On Time-Series InSAR by SA-SVR Algorithm: Prediction and Analysis of Mining Subsidence

Abstract

Given the increasingly serious geological disasters caused by underground mining in the Hancheng mining area in China and the existing problems with mining subsidence prediction models, this article uses the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology to process 109 Sentinel-1A images of this mining area from December 2015 to February 2020. The results show that there are three subsidences: one in Donganshang, one in south of Zhuyuan village, and one in Shandizhaizi village. In the basin, the maximum annual average subsidence rate is 300 mm/a, and the maximum cumulative subsidence is 1000 mm. The SBAS-InSAR results are compared with Global Positioning System (GPS) observation results, and the correlation coefficient is 74%. Finally, a simulated annealing (SA) algorithm is used to estimate the optimal parameters of a support vector regression (SVR) prediction model, which is applied for mining subsidence prediction. The prediction results are compared with the results of SVR and the GM (1, 1). The minimum value of the coefficient of determination for prediction with SA-SVR model is 0.57, which is significantly better than that those of the other two prediction methods. The results indicate that the proposed prediction model offers high subsidence prediction accuracy and fully meets the requirements of engineering applications.

Residue Classification-Based Robust Phase Unwrapping in High-Noise Region

“…(3) Connect the residues to the boundary when the preset maximum radius is exceeded. Connect all the residues as described above (4) The flood-fill integral is used to obtain the unwrapped phase (5) The unwrapped phase is obtained by the above steps, and then the estimated phase of the study area can be obtained by adding it to the reference phase of the starting pixel 2.2. Background of the MCF Method.…”

“…Therefore, the requirements for early warning of natural disasters and emergency responses after disasters are more stringent. Synthetic aperture radar interferometry (InSAR) [1][2][3] is a powerful and well-established remote-sensing technique used to measure many important geophysical parameters, e.g., surface height of topography [4] or ground deformation [5][6][7][8], which plays an important role in early warning of natural disasters and emergency responses after disasters. Phase unwrapping (PU) is one of the key steps of InSAR [9].…”

Research on time series InSAR monitoring method for multiple types of surface deformation in mining area

“…The new time-series InSAR combined with the ascending and descending orbit data to analyze the surface deformation after mining located at the French and German borders, and the high-precision subsidence and uplift data identified more deformation areas and compensated for the insufficient number of level survey datum points (Samsonov, d'Oreye, and Smets 2013). The surface deformation values calculated by SBAS-InSAR were validated using GPS data at a mine site in Seoul, and the support vector regression algorithm used the validated surface deformation values for prediction to obtain high accuracy surface deformation predictions (Shi et al 2020). Surface subsidence diagram and monitoring of villages near mining areas in India using the modified PS-InSAR has good practical results for monitoring the slow settlement of villages with high accuracy (Kumar et al 2020).…”

Test Strategy Optimization Based on Soft Sensing and Ensemble Belief Measurement

“…The maintainability estimation model provides real-time operation condition diagnosis and realizes system health management along with testability design. In general, existing maintainability design approaches can be divided into physics-of-failure (PoF) approaches [ 19 , 20 , 21 , 22 ] and data-driven (DD) approaches [ 23 , 24 , 25 ]. PoF approaches use rules from physics or chemical dynamics to estimate electric system failure conditions [ 26 ].…”