A comparative study of texture attributes for characterizing subsurface structures in seismic volumes

Abstract

A comparative study of texture attributes for characterizing subsurface structures in seismic volumes,

Subsurface Structure Analysis Using Computational Interpretation and Learning: A Visual Signal Processing Perspective

“…Recently, some comparative studies were conducted to examine various texture attributes in the context of seismic volume labeling. In one study, the focus was on a group of spatial attributes from the family of local descriptors, including the local binary pattern (LBP), a few of its typical variants, and the local radius index (LRI) [52]. These attributes can represent texture patterns with robustness and computational efficiency.…”

Joint Learning for Spatial Context-based Seismic Inversion of Multiple Datasets for Improved Generalizability and Robustness

“…The success of deep learning in computer vision and natural language processing domains has of late inspired geophysicists to replicate these successes in the field of seismic interpretation. Machine learning has been used to solve problems in salt body delineation (Di et al, 2018b;Amin et al, 2017;Shafiq et al, 2017;Wang et al, 2015), fault detection (Di and AlRegib, 2019;Di et al, 2019a,b) , facies classification (Alaudah et al, 2019b,c), seismic attribute analysis (Long et al, 2018;Di et al, 2018a;, and structural similarity based seismic image retrieval and segmentation (Alaudah et al, 2019a).…”

Texture classification using block intensity and gradient difference (BIGD) descriptor

“…Texture can be broadly defined as a type of visual features that characterize the surface of an object or a material. Distinctive and robust representation of texture is the key for various multimedia applications such as image representation [1], texture retrieval [2], face recognition [3], image quality assessment [4,5], image/texture segmentation [6], dynamic texture/scene recognition [7,3], texture/color style transfer [8], and seismic interpretation [9]. Texture descriptors [10,11,12,13,14,15,16], which are robust against rotations and translations of images, are able to provide discriminative features.…”