Predicting mining-induced dynamic deformations for drilling solution rock salt mine based on probability integral method and weibull temporal function

ABSTRACT

Long-term dynamic monitoring and sustainable prediction of rock salt mines is extremely important in preventing potential geological damages. However, the future prediction of the mining-induced surface displacement after the underground mining extraction cannot be achieved with single Multi-Temporal Differential Interferometric Synthetic Aperture Radar (MT-InSAR) technique, which can only reveal the deformation history during the period of Synthetic Aperture Radar (SAR) acquisition dates. In this paper, a novel dynamic model, namely Dynamic Probability Integral Method (DPIM), based on the integration of Probability Integral Method (PIM) and Weibull temporal function, is proposed in order to predict the future dynamic subsidence for the period which can not covered by the SAR acquisitions. Firstly, the time series deformation over the test salt mine are generated by Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique with use of 43 Sentinel-1 images, and then the functional relationship between the derived deformation and DPIM is established; Subsequently, the unknown parameters, including the PIM parameters and the Weibull temporal parameters are estimated following the genetic algorithm; Finally, the dynamic surface deformation occurring during the entire period of the underground extraction of the test rock salt mine is predicted. Both simulation and real deformation data over Xinhua (a typical rock salt mine in Hunan province, China) have been investigated. The scenario using the simulated data shows that the overall root-mean-square error of DPIM-predicted subsidence is ±3 mm. The real data experimental results show that the maximum predicted vertical subsidence over the rock salt mine was up to 246 mm. The root-mean-square error with comparison to the levelling measurements is estimated as ±7 mm.

Acknowledgements

The field levelling measurements was carried out by Qi Zhang and Cong Liu, which provides abundant validation data for this paper. The Sentinel-1A dataset used in this paper was provided by the European Space Agency (ESA).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [41701536, 61701047, 41674040, 41201468]; the Natural Science Foundation of Hunan Province [2017JJ3322, 2019JJ50639]; the Key Project of Education Department of Hunan Province [18A148, 16B004]; Project of Education Department of Hunan Province under Grant (No. 16C0034); and the Graduate Student Research Innovation Fund of Changsha University of Science and Technology [CX2020SS10].