Enhanced Lithology Recognition in Coal Mining: A Data-Driven Approach with DBO-BiLSTM and Wavelet Denoising

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Abstract
This study investigates the relationship between anchor cable drilling parameters and roadway roof strata properties. The goal is to enable rapid and accurate rock type identification. Field-measured drilling data were processed using data cleaning and wavelet transform noise reduction. Four recognition models were developed and compared: LSTM (Long Short-Term Memory), BiLSTM (Bidirectional Long Short-Term Memory), DBO-LSTM (Dung Beetle Optimizer), and DBO-BiLSTM. The results demonstrate a strong correlation between vibration, pressure signals and rock strength, enabling the effective differentiation of rock types. All models performed exceptionally for coal seams with distinct features, achieving 100% accuracy, precision, recall, and F1 scores. Model performance improved with increased complexity for strata with subtle differences, such as sandstone and mudstone. The DBO-BiLSTM model outperformed others, showing significant improvements in accuracy, recall, and F1 score compared to LSTM, BiLSTM, and DBO-LSTM models. Specifically, accuracy improved by up to 9%, recall by 12.48%, and F1 score by 13.06%. These findings highlight the DBO-BiLSTM model’s superior recognition capability for roof strata drilling signals. This method provides a robust technical foundation for lithology identification in Measurement While Drilling (MWD) systems. It supports more precise and efficient roadway design in complex geological conditions.
Funding Information
  • Natural Science Basic Research Program of Shaanxi Province (2021JLM-06)

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