Machine Learning Lithology Classification

Machine Learning Lithology Classification is a computational method that employs machine learning algorithms to automatically categorize subsurface rock types from geophysical or geological data, distinguishing itself from traditional lithology identification techniques through its reliance on data-driven pattern recognition and predictive modeling.

175

Publications

9.3K

Citations

628

Authors

224

Institutions

Publications per year

2017–2026

145

Authors

628

  1. MJ
    Matthew J. Cracknell

    University of Tasmania, Australia

    5 publications · 856 citations

    5

    h-index

  2. YK
    Yu Kang

    University of Science and Technology of China, China

    5 publications · 207 citations

    5

    h-index

  3. ZL
    Zerui Li

    University of Science and Technology of China, China

    5 publications · 207 citations

    5

    h-index

  4. MH
    Majid H. Tangestani

    Shiraz University, Iran

    4 publications · 97 citations

    4

    h-index

  5. MH
    Mustapha Hakdaoui

    University of Hassan II Casablanca, Morocco

    4 publications · 183 citations

    4

    h-index

1–5 of 628

Venues

  1. Journal of Petroleum Science and Engineering

    25 publications · 1.9K citations

    23

    h-index

  2. Geoenergy Science and Engineering

    7 publications · 215 citations

    6

    h-index

  3. IEEE Geoscience and Remote Sensing Letters

    GRSL · 6 publications · 299 citations

    6

    h-index

  4. Journal of Applied Geophysics

    6 publications · 274 citations

    6

    h-index

  5. Computers & Geosciences

    CAGEO · 6 publications · 1.4K citations

    6

    h-index

1–5