Automatic Fracture Identification from Logging Images Using the Tscode-Simam-Yolov5 Algorithm
GEOENERGY SCIENCE AND ENGINEERING(2024)
Jilin Univ
Abstract
The interpretation of fractured reservoirs presents significant challenges within the global petroleum industry. Given the complexity and irregularity of subsurface fractures, the accurate and rapid identification of fractures using logging image data remains a critical area of research. This paper enhances feature representation by incorporating a three-dimensional attention mechanism into the existing target detection network architecture. Additionally, it addresses the spatial misalignment in the original network and the issue of shared features between the classification and regression subtasks, which address different problems. This is achieved by introducing a novel decoupled head structure that improves performance. The TSCODE-SIMAM-YOLOv5 model effectively overcomes the limitations associated with manual detection’s low efficiency and the poor accuracy of previous automated methods. Our results show that the TSCODE-SIMAM-YOLOv5 network model achieves a detection accuracy of 91.8%, with values of mAP reaching up to 89.7%, and a detection speed of 27.682 frames per second, significantly surpassing traditional manual methods. These test outcomes prove the superior detection accuracy of the TSCODE-SIMAM-YOLOv5 network model, providing a robust deep learning framework for directional fracture detection in logging. This work also offers a reliable method for the identification of fractures in logging images.
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Key words
Logging images,Fracture identification,TSCODE-SIMAM-YOLOv5 target detection model,SIMAM attention mechanism,TSCODE decoupled head structure,TSCODE decoupled head structure
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