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

Jing Li,Li Zhao,Hongmei Ge,Yong Wang,Yong Yang

doi:10.1177/1550147719884894

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.

Highlights

Water inrush in mines generally occurs when a large volume of water unexpectedly bursts into underground working areas in a short period of time, which jeopardizes the safe production of coal mine, causes casualties, and engenders severe economic losses
Many research works have verified that different aquifers and different water sources have distinctive hydro chemical and physical characteristics.[1,2,3,4]
Particular, convolutional neural network (CNN) is specific to image data, which automatically learns from fluorescence spectra of laser-induced fluorescence (LIF) technology and avoids feature extraction and feature selection by human interference

Summary

Introduction

Water inrush in mines generally occurs when a large volume of water unexpectedly bursts into underground working areas in a short period of time, which jeopardizes the safe production of coal mine, causes casualties, and engenders severe economic losses. Related methods include the support vector machine (SVM),[9] clustering analysis,[10] extreme learning machine (ELM),[3] back propagating neural network,[11] and so on These methods are applied to discriminate the type of water source in mine. As deep neural network grows deeper and stronger, deep concept has been successfully applied to discriminate the type of water source in mines and predict the disaster of water inrush.[1] Particular, convolutional neural network (CNN) is specific to image data, which automatically learns from fluorescence spectra of LIF technology and avoids feature extraction and feature selection by human interference. Available data are extremely limited in actual industrial production This thorny phenomenon is the bottleneck problem to construct mine water source type discrimination model using CNN. The GAN provides a new idea to solve the shortcomings of validly data for discrimination of mine water inrush sources

Methods

Conclusion