Application of deep learning in quantitative analysis of the infrared spectrum of logging gas

摘要

Infrared spectrum analysis technology can perform fast and nondestructive detection of gas and has been widely used in many fields. This work studies the quantitative analysis technology of the infrared spectrum based on deep learning. The experimental results show that the quantitative analysis model of logging gas established here can reach 100% recognition accuracy for elemental gas; further, the accuracy rate of spectral of mixed gas recognition reached 98%, indicating that the infrared spectrum logging gas detection model based on deep learning can quickly and accurately perform quantitative analysis of logging gas. (C) 2020 Optical Society of America