Applications of an Automated C-Axis Analyzer

摘要本提案要求支持冰织物分析自动化系统的应用。该系统将最先进的数字成像技术与计算机控制的偏振光片相结合，可以快速可靠地确定冰薄片上所有颗粒的方向。该方法依赖于一个原理类似于计算机辅助断层扫描（CAT）经常用于医学成像。一个包含c轴的平面被确定为冰样品的几个方向中的每一个。用一种简单的算法求出与测量平面集一致的c轴唯一方向。该系统将与国家冰芯实验室（NICL）目前的冰粒成像和尺寸测量能力相结合，提供一个能够完全表征冰薄切片结构的设备。这个设施将对任何想要分析冰结构的人开放。支持者将把这一文书应用于与冰流和冰结构有关的具体问题。该项目将利用来自GISP2的大量样品，这些样品已准备好，但由于时间限制而未进行分析。从这些数据中，我们将研究冰流与织物柔软度指数之间的关系，并利用新的分析技术来识别冰中的活动过程（如颗粒形成）。这些过程的存在对流动规律指数和冰对变形不稳定性的敏感性具有重要意义。这些研究应该为更精确的冰流模型指明道路，也为分析冰芯气候数据提供更好的诊断。

AbstractThis proposal requests support for applications of an automated system for ice fabric analysis. The system combines state-of-the-art digital imaging with computer-controlled polarizers to allow the orientation of all the grains on an ice thin section to be determined quickly and reliably. The method relies on a principle similar to computer aided tomography (CAT) used frequently for medical imaging. A plane containing the c-axis is determined for each of several orientations of the ice sample. A straightforward algorithm is used to find the unique direction of the c-axis which is consistent with the set of measured planes. The system will be integrated with the current capability at the National Ice Core Laboratory (NICL) for imaging and sizing ice grains to provide a facility which can completely characterize the fabric of ice thin sections. This facility will be available to anyone in the ice physics community who would like to analyze ice fabrics.The proponents will apply this instrument to specific issues relating ice flow and fabric. The project will take advantage of the vast store of samples from GISP2 which have been prepared but not analyzed due to time constraints. From this data set, the relationships between ice flow and softness indices determined from fabric will be investigated, and new analysis techniques to discern active process (e.g. grain formation) in the ice will be utilized. The presence of these processes has important implications for flow law exponents and the susceptibility of ice to deformation instabilities. These studies should point the way toward more accurate ice flow models, and also better diagnostics for analyzing ice core climate data.