火山SO2排放速率反演

doi:10.3969/j.issn.1673-6141.2024.01.008

大气与环境光学学报 ›› 2024, Vol. 19 ›› Issue (1): 98-110.doi: 10.3969/j.issn.1673-6141.2024.01.008

火山SO2排放速率反演

郭建军 1, 李发泉 2, 张子豪 1, 张会亮 1, 李娟 3, 武魁军 1, 何微微 1*

1 烟台大学物理与电子信息学院, 山东烟台 264005; 2 中国科学院精密测量科学与技术创新研究院, 湖北武汉 430071; 3 中国科学院西安光学精密机械研究所, 陕西西安 710119

收稿日期:2022-12-26 修回日期:2023-02-07 出版日期:2023-11-28 发布日期:2024-02-06
通讯作者: E-mail: heweiwei@ytu.edu.cn E-mail:heweiwei@ytu.edu.cn
作者简介:郭建军 (1998- ), 江西吉安人, 硕士研究生, 主要从事SO2紫外相机仪器研制及反演算法方面的研究。E-mail: guojianjun@s.ytu.edu.cn
基金资助:
国家自然科学基金 (41975039, 61705253), 国家重点研发计划 (2017YFC0211900), 山东省自然科学基金 (ZR2021QD088), 山东省高等学校“青创科技支持计划” (2021KJ008)

Retrieval of volcanic SO2 emission rate

GUO Jianjun 1, LI Faquan 2, ZHANG Zihao 1, ZHANG Huiliang 1, LI Juan 3, WU Kuijun 1, HE Weiwei 1*

1 School of Physics and Electronic Information, Yantai University, Yantai 264005, China; 2 Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China; 3 Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China

Received:2022-12-26 Revised:2023-02-07 Online:2023-11-28 Published:2024-02-06
Contact: Weiwei He E-mail:heweiwei@ytu.edu.cn
Supported by:
The National Natural Science Foundation of China;National Key Research and Development Program of China;Natural Science Foundation of Shandong Province;Youth Innovation Technology Project of Higher School in Shandong Province

摘要/Abstract

摘要： SO2紫外相机因在时间分辨率、空间分辨率、探测灵敏度以及探测精度等诸多方面均具有显著优势而成功应用于火山活动监测及其动力学研究。为解决紫外相机反演SO2排放速率容易受烟羽湍流及图像低对比度影响等问题，提出了融入神经网络的光流算法。首先，基于大气紫外辐射传输特性，阐述了SO2紫外相机的工作机理及SO2浓度图像的反演方法；其次，将神经网络融入光流算法，实现了火山烟羽图像中SO2排放速率的精确反演；最后，与传统光流法进行对比，论证了神经网络光流算法的科学性及优越性与精确性。实验结果表明：在图像低对比度及烟羽湍流效应的双重影响下，神经网络光流法可以把边缘反演的误差从94%降低至5%，显著提高了SO2排放速率反演的精确性。

关键词: SO2相机, 光流法, 神经网络, 排放速率, 湍流, 火山排放

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

中图分类号:

P317

郭建军, 李发泉, 张子豪, 张会亮, 李娟, 武魁军, 何微微 . 火山SO2排放速率反演[J]. 大气与环境光学学报, 2024, 19(1): 98-110.

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.

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火山SO2排放速率反演

Retrieval of volcanic SO2 emission rate

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