Automated identification of sedimentary structures in core images using object detection algorithms.

ABSTRACT: Manual interpretation of sedimentary structures in core-based analyses is critical for understanding subsurface geology but remains time-intensive, expert-dependent, and susceptible to bias. This study investigates the use of convolutional neural networks (CNNs) to automate structure identification in core images, focusing on siliciclastic deposits from deltaic, shoreface, fluvial, and lacustrine environments. Two object detection models-YOLOv4 and Faster R-CNN-were trained on annotated datasets comprising 15 sedimentary structure types. YOLOv4 achieved high precision (up to 95%) with faster training and shorter inference times (3.2 s/image) compared to Faster R-CNN (2.5 s/image) under consistent batch size and hardware conditions. Although Faster R-CNN reached a higher mean average precis

SUBMITTER: Abdlmutalib AJ 

PROVIDER: S-EPMC12273977 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature