Lithology identification technology of logging data based on deep learning model

• Published in: Earth science informatics Vol. 16; no. 3; pp. 2545 - 2557
• Main Authors: Zhang, XiaLin, Wen, JinJun, Sun, Qing, Wang, ZhenJiang, Zhang, LuYi, Liang, Peng
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2023; Springer Nature B.V
• Subjects: Artificial neural networks; Data logging; Deep learning; Earth and Environmental Science; Earth Sciences; Earth System Sciences; Effectiveness; Feature extraction; Identification; Information Systems Applications (incl.Internet); Lithology; Machine learning; Modelling; Neural networks; Ontology; Simulation and Modeling; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; Test sets; Logging data; Feature fusion; Lithology identification; Convolutional neural network
• ISSN: 1865-0473; 1865-0481
• DOI: 10.1007/s12145-023-01051-2

Traditional machine learning models have mainly been used to study geological logging data of a single sample point, ignoring the fact that logging data has a strong spatial correlation. In this study, we use convolutional neural network to extract single-point features, structural features, and multidimensional features from logging data and compare the identification effects of lithology identification models based on the three features. The identification model based on the multidimensional feature extraction achieves 77.94% correctness in the test set, which is the best result among the identification models based on CNN and the three machine learning models. Based on this feature extraction model, the feature fusion modules in U-net and feature pyramid are added respectively to build two feature fusion models to combine the features extracted from different convolutional layers and improve the effectiveness of the model. The model also introduces attention mechanism to improve the role of useful features in the model training process. The identification accuracy of the two feature fusion models, U-CNN and P-CNN, reached 79.67% and 80.02% on the test set, respectively, which verified the effectiveness of the feature fusion models for lithology identification in the study area.