. This affects the international recognition of a cave since especially record‐holding caves bear significant tourist attraction potential. In the past, chambers have been identified manually, without any clear definition or guidance. While experts agree on core parts of chambers in general, their opinions may differ in more controversial areas. Since this process is heavily subjective, it is not suited for objective quantitative comparison of caves. Therefore, we present a novel fully‐automatic curve skeleton‐based chamber recognition algorithm that has been derived from requirements from field experts. We state the problem as a binary labeling problem on a curve skeleton and find a solution through energy minimization. A thorough evaluation of our results with the help of expert feedback showed that our algorithm matches real‐world requirements very closely and is thus suited as the foundation for any quantitative cave analysis system.     

Automatic Surface Reconstruction from Point Sets in Space

In this paper an algorithm is proposed that takes as input a generic set of unorganized points, sampled on a real object, and returns a closed interpolating surface. Specifically, this method generates a closed 2-manifold surface made of triangular faces, without limitations on the shape or genus of the original solid. The reconstruction method is based on generation of the Delaunay tetrahedralization of the point set, followed by a sculpturing process constrained to particular criteria. The main applications of this tool are in medical analysis and in reverse engineering areas. It is possible, for example, to reconstruct anatomical parts starting from surveys based on TACs or magnetic resonance.     

燃气发电机组自动并网测控系统的设计与实现

为解决自备燃气发电机组电网与供电电网的安全并网问题,研制了一种以DSP芯片TMS320F2812为核心的发电机组自动并网测控系统.充分利用DSP芯片内部的高速A/D、CAN总线接口及其强大处理能力,使该系统具有很高的性价比.     

基于Curvelet变换的地震噪声衰减

在研究地震勘探中,随机噪声是一种频带较宽的干扰波,常规的去噪方法效果不理想.为对方位提高识别能力,首先根据具有多尺度与多方向性的Curvelet变换的基本原理及其实现方法.采用块阈值法应用于地震数据随机噪声衰减中,并进行了仿真计算和实际资料的处理.结果证明利用Curvelet变换块阈值法能比较彻底地去除噪声,去噪后的图像边缘保持良好,滤除噪声同时还保留了有效部分,去噪效果良好,具有小波变换无可比拟的优势,且易于实现,在地震资料处理中具有一定的可行性和应用价值.     

地震数据的无损压缩存储

为解决地震数据的无损压缩存储问题,研究了多种地震数据标准以及整型和浮点型数据的无损压缩算法.在此基础上,提出了地震数据的无损压缩存储方法.该方法通过分析多种地震标准的内部结构特点,有针对性地对整型和浮点型数据分别进行无损压缩.在保证了地震数据文件中各参数、标识等信息不被破坏的条件下,最大限度地减小了地震数据文件的大小.试验证明,本方法不仅对整型数据而且对于浮点型数据都可以做到无损的数据压缩与还原.     

非平衡数据集分类方法探讨

由于数据集中类分布极不平衡,很多分类算法在非平衡数据集上失效,而非平衡数据集中占少数的类在现实生活中通常具有显著意义,因此如何提高非平衡数据集中少数类的分类性能成为近年来研究的热点。详细讨论了非平衡数据集分类问题的本质、影响非平衡数据集分类的因素、非平衡数据集分类通常采用的方法、常用的评估标准以及该问题中存在的问题与挑战。     

Simultaneous Graphic Generalization of Vector Data Sets

Manual cartographic generalization is a simultaneous process. However, most automatic approaches so far have been sequential; generalization operators are applied one at a time in a certain order. This has been the case both for model generalization (generalization of the conceptual model) and graphic generalization. Our research seeks to demonstrate that the graphic part of cartographic generalization can be formulated as an optimization problem and accordingly be solved in a single step. This paper deals with several issues regarding this optimization approach. Firstly, a set of appropriate analytical constraints for the generalization process is given, as well as rules for when to apply these constraints. In our approach, we are limited to formulating these constraints on point locations. Secondly, least-squares adjustment is proposed to find the optimal solution according to the constraints. Finally, the conjugate-gradients method is recommended for solving the normal equations. A prototype system for simultaneous graphic generalization has been implemented in C++, which communicates with a commercial map production system. Results from three tests of the prototype system are included in the paper.     

大型数据集的多维变量关联性挖掘

为了探索多维数据集中各变量之间的关系,特别是非函数关系,对数据集所在的n维立方体的各个维度进行划分,在得到的n维网格中定义自然概率密度函数,以此得到数据集在特定划分下的互信息值。对所有的划分取互信息最大值,正态化后即为所定义的特征张量的一项,取特征张量在给定最大网格数下的最大项的值定义为MIC,它度量了多维变量间的关联程度。     

基于CUDA的地震数据层位面自动追踪算法

层位面追踪是三维地震数据解释的一项关键操作,对于地质勘探和油藏工程有重要的指导意义。传统的层位面自动追踪算法采用的是种子点蔓延法,该方法是一个不断进行迭代计算的过程。由于三维地震数据的数据量很大,传统的基于CPU的层位面自动追踪算法的计算效率不高。文中提出了一种基于CUDA的三维层位面自动追踪算法。该算法对种子点蔓延法进行了改进,实现了层位面自动追踪的GPU并行解决方案。实验表明,与传统的基于CPU的层位面自动追踪算法相比,本算法的计算效率有明显的提升。     

Fully Automatic Registration of Image Sets on Approximate Geometry

The photorealistic acquisition of 3D objects often requires color information from digital photography to be mapped on the acquired geometry, in order to obtain a textured 3D model. This paper presents a novel fully automatic 2D/3D global registration pipeline consisting of several stages that simultaneously register the input image set on the corresponding 3D object. The first stage exploits Structure From Motion (SFM) on the image set in order to generate a sparse point cloud. During the second stage, this point cloud is aligned to the 3D object using an extension of the 4 Point Congruent Set (4PCS) algorithm for the alignment of range maps. The extension accounts for models with different scales and unknown regions of overlap. In the last processing stage a global refinement algorithm based on mutual information optimizes the color projection of the aligned photos on the 3D object, in order to obtain high quality textures. The proposed registration pipeline is general, capable of dealing with small and big objects of any shape, and robust. We present results from six real cases, evaluating the quality of the final colors mapped onto the 3D object. A comparison with a ground truth dataset is also presented.     

缺失数据数据集的组增量式特征选择

实际应用中获取到的数据集通常是动态增加的,且随着数据获取工具的迅速发展,新数据通常会一组一组地增加。为此,针对含有缺失数据的动态数据集,基于粗糙集理论,提出了一种组增量式的粗糙特征选择算法。首先分析、证明了信息熵的组增量计算公式,并以信息熵作为特征重要度的度量,在此基础上设计了基于信息熵的可有效处理含有缺失数据的动态数据集的组增量式特征选择算法。实验结果进一步证明了新算法的可行性和高效性。     

自动生成量化属性模糊集的算法

提出一种由量化属性数据自动生成模糊集及其隶属函数的算法。该算法首先用聚类方法对每个量化属性进行聚类,求得聚类中心,最后通过聚类中心构造模糊集,定义隶属函数。