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.

火山气体排放可深入了解地下岩浆工艺；因此，它们的准确测量对于火山监测和研究至关重要。确实，气体测量值对许多地方的喷发预测至关重要，通过随后的及时撤离挽救了数千人的生命。由于其背景低的大气浓度和紫外线（UV）波长的独特吸收特征，二氧化硫（SO2）是带有遥感仪器的最常见的气体。近年来，谢菲尔德的火山学小组开发了用于SO2检测的低成本紫外摄像机，现在在全球6座火山上部署了永久仪器。因此，现在正在收集大量有价值的数据；但是，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，需要实质性的专家用户交互，以确保以可靠的方式处理数据。该博士学位项目旨在通过开发全自动的处理工作流程来解决上述问题，以确保完全自动化的处理工作流程以准确检索SO2 SO2排放率。为了实现这一目标，学生将将计算机视觉和机器学习算法融入到易于使用的工具中，该工具适用于广泛的测量条件。此外，它们将旨在准确量化测量不确定性，这些不确定性通常被忽视或受到限制不佳。在开发了工具后，学生将分析来自两个活跃火山（El Reventador，厄瓜多尔；我们的Kilauea，US）的数据集，以进一步了解这些高度活跃地点的火山。