Lithosphere (Aug 2022)

Improved Unet in Lithology Identification of Coal Measure Strata

  • Suzhen Shi,
  • Mingxuan Li,
  • Weixu Gao,
  • Guifei Shi,
  • Jiebin Bai,
  • Jianping Zuo

DOI
https://doi.org/10.2113/2022/4087265
Journal volume & issue
Vol. 2022, no. Special 12

Abstract

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AbstractThe lithology of underground formations can be determined using logging data, which is important for a variety of subsurface geoscience and industrial applications. Deep learning technology offers the advantage of discovering a potential relationship between input and output variables, making it a great choice for generating fast and cost-effective lithology classification models. To automatically characterize lithologies, a multiclass image segmentation problem is considered and an improved Unet as a solution is adopted. The model’s input data is two-dimensional images composed of rock feature data at different depths, and the outcome is a result of one-dimensional rock lithology classification. The algorithm’s practicality was tested using the logging data set from the Xinjing mining area in Shanxi Province, in north-central China, and an open-source data set of Canadian strata. Our model is tested against the 1D-convolutional neural network (CNN) and XGBoost algorithms using a good logging data set of the same depth and different depths for testing. The results show that the improved Unet method outperforms the 1D-CNN and XGBoost algorithms in the classification of rock lithologies. This algorithm has high application potential in the automatic interpretation of rock lithologies.