Generative adversarial nets in laser-induced fluorescence spectrum image recognition of mine water inrush

Water inrush occurred in mines, threatens the safety of working miners which triggers severe accidents in China. To make full use of existing distinctive hydro chemical and physical characteristics of different aquifers and different water sources, this article proposes a new water source discrimination method using laser-induced fluorescence technology and generative adversarial nets. The fluorescence spectrum from the water sample is stimulated by 405-nm lasers and improved by recursive mean filtering method to alleviate interference and auto-correlation to enhance the feature difference. Based on generative adversarial nets framework and improved spectra features, the article proposes a novel water source discrimination-generative adversarial nets model in mines to solve the problem of data limitation and improve the discrimination ability. The results show that the proposed method is an effective method to distinguish water inrush types. It provides a new idea to discriminate the sources of water inrush in mines timely and accurately.