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September 10, 2025Deep Underground Science and EngineeringOpen Access

Real‐time lithology identification while drilling based on drill cuttings image analysis with ensemble learning

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Authors

KLKun LiTRTing RenNYNingping Yao

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Overview

Ensemble learning enhances lithology identification accuracy to 97.42% in underground drill cuttings analysis, suggesting improved hazard management.

Key Points

  • The proposed method enables accurate lithology identification during drilling operations, enhancing safety measures.
  • In underground trials, the system identified formation lithology with an accuracy of 97.42% and processed images in under 0.11 seconds.
  • By integrating a cuttings sampling system with rigorous control and standards, automation improves overall drilling efficiency.
  • This innovative approach supports unmanned drilling technology development and real-time hazard risk management.

Cite This Study

Li et al. (2025) studied this question.

synapsesocial.com/papers/68c1c22d54b1d3bfb60ef7ebhttps://doi.org/10.1002/dug2.70047
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Also Consider

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  1. 1Multimodal Machine Learning–Driven Automated Lithology Analysis Using HyLogger Data of Drill Cuttings2026
  2. 2Real-Time Lithology Prediction at the Bit Using Machine Learning2024 · 8 citations
  3. 3Multimodal Deep Learning for Comprehensive Rock Lithology Characterization: A Novel Approach Combining Images and Formation Data2025 · 1 citations
  4. 4Intelligent Lithology Identification of Deep Coal Seam Driven by Drilling Parameters2025
  5. 5Machine Learning–Based Real‐Time Lithology Identification From Drilling Data in the Ca Tam Offshore Field2026 · 1 citations