Deep subsurface exploration is important for mining, oil and gas industries,
as well as in the assessment of geological units for the disposal of chemical
or nuclear waste, or the viability of geothermal energy systems. Typically,
detailed examinations of subsurface formations or units are performed on
cuttings or core materials extracted during drilling campaigns, as well as on
geophysical borehole data, which provide detailed information about the
petrophysical properties of the rocks. Depending on the volume of rock samples
and the analytical program, the laboratory analysis and diagnostics can be very
time-consuming. This study investigates the potential of utilizing machine
learning, specifically convolutional neural networks (CNN), to assess the
lithology and mineral content solely from analysis of drill core images, aiming
to support and expedite the subsurface geological exploration. The paper
outlines a comprehensive methodology, encompassing data preprocessing, machine
learning methods, and transfer learning techniques. The outcome reveals a
remarkable 96.7% accuracy in the classification of drill core segments into
distinct formation classes. Furthermore, a CNN model was trained for the
evaluation of mineral content using a learning data set from multidimensional
log analysis data (silicate, total clay, carbonate). When benchmarked against
laboratory XRD measurements on samples from the cores, both the advanced
multidimensional log analysis model and the neural network approach developed
here provide equally good performance. This work demonstrates that deep
learning and particularly transfer learning can support extracting
petrophysical properties, including mineral content and formation
classification, from drill core images, thus offering a road map for enhancing
model performance and data set quality in image-based analysis of drill cores.

利用机器学习，特别是卷积神经网络 (CNN)，从钻井岩心图像分析中评估岩性和矿物含量的潜力，并提供了全面的方法论，展示了 96.7% 的岩心分割分类准确率，并验证了该方法在矿物含量评估上与实验室检测结果相当。