Automated analysis of volcano imagery with machine learning techniques

利用机器学习技术自动分析火山图像

• 批准号: 2908452
• 金额: --
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2908452
• 关键词: Automated; analysis; volcano; imagery; machine

Volcanic gas emissions provide an insight into subsurface magmatic processes; their accurate measurement is therefore critical to volcano monitoring and research. Indeed, gas measurements have been pivotal to eruption forecasting in a number of places, saving many thousands of lives through subsequent timely evacuations. Due to its low background atmospheric concentration and distinctive absorption features at ultraviolet (UV) wavelengths, sulphur dioxide (SO2) is the most commonly monitored gas with remote sensing instruments. In recent years, the volcanology group at Sheffield have developed low-cost UV cameras for SO2 detection, with permanent instruments now deployed on 6 volcanoes worldwide. Vast amounts of valuable data are therefore now being collected; however, automation of retrieval algorithms for the accurate determination of SO2 emission rates remains troublesome: typically, substantial expert-user interaction is required to ensure the data are processed in a robust manner.This PhD project aims to address the above issue by developing a fully automated processing workflow for accurate retrieval of SO2 emission rates. To achieve this goal, the student will incorporate computer vision and machine learning algorithms into an easy-to-use tool that is robust to a wide range of measurement conditions. Furthermore, they will aim to accurately quantify measurement uncertainties, which are often neglected or poorly constrained. Having developed their tool, the student will analyse datasets from two active volcanoes (El Reventador, Ecuador; Kilauea, US) to further our understanding of volcanism at these highly- active sites.

火山气体排放可以深入了解地下岩浆过程；因此，它们的准确测量对于火山监测和研究至关重要。事实上，气体测量对于许多地方的喷发预测至关重要，通过随后的及时疏散挽救了数千人的生命。由于其背景大气浓度较低且在紫外线 (UV) 波长下具有独特的吸收特征，二氧化硫 (SO2) 是遥感仪器最常监测的气体。近年来，谢菲尔德的火山学小组开发了用于 SO2 检测的低成本紫外线相机，目前在全球 6 座火山上部署了永久性仪器。因此，现在正在收集大量有价值的数据；然而，用于准确确定 SO2 排放率的检索算法的自动化仍然很麻烦：通常需要大量的专家与用户交互来确保以稳健的方式处理数据。该博士项目旨在通过开发一个完整的解决方案来解决上述问题。自动化处理工作流程，用于准确检索 SO2 排放率。为了实现这一目标，学生将把计算机视觉和机器学习算法整合到一个易于使用的工具中，该工具对各种测量条件都具有鲁棒性。此外，他们的目标是准确量化测量的不确定性，而这些不确定性常常被忽视或约束不力。开发出工具后，学生将分析两座活火山（厄瓜多尔雷文塔多；美国基拉韦厄火山）的数据集，以进一步了解这些高度活跃地点的火山活动。