Retrieval of volcanic SO2 emission rate

doi:10.3969/j.issn.1673-6141.2024.01.008

Abstract

Abstract: SO2 UV camera has been successfully applied in volcanic activity monitoring and its dynamics research due to its remarkable advantages in temporal resolution, spatial resolution, detection sensitivity, and detection accuracy. To address the issues that SO2 emission rate retrieved from UV camera images is easily affected by plume turbulence and the imges obtined are often with low contras, an optical flow algorithm incorporating neural network is proposed in this work. Firstly, based on the characteristics of atmospheric ultraviolet radiation transmission, the working mechanism of the SO2 UV camera and the inversion method of SO2 concentration image are described. Secondly, the neural network is integrated into the optical flow algorithm to achieve accurate inversion of SO2 emission rate in volcanic plume images; Finally, compared with the traditional optical flow methods, the superiority and accuracy of the proposed neural network optical flow algorithm is confirmed. The experimental results show that the neural network optical flow method can reduce the error of edge inversion from 94% to 5% even under the dual influence of low contrast of images and plume turbulence effect, significantly improving the accuracy of SO2 emission rate inversion.

Key words: SO2 camera, optical flow algorithm, neural network, emission rate, turbulence, volcanic emission

CLC Number:

P317

GUO Jianjun , LI Faquan , ZHANG Zihao , ZHANG Huiliang , LI Juan , WU Kuijun , HE Weiwei . Retrieval of volcanic SO2 emission rate[J]. Journal of Atmospheric and Environmental Optics, 2024, 19(1): 98-110.

References 24

[1]	Terry J P, Goff J, Winspear N, et al. Tonga volcanic eruption and tsunami, January 2022: Globally the most significant
	opportunity to observe an explosive and tsunamigenic submarine eruption since AD 1883 Krakatau [J]. Geoscience Letters,
20	22, 9(1): 1-11.
[2]	Thomas H, Prata F. Computer vision for improved estimates of SO2 emission rates and plume dynamics [J]. International
	Journal of Remote Sensing, 2018, 39(5): 1285-1305.
[3]	Elias T, Kern C, Horton K A, et al. Measuring SO2 emission rates at Kīlauea volcano, Hawaii, using an array of upwardlooking
	UV spectrometers, 2014―2017 [J]. Frontiers in Earth Science, 2018, 6: 214.
[4]	Xiong Y H, Luo Z J, Chen Z W, et al. Study on ultraviolet imaging remote sensing monitoring technology for SO2 gas
	emission [J]. Spectroscopy and Spectral Analysis, 2020, 40(4): 1289-1296.
	熊远辉, 罗中杰, 陈振威, 等. SO2气体排放的紫外成像遥感监测 [J]. 光谱学与光谱分析, 2020, 40(4): 1289-1296.
[5]	Mori T, Burton M. The SO2 camera: A simple, fast and cheap method for ground-based imaging of SO2 in volcanic plumes [J].
	Geophysical Research Letters, 2006, 33(24): L24804.
[6]	Xu G F, Zeng J C, Liu X X. Visual odometer based on optical flow method and feature matching [J]. Laser & Optoelectronics
	Progress, 2020, 57(20): 201501.
	许广富, 曾继超, 刘锡祥. 融合光流法和特征匹配的视觉里程计 [J]. 激光与光电子学进展, 2020, 57(20): 201501.
[7]	Shu A, Pei H D, Zhou S S, et al. Stereo measurement of position and attitude of non-cooperative spacecraft [J]. Optics and
	Precision Engineering, 2021, 29(3): 493-502.
	束安, 裴浩东, 周姗姗, 等. 非合作航天器的立体视觉位姿测量 [J]. 光学精密工程, 2021, 29(3): 493-502.
[8]	Kwan C, Budavari B. Enhancing small moving target detection performance in low-quality and long-range infrared videos
	using optical flow techniques [J]. Remote Sensing, 2020, 12(24): 4024.
[9]	Sun W, Sun M, Zhang X R, et al. Moving vehicle detection and tracking based on optical flow method and immune particle
	filter under complex transportation environments [J]. Complexity, 2020, 2020: 1-15.
[10]	Zhu J K, Dai J H. A rain-type adaptive optical flow method and its application in tropical cyclone rainfall nowcasting [J].
	Frontiers of Earth Science, 2022, 16(2): 248-264.
[11]	Teng J H, Zhang Y G, Ai Y, et al. Research and verification of gas displacement calculation model based on optical flow
	method [J]. Optics & Optoelectronic Technology, 2021, 19(5): 9-14.
	滕建厚, 张燕革, 艾勇, 等. 基于光流法的气体排量计算模型研究及验证 [J]. 光学与光电技术, 2021, 19(5): 9-14.
[12]	Hino T, Tsunomori A, Fukumoto T, et al. Vector-field dynamic X-ray (VF-DXR) using optical flow method [J]. British Journal
	of Radiology, 2022, 95(1132): 20201210.
[13]	Stebel K, Amigo A, Thomas H, et al. First estimates of fumarolic SO2 fluxes from Putana volcano, Chile, using an ultraviolet
	imaging camera [J]. Journal of Volcanology & Geothermal Research, 2015, 300: 112-120.
[14]	Peters N, Hoffmann A, Barnie T, et al. Use of motion estimation algorithms for improved flux measurements using SO2
	cameras [J]. Journal of Volcanology and Geothermal Research, 2015, 300: 58-69.
[15]	Gliß J, Stebel K, Kylling A, et al. Improved optical flow velocity analysis in SO2 camera images of volcanic plumesimplications
	for emission-rate retrievals investigated at Mt Etna, Italy and Guallatiri, Chile [J]. Atmospheric Measurement
	Techniques, 2018, 11(2): 781-801.
[16]	Zhao C Y, Wang H F, Zeng L W, et al. Effects of oncoming flow turbulence on the near wake and forces of a 3D square
	cylinder [J]. Journal of Wind Engineering and Industrial Aerodynamics, 2021, 214(8): 104674.
[17]	Varnam M, Burton M, Esse B, et al. Two independent light dilution corrections for the SO2 camera retrieve comparable
	emission rates at Masaya volcano, Nicaragua [J]. Remote Sensing, 2021, 13(5): 935.
[18]	Gliß J, Stebel K, Kylling A, et al. Pyplis-A python software toolbox for the analysis of SO2 camera images for emission rate
	retrievals from point sources [J]. Geosciences, 2017, 7(4): 134.
[19]	Chen Z S, Yang W Y, Yang J M. Video super-resolution network using detail component extraction and optical flow
	enhancement algorithm [J]. Applied Intelligence, 2022, 52(9): 10234-10246.
[20]	Deng X T, Gu Y, Li F, et al. Evaluation of teaching quality of computing method course based on improved BP neural network
[J]	Journal of Physics Conference Series, 2021, 1774(1): 012026.
[21]	Chen Q, Zhou H Y, Yu F H. Microscopic image mosaic algorithm using improved optical flow method [J]. Laser &
	Optoelectronics Progress, 2021, 58(24): 2410001.
	陈庆, 周海洋, 余飞鸿. 基于改进光流法的显微图像拼接算法研究 [J]. 激光与光电子学进展, 2021, 58(24): 2410001.
[22]	LI X X, MA J J, LIANG X F. Retrieving the cloud phase based on BP neural network [J]. Journal of Atmospheric and
	Environmental Optics, 2010, 5(4): 299-304.
	李锡祥, 麻金继, 梁晓芳. 基于BP神经网络进行云相态识别方法的研究 [J]. 大气与环境光学学报, 2010, 5(4): 299-304.
[23]	Liu M Y. Research on the Application of Multi-Factor Quantitative Stock Selection Model Based on BP Neural Network [D].
	Jilin: Changchun University of Technology, 2022.
	刘梦尧. 基于BP神经网络的量化选股模型应用研究 [D]. 吉林: 长春工业大学, 2022.
[24]	Jin Z, Qiu K J, Zhang M M. Investigation of visibility estimation based on BP neural network [J]. Journal of Atmospheric and
	Environmental Optics, 2021, 16(5): 415-423.
	金钊, 邱康俊, 张苗苗. 基于BP 神经网络的能见度估测研究 [J]. 大气与环境光学学报, 2021, 16(5): 415-423.