Lithology identification method based on Multi-mode adaptive prediction system: Algorithms and Applications

Lithology identification is crucial in mineral and energy resource exploration as it determines geological composition and guides exploration activities, improving resource location and evaluation efficiency. The advancement of artificial intelligence technology has promoted the application of machine learning-based multi-source geophysical data fusion methods in lithology identification. However, due to the differences in geophysical exploration techniques and data types across mining areas, single machine learning methods often struggle to adapt to diverse geological environments, lacking necessary universality and robustness, which severely restricts the practical application of intelligent identification technology in actual exploration. To address these limitations, this study introduces a Multi-mode Adaptive Prediction System (MAPS) for lithology identification. MAPS innovatively integrates three learning models (supervised, semi-supervised, and unsupervised learning), and can automatically select the most suitable learning mode based on prior information such as the quantity and quality of existing labeled samples and the completeness of geological background information, achieving rapid and accurate lithology identification. We verified MAPS's performance advantages through extensive comparative experiments: in supervised learning mode, compared to Support Vector Machine (SVM) and Naive Bayes classifier, accuracy improved by 0.7% and 3.5% respectively, with F1 scores increasing by 3.4% and 4.5%; in semi-supervised learning mode, compared to semi-supervised fuzzy C-means algorithm and self-learning algorithm, accuracy and F1 scores improved by a minimum of 33.67% and 0.15 respectively; in unsupervised mode, compared to traditional fuzzy C-means and Gaussian mixture models, MAPS demonstrated superior ability to mine and construct internal data structures, showing stronger feature learning capabilities. Furthermore, MAPS has shown excellent performance in the practical application of coal seam location prediction. The coal seam locations predicted by the system are highly consistent with actual drilling results, further validating MAPS's significant application potential in practical engineering. In conclusion, MAPS significantly improves the efficiency and accuracy of lithology identification, providing reliable technical support for mineral and energy resource exploration with broad application prospects.