Lithology recognition of cuttings based on transfer learning

doi:10.7523/j.ucas.2022.026

Abstract

Abstract: Cutting logging plays an important role in the fields of geological structure research and oil and gas exploration. With the improvement of logging technology, the number of cutting logging pictures has increased sharply, and traditional manual identification of cuttings is far from meeting actual work requirements. Convolutional neural networks based on transfer learning are known for their high efficiency in image classification and recognition. This paper focuses on 18 common kinds of cuttings as the research object. Based on the VGG-16 model trained on the ImageNet image data set, a migration learning model conforming to the characteristics of the cuttings image data set is established and applied to the actual lithology recognition. This paper selects 5 877 cutting logging pictures, and the training set, validation set, and test set were randomly divided in the ratio of 3:1:1. The lithology recognition accuracies of the training set, validation set, and test set reach 98.6%, 87.2%, and 87.2%, respectively. The test results on learning show that this method is very effective in lithology classification and recognition.

Key words: lithology recognition, convolution neural network, transfer learning, VGG-16

CLC Number:

P588

DONG Wenhao, ZHANG Huai. Lithology recognition of cuttings based on transfer learning[J]. Journal of University of Chinese Academy of Sciences, 2023, 40(6): 743-750.

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