Automated analysis of volcano imagery with machine learning techniques

火山气体排放提供了对地下岩浆过程的了解；因此，它们的精确测量对火山监测和研究至关重要。事实上，气体测量对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。二氧化硫（SO2）由于其低背景大气浓度和在紫外线（UV）波长的独特吸收特征，是遥感仪器最常监测的气体。近年来，谢菲尔德大学的火山学小组开发了用于二氧化硫探测的低成本紫外线相机，目前已在全球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，精确测定二氧化硫排放率的自动检索算法仍然很麻烦：通常，需要大量的专家-用户交互才能确保以稳健的方式处理数据。本博士项目旨在通过开发一个完全自动化的处理工作流程来精确检索二氧化硫排放率，从而解决上述问题。为了实现这一目标，学生将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，他们将致力于准确地量化测量不确定度，这些不确定度经常被忽视或缺乏约束。在开发了他们的工具后，学生将分析来自两个活火山（厄瓜多尔的Reventador火山和美国的Kilauea火山）的数据集，以进一步了解这些高度活跃地点的火山作用。

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.