关键词: 开采沉陷, D-InSAR, 遗传算法, 概率积分法, 下沉盆地

Abstract: The vegetation is covered on the surface in mining area on a large scale,mining subsidence speed is fast and mining subsidence quantity is large,which generate the geological disasters is more serious than general surface subsidence,besides that,it is easy to make the two SAR images loss for coherence and phase unwrapping errors.In order to overcome the limitations of low coherence of SAR images in mining area and the missing of central phase of subsidence basin,combing with the differential interferometric synthetic aperture radar (D-InSAR) and the probability integral method based on genetic algorithm,a mining subsidence basin monitoring and predicting method is proposed.Some D-InSAR monitoring values that are obtained at the edge of the subsidence basin in mining area with the characteristics of high coherence and marked subsidence and a few monitoring values that are obtained near of the maximum subsidence points and inflection points in the mining basin in mining area are used to conducted the experiments of II3720 working face of a coal mine.To be specific,firstly,the probability integral model is used to conduct inversion the parameters of probability integral prediction method and they are optimized by genetic algorithm in a few times;then,the optimized parameters of probability integral method are adopted to predict the subsidence basin in mining area.The experimental result show that the parameters and subsidence values of the subsidence basin in mining area obtained by the method proposed in this paper are consistent to the practical measured data basically,it is contributed to overcome the deficiencies of the low precise of the SAR images prediction results with poor interference effect，the subsidence basin in mining area can be predicted effectively based on a few measured data,the method proposed in this paper can provide reference for improving the precise of the mining subsidence monitoring and predicting.

Key words: Mining subsidence, D-InSAR,Genetic algorithm, Probability integral method, Subsidence basin