1. Xu, Z., et al.: Lithology identification: method, current situation and intelligent development trend. Geolog. Rev. 68(06), 2290–2304 (2022)
2. Cai, H., et al.: Classification of metallogenic prospect areas based on convolutional neural network model: a case study of gold polymetallic ore field in Daqiao Area, Gansu Province. Geolog. Bull. China 38(12), 1999–2009 (2019)
3. Duan, Y., Wang, Y., Sun, Q.: Application of selective ensemble learning model in lithology-porosity prediction. Sci. Technol. Eng. 20(03), 1001–1008 (2020)
4. Xu, T., et al.: Evaluation of active learning algorithms for formation lithology identification. J. Petrol. Sci. Eng. 206, 108999 (2021)
5. Arnø, M., Morten, J., Morten Aamo, O.: Real-time classification of drilled lithology from drilling data using deep learning with online calibration. In: SPE/IADC International Drilling Conference and Exhibition (2021)