Fusing Minimal Unit Probability Integration Method and Optimized Quantum Annealing for Spatial Location of Coal Goafs

Accurately detecting the location of the goafs is an effective method for coordinating underground mining and surface engineering construction and realizing illegal mining supervision. Aiming at the problems of the existing solution methods of goaf spatial characteristic parameters, a spatial location identification method of coal underground goaf with fusing minimal unit probability integration method and optimized quantum annealing is proposed. Meanwhile, to study the characteristics and stability of the proposed model, the advantages and disadvantages of space movement vector data, the robust ability of the method, and the application of multi-source data are discussed in the Discussions. Finally, the achievements of this paper apply to 1414 (1) working face of Gubei Coal Mine in Huainan. The results show the model can accurately identify the location and boundary of the goaf. The research achievements have important theoretical and practical significance for solving problems of land resource reuse in mining areas lacking geological mining data, coal mine safety production, and the supervision of illegal mining.