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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.

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Bayesian methods are widely used in the GWAS meta-analysis. But the considerable consumption in both computing time and memory space poses great challenges for large-scale meta-analyses. In this research, we propose an algorithm named SMetABF to rapidly obtain the optimal ABF in the GWAS meta-analysis, where shotgun stochastic search (SSS) is introduced to improve the Bayesian GWAS meta-analysis framework, MetABF. Simulation studies confirm that SMetABF performs well in both speed and accuracy, compared to exhaustive methods and MCMC. SMetABF is applied to real GWAS datasets to find several essential loci related to Parkinson’s disease (PD) and the results support the underlying relationship between PD and other autoimmune disorders. Developed as an R package and a web tool, SMetABF will become a useful tool to integrate different studies and identify more variants associated with complex traits.