Name
Title | ||
|---|---|---|
Predictable Condition Analysis and Prediction Method of SBAS-InSAR Coal Mining Subsidence |
Abstract | ||
|---|---|---|
The forward prediction of mining subsidence in coal mining areas is key to evaluating mining risk and improving mine management plans. At present, the conditions that ensure successful application of the time function method to predict future subsidence in coal mining areas remain poorly known, and a prediction method suitable for large-scale subsidence prediction during coal mining has not been established. Based on the characteristics of logistic model and simulation experiments, we determined its predictable conditions for coal mining subsidence prediction. We propose a small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) coal mining subsidence prediction method with a predictable dynamic range based on the predictable condition obtained. The method uses time-series subsidence data obtained by SBAS-InSAR as the fitting data. The logistic model parameters are obtained pixel-by-pixel via the Levenberg-Marquardt (LM) algorithm. The predictable range is subsequently determined based on the predictable condition. Finally, future subsidence in the predictable range is predicted. The methodology was tested in two coal mining areas in Inner Mongolia-first one is a single working face mine and another one is a parallel double working face mine. The predicted results agree well with the InSAR monitoring results. The average root-mean-square error (RMSE) of the predicted results was 0.0119 m. In addition, we used the Knothe model to conduct comparative experiments without considering predictable conditions. The results reflect the advantages of our proposed method and the necessity of predictable conditions. The new prediction method is beneficial for risk assessment and coal mining planning. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TGRS.2022.3195269 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING |
Keywords | DocType | Volume |
Predictive models, Logistics, Fitting, Coal mining, Strain, Monitoring, Data mining, Coal mining subsidence, logistic model, predictable condition, small baseline subset interferometric synthetic aperture radar (SBAS-InSAR), subsidence prediction | Journal | 60 |
ISSN | Citations | PageRank |
0196-2892 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guo Zhang | 1 | 0 | 10.48 |
| Zixing Xu | 2 | 0 | 0.34 |
| Zhenwei Chen | 3 | 5 | 1.91 |
| Shunyao Wang | 4 | 0 | 0.34 |
| Hao Cui | 5 | 2 | 3.62 |
| Yuzhi Zheng | 6 | 0 | 0.34 |