Evaluation method for gas pre-extraction status in coal seam boreholes based on semi-supervised learning
Current evaluation methods for single-borehole gas extraction status typically rely on gas concentration, while overlooking the diversity of coal seam gas occurrence. Supervised learning models depend on labeled sample features, but manual labeling becomes costly when the sample size is large. Unsup...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Industry and Mine Automation
2025-03-01
|
| Series: | Gong-kuang zidonghua |
| Subjects: | |
| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2025020046 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850176081958535168 |
|---|---|
| author | YAN Li WEN Hu WANG Zhenping JIN Yongfei |
| author_facet | YAN Li WEN Hu WANG Zhenping JIN Yongfei |
| author_sort | YAN Li |
| collection | DOAJ |
| description | Current evaluation methods for single-borehole gas extraction status typically rely on gas concentration, while overlooking the diversity of coal seam gas occurrence. Supervised learning models depend on labeled sample features, but manual labeling becomes costly when the sample size is large. Unsupervised learning models lack sample labeling, making qualitative evaluation infeasible. To address these issues, an evaluation method based on semi-supervised learning was proposed for the gas pre-extraction status evaluation of coal seam boreholes. A multi-dimensional evaluation system was established, incorporating eight indicators such as methane concentration, extraction negative pressure, and ambient temperature. The weighting method combining the analytic hierarchy process (AHP) and fuzzy evaluation method (FEM) was used to establish classification standards for extraction performance. Building on this, a semi-supervised learning model based on the Gaussian mixture model (GMM) and K-Means algorithm (SSGMM/SSK-Means) was developed. By integrating a small number of manually labeled samples and a large quantity of unlabeled data, the model enabled dynamic classification of single-borehole extraction status. The SSGMM demonstrated better clustering rate, while the SSK-Means achieved higher efficiency, developing a complementary "accuracy-efficiency" relationship. The application results from the 215 working face of the Huangling No. 2 Coal Mine in Shaanxi Province showed that the maximum validity clustering rate (MVCR) and adjusted rand index (ARI) of SSGMM and SSK-Means reached 82.64% and 85.83%, respectively, significantly outperforming conventional clustering methods. After optimization through a dynamic feedback mechanism, boreholes initially classified as "poor" showed an improvement of 5.26% to 5.80% in extraction efficiency, achieving a 100% remediation rate. |
| format | Article |
| id | doaj-art-da9965df989b4e66a2384e813e58d896 |
| institution | OA Journals |
| issn | 1671-251X |
| language | zho |
| publishDate | 2025-03-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj-art-da9965df989b4e66a2384e813e58d8962025-08-20T02:19:19ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2025-03-0151311312110.13272/j.issn.1671-251x.2025020046Evaluation method for gas pre-extraction status in coal seam boreholes based on semi-supervised learningYAN LiWEN HuWANG ZhenpingJIN YongfeiCurrent evaluation methods for single-borehole gas extraction status typically rely on gas concentration, while overlooking the diversity of coal seam gas occurrence. Supervised learning models depend on labeled sample features, but manual labeling becomes costly when the sample size is large. Unsupervised learning models lack sample labeling, making qualitative evaluation infeasible. To address these issues, an evaluation method based on semi-supervised learning was proposed for the gas pre-extraction status evaluation of coal seam boreholes. A multi-dimensional evaluation system was established, incorporating eight indicators such as methane concentration, extraction negative pressure, and ambient temperature. The weighting method combining the analytic hierarchy process (AHP) and fuzzy evaluation method (FEM) was used to establish classification standards for extraction performance. Building on this, a semi-supervised learning model based on the Gaussian mixture model (GMM) and K-Means algorithm (SSGMM/SSK-Means) was developed. By integrating a small number of manually labeled samples and a large quantity of unlabeled data, the model enabled dynamic classification of single-borehole extraction status. The SSGMM demonstrated better clustering rate, while the SSK-Means achieved higher efficiency, developing a complementary "accuracy-efficiency" relationship. The application results from the 215 working face of the Huangling No. 2 Coal Mine in Shaanxi Province showed that the maximum validity clustering rate (MVCR) and adjusted rand index (ARI) of SSGMM and SSK-Means reached 82.64% and 85.83%, respectively, significantly outperforming conventional clustering methods. After optimization through a dynamic feedback mechanism, boreholes initially classified as "poor" showed an improvement of 5.26% to 5.80% in extraction efficiency, achieving a 100% remediation rate.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2025020046coal seam gasextraction performance evaluationsemi-supervised learninganalytic hierarchy processfuzzy evaluation methodgaussian mixture modelk-means algorithm |
| spellingShingle | YAN Li WEN Hu WANG Zhenping JIN Yongfei Evaluation method for gas pre-extraction status in coal seam boreholes based on semi-supervised learning Gong-kuang zidonghua coal seam gas extraction performance evaluation semi-supervised learning analytic hierarchy process fuzzy evaluation method gaussian mixture model k-means algorithm |
| title | Evaluation method for gas pre-extraction status in coal seam boreholes based on semi-supervised learning |
| title_full | Evaluation method for gas pre-extraction status in coal seam boreholes based on semi-supervised learning |
| title_fullStr | Evaluation method for gas pre-extraction status in coal seam boreholes based on semi-supervised learning |
| title_full_unstemmed | Evaluation method for gas pre-extraction status in coal seam boreholes based on semi-supervised learning |
| title_short | Evaluation method for gas pre-extraction status in coal seam boreholes based on semi-supervised learning |
| title_sort | evaluation method for gas pre extraction status in coal seam boreholes based on semi supervised learning |
| topic | coal seam gas extraction performance evaluation semi-supervised learning analytic hierarchy process fuzzy evaluation method gaussian mixture model k-means algorithm |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2025020046 |
| work_keys_str_mv | AT yanli evaluationmethodforgaspreextractionstatusincoalseamboreholesbasedonsemisupervisedlearning AT wenhu evaluationmethodforgaspreextractionstatusincoalseamboreholesbasedonsemisupervisedlearning AT wangzhenping evaluationmethodforgaspreextractionstatusincoalseamboreholesbasedonsemisupervisedlearning AT jinyongfei evaluationmethodforgaspreextractionstatusincoalseamboreholesbasedonsemisupervisedlearning |