Interval PIP-based Adaboost-ELM for stratigraphic lithology identification in open-pit coal mining process

Drilling blast holes in open-pit coal mines is frequent, and the process data contain rich lithological information that provides a basis for bench blasting design. However, most existing studies rely on offline modeling using logging data and lack lithology identification models that can utilize drilling data to achieve parameter self-updating and adapt to changing drilling conditions. Therefore, in this paper, we propose a method for lithology identification with drilling based on time series feature learning. Interval constraints are introduced to optimize the distribution of Perceptually Important Points (PIP) in the drilling data sequence, the process of perceptually important point identification is transformed into a multi-objective optimization problem, and the strategy of priority gradient is adopted to optimize the solution. A segmented feature representation learning method is further designed, connecting adjacent perceptually important points (PIPs) to form subsequences of the original drilling data and extracting their temporal, numerical, and statistical features. This incremental learning approach enables rapid feature extraction based on drilling data collected in real time.Adaboost Extreme Learning Machine is used as the classifier, and recursive least squares method is introduced to update the model parameters. The proposed lithology recognition model can be integrated into the drilling system as a real-time control module, continuously sensing formation changes. This allows adaptive adjustment of drilling parameters, optimizing drilling efficiency and safety while effectively responding to complex strata variations. Therefore, based on the actual engineering needs of open-pit coal mines, this provides an important basis for the design of blasting parameters and the intelligent drilling of roller cone drilling rigs.