Intelligent lithology identification technology serves as a critical component in optimizing oil and gas exploration decision-making, with its core focus lying in the rapid interpretation of geological information embedded in mud logging cuttings images. As mud logging technology advances, the volume of obtainable cuttings images has surged dramatically while the complexity and ambiguity of cuttings have intensified, creating an urgent demand for rapid and accurate lithology identification methods in field operations. This study develops an improved EfficientNet-based deep learning and transfer learning model to classify eight common types of cuttings. Compared to classical neural networks like ResNet and Inception, the EfficientNet-based transfer learning model shows better generalization and faster computation, achieving a recognition accuracy of 97.84% on the test dataset.
DONG Wenhao
,
ZHANG Huai
. Lithology identification of cuttings based on improved EfficientNet model[J]. Journal of University of Chinese Academy of Sciences, 0
: 251227
.
DOI: 10.7523/j.ucas.2025.062
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