Prediction of surface deformation time series in closed mines based on LSTM and optimization algorithms
Underground coal mining impacts surface deformation not only during mining operations but also after mine closures, potentially causing prolonged subsidence or uplift. Long-term predictions of surface deformation in closed mining areas remain challenging, as traditional statistical methods have limi...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2025-06-01
|
| Series: | Open Geosciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/geo-2025-0811 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850124649259597824 |
|---|---|
| author | Hu Caixiong Zhang Lili Li Haoran Zhang Yaowen Yao Yunsheng |
| author_facet | Hu Caixiong Zhang Lili Li Haoran Zhang Yaowen Yao Yunsheng |
| author_sort | Hu Caixiong |
| collection | DOAJ |
| description | Underground coal mining impacts surface deformation not only during mining operations but also after mine closures, potentially causing prolonged subsidence or uplift. Long-term predictions of surface deformation in closed mining areas remain challenging, as traditional statistical methods have limited capability in modeling complex spatiotemporal deformation patterns. To address this, this study utilized the small base line subsets interferometric synthetic aperture radar technique to derive deformation results in the line-of-sight direction for the study area. A long short-term memory (LSTM) neural network combined with the gray wolf optimizer (GWO) algorithm was introduced to improve prediction accuracy. Experimental results showed that the LSTM model achieved a correlation coefficient of 0.93 with the observed data, while the GWO–LSTM model further enhanced prediction accuracy with a correlation coefficient of 0.99, demonstrating its potential for complex deformation prediction. The findings provide a scientific basis for surface deformation prediction and risk assessment in closed mining areas and have practical significance for disaster warning and decision-making in the region. As synthetic aperture radar data availability continue to grow, this method holds promise for broader applications in other geological environments. |
| format | Article |
| id | doaj-art-1ad0c5618e9f4a2aa2e610e2c76b929d |
| institution | OA Journals |
| issn | 2391-5447 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Geosciences |
| spelling | doaj-art-1ad0c5618e9f4a2aa2e610e2c76b929d2025-08-20T02:34:16ZengDe GruyterOpen Geosciences2391-54472025-06-0117117054110.1515/geo-2025-0811Prediction of surface deformation time series in closed mines based on LSTM and optimization algorithmsHu Caixiong0Zhang Lili1Li Haoran2Zhang Yaowen3Yao Yunsheng4Institute of Disaster Prevention, Sanhe, 065201, Hebei, ChinaInstitute of Disaster Prevention, Sanhe, 065201, Hebei, ChinaInstitute of Disaster Prevention, Sanhe, 065201, Hebei, ChinaInstitute of Disaster Prevention, Sanhe, 065201, Hebei, ChinaInstitute of Disaster Prevention, Sanhe, 065201, Hebei, ChinaUnderground coal mining impacts surface deformation not only during mining operations but also after mine closures, potentially causing prolonged subsidence or uplift. Long-term predictions of surface deformation in closed mining areas remain challenging, as traditional statistical methods have limited capability in modeling complex spatiotemporal deformation patterns. To address this, this study utilized the small base line subsets interferometric synthetic aperture radar technique to derive deformation results in the line-of-sight direction for the study area. A long short-term memory (LSTM) neural network combined with the gray wolf optimizer (GWO) algorithm was introduced to improve prediction accuracy. Experimental results showed that the LSTM model achieved a correlation coefficient of 0.93 with the observed data, while the GWO–LSTM model further enhanced prediction accuracy with a correlation coefficient of 0.99, demonstrating its potential for complex deformation prediction. The findings provide a scientific basis for surface deformation prediction and risk assessment in closed mining areas and have practical significance for disaster warning and decision-making in the region. As synthetic aperture radar data availability continue to grow, this method holds promise for broader applications in other geological environments.https://doi.org/10.1515/geo-2025-0811sbas-insardeep learningclosed minelstmprediction |
| spellingShingle | Hu Caixiong Zhang Lili Li Haoran Zhang Yaowen Yao Yunsheng Prediction of surface deformation time series in closed mines based on LSTM and optimization algorithms Open Geosciences sbas-insar deep learning closed mine lstm prediction |
| title | Prediction of surface deformation time series in closed mines based on LSTM and optimization algorithms |
| title_full | Prediction of surface deformation time series in closed mines based on LSTM and optimization algorithms |
| title_fullStr | Prediction of surface deformation time series in closed mines based on LSTM and optimization algorithms |
| title_full_unstemmed | Prediction of surface deformation time series in closed mines based on LSTM and optimization algorithms |
| title_short | Prediction of surface deformation time series in closed mines based on LSTM and optimization algorithms |
| title_sort | prediction of surface deformation time series in closed mines based on lstm and optimization algorithms |
| topic | sbas-insar deep learning closed mine lstm prediction |
| url | https://doi.org/10.1515/geo-2025-0811 |
| work_keys_str_mv | AT hucaixiong predictionofsurfacedeformationtimeseriesinclosedminesbasedonlstmandoptimizationalgorithms AT zhanglili predictionofsurfacedeformationtimeseriesinclosedminesbasedonlstmandoptimizationalgorithms AT lihaoran predictionofsurfacedeformationtimeseriesinclosedminesbasedonlstmandoptimizationalgorithms AT zhangyaowen predictionofsurfacedeformationtimeseriesinclosedminesbasedonlstmandoptimizationalgorithms AT yaoyunsheng predictionofsurfacedeformationtimeseriesinclosedminesbasedonlstmandoptimizationalgorithms |