Application Research of PNN Algorithm in Coalfield Lithology Identification

Authors

  • Peixin Kou Shenmu Ningtiaota Mining Co., Ltd. of Shaanxi Coal Group, Shenmu 719300, China Author

DOI:

https://doi.org/10.63313/SD.9016

Keywords:

Coalfield, Lithology Identification, PNN

Abstract

This paper introduces the principle of the Probabilistic Neural Network (PNN) method and its algorithm training and learning process, and elaborates on the selection of lithology parameters for network identification and the establishment process of the lithology identification model. The study shows that the PNN method achieves good performance in practical application with short training and recognition time. Using artificial neural network to automatically interpret and analyze logging data can meet the timeliness of logging while drilling (LWD) and the geosteering requirements of rapid interpretation and processing.

References

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[2] Long Xihua, Zhang Bing, Gao Kai. Application of hypersphere support vector machine in logging lithology identification[J]. Mathematics in Practice and Theory, 2013, 43(18):110–115.

[3] Shi Zhijun, Yao Ningping, Ye Genfei. Construction technology and equipment for underground gas drainage boreholes in coal mines[J]. Coal Science and Technology, 2009, 37(07):1–4.

[4] Chen Gang, Yang Xue, Pan Baozhi, et al. Research status of borehole trajectory calculation and visualization[J]. World Geology, 2015, 34(03):830–841.

[5] Sun Xiaogang, Zhang Jianhua, Hou Guolian, et al. Research on condenser fault diagnosis based on probabilistic neural network[J]. Modern Electric Power, 2005(03):58–61.

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Published

2026-08-24

Issue

Section

Articles

How to Cite

Application Research of PNN Algorithm in Coalfield Lithology Identification. (2026). Sustainable Development, 1(3), 65–69. https://doi.org/10.63313/SD.9016