TY - BOOK

T1 - Textural attributes based on the grey level co-occurrence matrix (GLCM)

AU - Eichkitz, Christoph Georg

N1 - no embargo

PY - 2020

Y1 - 2020

N2 - Seismic attributes are quantities extracted from seismic data that help us visually enhance or quantify features of interpretation interest. Since their introduction in 1971, hundreds of seismic attributes have been described, although only a few of them are used on a daily basis. Various authors have tried to give a classification scheme for this number of seismic attributes based on the input domains, the computation characteristics, the type of analysis window, or its field of application. The primary goal of this work is to develop a completely new approach for integrating textural attributes in seismic interpretation. The results of this newly developed attribute are compared to common seismic attributes. To understand the huge number of seismic attributes and in order to choose the right attribute and algorithm it is necessary to establish a good overview on standard seismic attribute computations. For this purpose, an alternative classification scheme is developed, which finally leads to the development of a seismic attribute database. As attribute computations often tend to be noisy another minor goal of this work is to enhance given workflows to achieve a better description of geological structures such as faults, fractures, or channels. The focus of enhancing given workflows is on delineation of fault lineaments and channel edges. For these targets geometrical attributes such as coherence are recommended attributes. In modern seismic interpretation software packages numerous algorithms are available for this purpose, but most of them are black box operations. Therefore, an enhanced workflow from data conditioning to attribute computation is established using a step-by-step approach. First, the volumetric vector dip is estimated using multiple methods and these results are compared to each other. Second, a series of filters are applied, whereas some of the filters also include the volumetric vector dip estimation. Finally, coherence is computed based on the filtered data in multiple realizations. These realizations differ in previously applied filters and in size of the analysis window. Texture analysis can generally be divided into four different methods: texture classification, texture segmentation, texture synthesis and texture shape. Among these methods texture classification is the most common method, but in seismic interpretation they still play a minor role. In this work the texture classification method of the grey level co-occurrence matrix (GLCM) is studied in detail and new workflows for using it in seismic interpretation are established. The GLCM is historically a 2D method that is commonly applied in image analysis. In seismic interpretation software packages, the GLCM is also implemented as a 2D or 2.5D method. In this work a workflow for complete 3D computation of the GLCM is established. GLCM computations can in principal be performed in specific directions individually or at once. This fact is used to develop a workflow for deriving a spatial variation factor based on multiple GLCM computations.

AB - Seismic attributes are quantities extracted from seismic data that help us visually enhance or quantify features of interpretation interest. Since their introduction in 1971, hundreds of seismic attributes have been described, although only a few of them are used on a daily basis. Various authors have tried to give a classification scheme for this number of seismic attributes based on the input domains, the computation characteristics, the type of analysis window, or its field of application. The primary goal of this work is to develop a completely new approach for integrating textural attributes in seismic interpretation. The results of this newly developed attribute are compared to common seismic attributes. To understand the huge number of seismic attributes and in order to choose the right attribute and algorithm it is necessary to establish a good overview on standard seismic attribute computations. For this purpose, an alternative classification scheme is developed, which finally leads to the development of a seismic attribute database. As attribute computations often tend to be noisy another minor goal of this work is to enhance given workflows to achieve a better description of geological structures such as faults, fractures, or channels. The focus of enhancing given workflows is on delineation of fault lineaments and channel edges. For these targets geometrical attributes such as coherence are recommended attributes. In modern seismic interpretation software packages numerous algorithms are available for this purpose, but most of them are black box operations. Therefore, an enhanced workflow from data conditioning to attribute computation is established using a step-by-step approach. First, the volumetric vector dip is estimated using multiple methods and these results are compared to each other. Second, a series of filters are applied, whereas some of the filters also include the volumetric vector dip estimation. Finally, coherence is computed based on the filtered data in multiple realizations. These realizations differ in previously applied filters and in size of the analysis window. Texture analysis can generally be divided into four different methods: texture classification, texture segmentation, texture synthesis and texture shape. Among these methods texture classification is the most common method, but in seismic interpretation they still play a minor role. In this work the texture classification method of the grey level co-occurrence matrix (GLCM) is studied in detail and new workflows for using it in seismic interpretation are established. The GLCM is historically a 2D method that is commonly applied in image analysis. In seismic interpretation software packages, the GLCM is also implemented as a 2D or 2.5D method. In this work a workflow for complete 3D computation of the GLCM is established. GLCM computations can in principal be performed in specific directions individually or at once. This fact is used to develop a workflow for deriving a spatial variation factor based on multiple GLCM computations.

KW - Seismic Attributes

KW - Textural Attributes

KW - GLCM

KW - Seismische Attribute

KW - Texturattribute

KW - GLCM

M3 - Doctoral Thesis

ER -