SGER: Measuring Black Smoker Flow Rates Using Image Correlation Velocimetry

摘要在此资助下，pi将开发一种基于图像分析的测速技术，以获得通过黑烟的流量的时间序列测量。这将涉及开发一套计算算法来分析黑吸烟者的视频图像，这将提供通过这些通风口的流量估计。这些算法将应用于以前收集的Juan de Fuca Ridge主要endeavor领域的黑吸烟者视频，以便开始描述黑吸烟者流量的变化。具体地说，pi将使用光谱技术寻找流量中潮汐变化的证据，并将结果与潮汐负荷相关的流量变化的模型预测进行比较。然后，pi将利用现有的地震数据寻找与地震活动有关的流动扰动的证据。本研究的广泛影响源于大洋中脊热液系统科学的跨学科性质。该项目还将通过完成开发下一代海底仪器所需的初步工作来加强研究基础设施。这项研究还将继续对华盛顿大学的一名研究生进行培训和教育。

AbstractUnder this funding, the PIs will develop an image analysis-based velocimetry technique to obtain time-series measurements of flow through black smokers. This will involve development a set of computational algorithms to analyze black smoker video imagery which will provide estimates of flow through these vents. These algorithms will be applied to previously collected video of black smokers in the Main Endeavour field of the Juan de Fuca Ridge in order to begin characterizing black smoker flow variability. Specifically, the PIs will look for evidence of tidal variability in the flow using spectral techniques, and will compare the results to model predictions of flow variability associated with tidal loading. The PIs will then look for evidence of flow perturbations related to seismic activity using available earthquake data. The broader impacts of this study derive from the interdisciplinary nature of mid-ocean ridge hydrothermal system science. This project will also enhance the research infrastructure by completing preliminary work necessary for the development of a next-generation seafloor instrument. This study will also continue the training and education of a University of Washington graduate student.