Home Journals Articles Updates About
CN
image
收藏切换
Prediction Model of Large-Scale Surface Settlement in Mining Area Based on SBAS-InSAR Technology and SOA-BP Model
收藏切换
Dong ZHU1, 2, 3, Liangji XU2, 4, Xiaopeng LIU1, 2, 3, Zongyou CAO1, 2, 3
Mining Research and Development | 2025, 45(6) : 94 - 102
Less
收藏切换
Mining Research and Development | 2025, 45(6): 94-102
Prediction Model of Large-Scale Surface Settlement in Mining Area Based on SBAS-InSAR Technology and SOA-BP Model
Full
Dong ZHU1, 2, 3, Liangji XU2, 4, Xiaopeng LIU1, 2, 3, Zongyou CAO1, 2, 3
Affiliations
  • 1.School of Spatial Information and Geomatic Engineering, Anhui University of Science and Technology, Huainan, Anhui 232001, China
  • 2.State Key Laboratory for Safe Mining of Deep Coal and Environment Protection, Anhui University of Science and Technology, Huainan, Anhui 232001, China
  • 3.Key Laboratory of Aviation-aerospace-ground Cooperative Monitoring and Early Warning of Coal Mining-induced Disasters of Anhui Higher Education Institutes, Huainan, Anhui 232001, China
  • 4.Institute of Energy, Hefei Comprehensive National Science Center, Hefei, Anhui 230031, China
Published: 2025-06-25
Outline
收藏切换

In response to the problems that the traditional prediction model is easy to fall into local optimal solution, the weight and threshold of BP neural network were optimized by seagull optimization algorithm (SOA), and the SOA-BP prediction model was constructed. Based on the time series settlement values of multiple high coherence points, the model can accurately predict the surface settlement in a large-scale in the mining area. The SBAS-InSAR technology was used to process 59 SAR images of the research area, and the time series settlement values of 6 997 high coherence points in the area were extracted. Based on the monitoring data, the SOA-BP prediction model was used to predict the surface settlement in the research area, and the prediction accuracy was calculated. The results show that compared with BP model, the maximum root mean square error of SOA-BP model is reduced by 82.77%, and the maximum mean absolute error is reduced by 81.13%. With the passage of time, the proportion of samples with residual distribution within ±5 mm of SOA-BP model is increased from 79.59% to 91.91%, and the maximum residual extreme value is decreased from 16.722 4 mm to 9.421 9 mm. The prediction accuracy of SOA-BP model is high, and it can accurately predict the law of surface settlement.

SBAS-InSAR  /  Large-scale settlement  /  SOA-BP prediction model  /  Time series settlement prediction
朱董, 徐良骥, 刘潇鹏, 曹宗友. SBAS-InSAR技术融合SOA-BP模型的矿区地表大范围沉降预测模型. 矿业研究与开发, 2025 , 45 (6) : 94 -102 .
Dong ZHU, Liangji XU, Xiaopeng LIU, Zongyou CAO. Prediction Model of Large-Scale Surface Settlement in Mining Area Based on SBAS-InSAR Technology and SOA-BP Model[J]. Mining Research and Development, 2025 , 45 (6) : 94 -102 .
Appendix
Less
Year 2025 volume 45 Issue 6
48
23
Cite this Article
BibTeX
Article Info
  • Receive Date：2024-10-28
  • Online Date：2026-10-30
  • Published：2025-06-25
Article Data
Affiliations
History
  • Received：2024-10-28
Funding
Affiliations
    1.School of Spatial Information and Geomatic Engineering, Anhui University of Science and Technology, Huainan, Anhui 232001, China
    2.State Key Laboratory for Safe Mining of Deep Coal and Environment Protection, Anhui University of Science and Technology, Huainan, Anhui 232001, China
    3.Key Laboratory of Aviation-aerospace-ground Cooperative Monitoring and Early Warning of Coal Mining-induced Disasters of Anhui Higher Education Institutes, Huainan, Anhui 232001, China
    4.Institute of Energy, Hefei Comprehensive National Science Center, Hefei, Anhui 230031, China
References
Share
https://castjournals.cast.org.cn/joweb/kyyjykf/EN/
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House