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

Abstract

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.

Image Recognition of Mine Water Inrush Based on Bilinear Convolutional Neural Network with Few-Shot Learning

“…Zhang et al 15 summarized the image recognition of coal and rock and pointed out the existing problems. Li et al 16 proposed a new mine water source discrimination method based on a generative adversarial network using the fluorescence spectrum of water samples. Alfarzaeai et al 17 used a convolutional neural network (CNN) and thermal imaging to identify coal gangue.…”

Image recognition of mine water inrush based on bilinear convolutional neural network with few-shot learning

“…Zhang et al (Zhang and Zhang, 2018) summarized the image recognition of coal and rock and pointed out the existing problems. Jing et al (Li and Yong et al, 2019) proposed a new mine water source discrimination method based on a generative adversarial network using the uorescence spectrum of water samples. Alfarzaeai et al (Alfarzaeai and Niu et al, 2020) used a convolutional neural network (CNN) and thermal imaging to identify coal gangue.…”

Interval uncertainty analysis of a confined aquifer

“…Water damage is a key problem during mining 1 – 3 . The large-well method is commonly used for predicting mine water inflow 4 – 6 , however, the accuracy of the results are subject to the constraints of hydrogeological conditions 7 – 9 . For example, the calculation process of the large well method is simple, but for areas with complex hydrogeological conditions, the calculation accuracy needs to be further improved.…”