A comparative study of texture attributes for characterizing subsurface structures in seismic volumes
원문 초록
In this paper, we explore how to computationally characterize subsurface geological structures presented in seismic volumes using texture attributes. For this purpose, we conduct a comparative study of typical texture attributes presented in the image processing literature. We focus on spatial attributes in this study and examine them in a new application for seismic interpretation, i.e., seismic volume labeling. For this application, a data volume is automatically segmented into various structures, each assigned with its corresponding label. If the labels are assigned with reasonable accuracy, such volume labeling will help initiate an interpretation process in a more effective manner. Our investigation proves the feasibility of accomplishing this task using texture attributes. Through the study, we also identify advantages and disadvantages associated with each attribute.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Attributes Shape the Embedding Space of Face Recognition Models
Ref-Long: Benchmarking the Long-context Referencing Capability of Long-context Language Models
Emergent Semantics Beyond Token Embeddings: Transformer LMs with Frozen Visual Unicode Representations
MambaFusion: Height-Fidelity Dense Global Fusion for Multi-modal 3D Object Detection
The Trilemma of Truth in Large Language Models
Style-Aligned Image Composition for Robust Detection of Abnormal Cells in Cytopathology
토론
이 논문과 연결된 포럼 글이 아직 없습니다.