Linking optical characteristics of small particles (50 - 500 micrometer) with their sinking velocities in the mesopelagic environment

在全球范围内，海洋清除的二氧化碳比释放到大气中的二氧化碳更多，将部分过剩的碳储存在深海中。下沉的颗粒，无论是活的浮游生物还是非生物的碎屑，都是这种碳流量的主要贡献者。现代摄像系统和图像分析技术使对这些颗粒进行计数、测量和分类成为可能，从而为海洋学家提供了一种工具，可以在空间和时间上以高分辨率估计向深海转移的碳。不幸的是，只知道颗粒的大小来估计这些颗粒下沉的速度是不够的，因为形状和颗粒密度也会影响下沉速度。该项目使用安装在粒子捕捉器上的摄像头检测单个粒子沉入深海时的速度。对于这些颗粒中的每一个，确定分类标准，如大小、形状因素、光密度，对于浮游生物，确定分类识别，并与它们各自的下沉速度进行比较。这些信息用于通过调查水柱中的颗粒来计算总的下沉速度，从而从相机图像中得出更可靠的碳通量估计。该项目支持水下成像系统的技术开发，研究生和本科生教育，以及通过公共推广计划为中学生和他们的导师提供科学素养倡议。海洋颗粒物清单的船上和自动运载工具调查在高时间和空间分辨率估计碳通量方面有着巨大的前景。然而，尽管较大颗粒的下沉速度相对较好地受到限制，例如有孔虫贝壳和盐类、磷虾和较大的桡足类的粪便颗粒，但较小颗粒大小的水池(50-500微米)的动力学仍然更加难以捉摸。尽管它们的大小和假定的缓慢下沉速度，小颗粒大量出现在中大洋表层和沉积物捕集物质中。它们在中上层的丰度可能是深度混合的结果，也可能是较大颗粒被消化后的残留物，具有较高超额密度的颗粒，如成岩尘埃颗粒、原生生物排放的小颗粒、原生孢子，或由于通量供给器的活动而在深处破碎的结果，以及其他可能性。这个项目通过将单独分辨的光学特征与下沉速度联系起来，解决了一些关于小粒子池的悬而未决的问题。根据斯托克斯定律，过剩密度是根据大小和下沉速度估算出来的，然后通过光学测量将其分配给粒子。水平安装的摄像系统记录了附着在大洋通量计划系泊阵列上的沉积物捕捉器中颗粒的下沉速度、大小和特征。记录的颗粒使用1)经典图像分析来表征，考虑了各种形状因素；2)单个颗粒的不透明度；以及3)使用卷积神经网络的监督和非监督深度学习的图像分类。另一个相同的相机测量水柱中同一站点和同一时间的颗粒物库存，以整合现有和未受干扰的颗粒物池的通量估计。尼斯金瓶样本和颗粒的显微镜检查增强了对图像数据的解释。该项目的成果有助于实现基于光学颗粒测量的碳通量模型获得更高预测能力的总体目标。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Globally, the ocean removes more carbon dioxide than it releases into the atmosphere storing a portion of the excess carbon in the deep sea. Sinking particles, both living plankton and non-living detritus, are major contributors to this flux of carbon. Modern camera systems and image analysis techniques have made it possible to count, measure and classify these particles, thus providing oceanographers with a tool to estimate carbon transfers to the deep ocean at high resolution in space and time. Unfortunately, it is not enough to know the sizes of particles to estimate how fast these particles sink because shape and particle density also influence the sinking velocity. This project examines the velocities of individual particles as they sink into the deep ocean using a camera attached to a particle trap. For each of these particles, classification criteria, such as size, shape factors, optical density, and in the case of plankton, taxonomic identification, is determined and compared to their individual sinking velocities. This information serves to calculate overall sinking velocities from surveys of particles in the water column and thereby produce more reliable estimates of carbon fluxes from camera images. This project supports technology development in underwater imaging systems, graduate and undergraduate student education, and science literacy initiatives for middle-school students and their mentors through public outreach programs.Shipboard and autonomous vehicle surveys of oceanic particle inventories hold great promise for estimating carbon fluxes at high temporal and spatial resolutions. However, while the sinking velocities of larger particles such as foraminifera shells and fecal pellets of salps, krill, and larger copepods are relatively well constrained, the dynamics of the smaller particle size pool (50–500 micrometers) remain more elusive. Despite their size and presumed slow sinking velocities, small particles occur in large numbers in the mesopelagic layer and sediment-trap material. Their abundance in the mesopelagic could be the result of deep mixing, or small particles could be remnants of digested larger particles, particles with a high excess density such as lithogenic dust particles, minipellets egested by protists, protist spores, or the result of fragmentation at depth due to the activity of flux feeders, among other possibilities. This project addresses some unanswered questions about the small particle pool by linking individually-resolved optical features with sinking velocities. Using Stokes’ law, excess density is being estimated from size and sinking velocity and then assigned to particles from optical surveys. A horizontally installed camera system records sinking velocities, sizes, and features of particles in a sediment trap attached to the Oceanic Flux Program mooring array. The recorded particles are being characterized using 1) classic image analysis, taking various shape factors into account; 2) opacity of individual particles; and 3) image classification with supervised and unsupervised deep learning using convolutional neural networks. A second identical camera surveys the particle inventory at the same station and time in the water column to integrate flux estimates over the existing and undisturbed particle pool. Niskin bottle samples and microscopic examination of particles augment the interpretation of image data. The results of this project contribute to the overarching goal of achieving higher predictive power for carbon flux models based on optical particle surveys.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.