基于多层次框架的三维地震图像全层位追踪方法研究
发布时间：2018-08-22 18:55
【摘要】：随着人工智能的发展,特征分类技术已经成为一种重要的数据分析方式,被广泛应用在数据挖掘、人工智能、模式识别等领域。特征分类是通过分析和辨识数据的特征和类别的关系,产生一个关于特征和类别的模型,然后利用这个模型来分析和处理新的数据特征,确定新数据的类别。本文通过研究特征分类技术和三维地震数据中层位特征,设计了三维地震数据中的全层位追踪方法。三维地震图像是地震波形在三维空间的分布,层位追踪是就是分析三维地震图像中的层位信息。目前的层位追踪工作主要依靠地质解释人员在二维剖面上进行层位标定,由于二维剖面并不能反映三维数据的全貌,人工解释在反映地质结构全貌上和效率上存在严重的不足,严格意义上的三维全层位追踪算法直接处理三维地震图像,在准确性和效率上存在很大的优势。本文就是针对上述问题对三维地震图像中的层位追踪问题进行研究,主要工作包括以下两个方面:(1)本文研究了三维地震图像中的层位特征和数据挖掘中特征分类技术,并结合三维地震图像中的波形特征、层位极值点空间分布特征以及特征分类技术的一般方法设计了基于多层次框架的三维全层位自动追踪方法,该方法将层位追踪过程按照所处理数据的粒度划分成了极值点层次、层位片段层次以及专家层次,分别从不同的层次研究三维地震图像中的层位追踪问题,在极值点层次根据极值点的空间上横向连续分布的特征设计了基于层位极值点连接的层位片段生成算法,将极值点连接成层位片段;在层位片段层次根据同一层位的波形相似性,结合GMM聚类算法设计了层位片段合并算法,将层位片段连接成大的层位;在专家层次,为了使地质解释人员可以根据实际需求调整层位追踪的结果,设计了专家级的层位结果修正算法,使得层位追踪结果更加符合实际的地质构造。该方法在不同层次对层位追踪问题进行相应的处理,最终实现了三维地震图像中三维全自动追踪。(2)本文从一种全新的角度提取三维地震图像中层位特征,通过研究三维地震图像中层位纵向分布特征提出了基于匹配搜索的全层位追踪方法。传统的层位追踪方法都是逐个层位的进行追踪,这样就忽视了三维地震图像中目标区域内时间方向上不同层位之间的关系,在三维地震图像中时间方向上层位呈现层状分布,相邻层位之间存在一定的间隙,而且不同层位之间的间隙会有一定的差异,这就是三维地震图像中的层位纵向分布特征。基于匹配搜索的全层位追踪方法主要根据时间方向上多个层位纵向分布的振幅特征和层位之间间隙的特征,设计了层位纵向分布特征提取算法、基于匹配搜索的数据块生成算法和基于振幅导向的数据块连接算法。该方法利用层位纵向分布特征提取算法和基于匹配搜索的数据块生成算法将三维地震图像中的极值点连接成三维空间中的层位块,基于振幅导向的数据块连接算法将层位块进行连接,形成地震图像中大的层位,然后针对层位的缺口问题对层位进行扩展处理得到最终的追踪结果。由于该方法充分利用了层位间的相互关系,实现了三维地震图像中全层位并行追踪,提高了层位追踪的效率和准确性。
[Abstract]:With the development of artificial intelligence, feature classification technology has become an important way of data analysis, and has been widely used in data mining, artificial intelligence, pattern recognition and other fields. This paper designs a full-layer tracking method in 3D seismic data by studying feature classification technology and horizon features in 3D seismic data. 3D seismic image is the distribution of seismic waveform in 3D space, horizon tracking is the analysis of horizon in 3D seismic image. The current work of horizon tracing mainly relies on geological interpreters to calibrate horizons on two-dimensional profiles. Because the two-dimensional profiles can not reflect the overall picture of three-dimensional data, there are serious shortcomings in the efficiency and efficiency of artificial interpretation in reflecting the overall picture of geological structure. Strict three-dimensional full-horizon tracing algorithm directly deals with three-dimensional data. Seismic images have great advantages in accuracy and efficiency. In this paper, the problem of horizon tracking in 3D seismic images is studied. The main work includes the following two aspects: (1) In this paper, horizon features in 3D seismic images and feature classification technology in data mining are studied and combined with 3D seismograms. Waveform features in images, spatial distribution characteristics of horizon extremum points and general method of feature classification technology are designed based on multi-level framework. This method divides the horizon tracking process into extremum point level, horizon segment level and expert level according to the granularity of the data processed, respectively, never. The problem of horizon tracking in 3-D seismic images is studied at the same level. An algorithm of horizon segment generation based on horizon extreme point connection is designed at the extreme point level according to the spatial continuous distribution characteristics of the extreme points. Clustering algorithm designed a layer fragment merging algorithm to connect the layer fragments into large layers; in the expert level, in order to make geological interpreters adjust the results of layer tracking according to actual needs, an expert level correction algorithm was designed to make the results of layer tracking more in line with the actual geological structure. The problem of horizon tracing is dealt with at the same level, and three-dimensional automatic tracing is realized in the end. (2) In this paper, horizon features in three-dimensional seismic images are extracted from a new perspective, and a full-horizon tracing method based on matching search is proposed by studying the vertical distribution characteristics of horizon in three-dimensional seismic images. The method of horizon tracing is to track each horizon one by one, thus ignoring the relationship between different horizons in the time direction of the target area in the three-dimensional seismic image. The horizons in the time direction of the three-dimensional seismic image show layered distribution, and there are certain gaps between adjacent horizons, and the gaps between different horizons will be one. According to the amplitude characteristics of the vertical distribution of multiple horizons in the time direction and the characteristics of the interval between horizons, the method of full horizon tracing based on matching search is designed to extract the vertical distribution of horizons, and the data block generation algorithm based on matching search is designed. In this method, the extremum points in 3-D seismic images are connected to the horizon blocks in 3-D space by using the vertical distribution feature extraction algorithm and the matching search-based data block generation algorithm, and the horizon blocks are connected by the amplitude-oriented data block connection algorithm to form the seismogram. The method makes full use of the interrelation between horizons, realizes the parallel tracking of all horizons in 3D seismic images, and improves the efficiency and accuracy of horizon tracking.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：P631.4;TP391.41
本文编号：2198008
[Abstract]:With the development of artificial intelligence, feature classification technology has become an important way of data analysis, and has been widely used in data mining, artificial intelligence, pattern recognition and other fields. This paper designs a full-layer tracking method in 3D seismic data by studying feature classification technology and horizon features in 3D seismic data. 3D seismic image is the distribution of seismic waveform in 3D space, horizon tracking is the analysis of horizon in 3D seismic image. The current work of horizon tracing mainly relies on geological interpreters to calibrate horizons on two-dimensional profiles. Because the two-dimensional profiles can not reflect the overall picture of three-dimensional data, there are serious shortcomings in the efficiency and efficiency of artificial interpretation in reflecting the overall picture of geological structure. Strict three-dimensional full-horizon tracing algorithm directly deals with three-dimensional data. Seismic images have great advantages in accuracy and efficiency. In this paper, the problem of horizon tracking in 3D seismic images is studied. The main work includes the following two aspects: (1) In this paper, horizon features in 3D seismic images and feature classification technology in data mining are studied and combined with 3D seismograms. Waveform features in images, spatial distribution characteristics of horizon extremum points and general method of feature classification technology are designed based on multi-level framework. This method divides the horizon tracking process into extremum point level, horizon segment level and expert level according to the granularity of the data processed, respectively, never. The problem of horizon tracking in 3-D seismic images is studied at the same level. An algorithm of horizon segment generation based on horizon extreme point connection is designed at the extreme point level according to the spatial continuous distribution characteristics of the extreme points. Clustering algorithm designed a layer fragment merging algorithm to connect the layer fragments into large layers; in the expert level, in order to make geological interpreters adjust the results of layer tracking according to actual needs, an expert level correction algorithm was designed to make the results of layer tracking more in line with the actual geological structure. The problem of horizon tracing is dealt with at the same level, and three-dimensional automatic tracing is realized in the end. (2) In this paper, horizon features in three-dimensional seismic images are extracted from a new perspective, and a full-horizon tracing method based on matching search is proposed by studying the vertical distribution characteristics of horizon in three-dimensional seismic images. The method of horizon tracing is to track each horizon one by one, thus ignoring the relationship between different horizons in the time direction of the target area in the three-dimensional seismic image. The horizons in the time direction of the three-dimensional seismic image show layered distribution, and there are certain gaps between adjacent horizons, and the gaps between different horizons will be one. According to the amplitude characteristics of the vertical distribution of multiple horizons in the time direction and the characteristics of the interval between horizons, the method of full horizon tracing based on matching search is designed to extract the vertical distribution of horizons, and the data block generation algorithm based on matching search is designed. In this method, the extremum points in 3-D seismic images are connected to the horizon blocks in 3-D space by using the vertical distribution feature extraction algorithm and the matching search-based data block generation algorithm, and the horizon blocks are connected by the amplitude-oriented data block connection algorithm to form the seismogram. The method makes full use of the interrelation between horizons, realizes the parallel tracking of all horizons in 3D seismic images, and improves the efficiency and accuracy of horizon tracking.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：P631.4;TP391.41
【参考文献】
相关期刊论文 前1条
1 李雪峰;阎建国;赵州;姚爽;;利用相干属性剖面特征进行层位解释[J];物探化探计算技术;2011年02期
，本文编号：2198008
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