Image analysis and mass petrography

岩相学-对材料薄片进行光学显微分析-是研究岩石、陶瓷和其他固体材料的矿物学和微观结构的最快和最具成本效益的工具。对岩石、土壤和考古薄片的研究已有150多年的历史，样本保存在世界各地的档案馆。岩相学分析产生的数据支持自然资源的勘探和加工--包括我们将需要的可再生能源技术所需的金属--建筑材料，以及对现代和古代物体的法医研究。岩相学分析传统上需要适当的光学显微镜，获得物理薄片样品，以及专家用户对样品进行分类、测量和解释。一次只能查看一个样本，并进行分析。专家用户可以使用基础显微镜在几分钟内获得定性数据，但定量分析很耗时。然而，突破性的新光学显微镜技术(见图)允许同时扫描多个切片，以非常快地产生高质量图像。我们将把这一进步与基于计算机的图像分析相结合，用于分类和量化，使薄片档案和以前不可用的派生数据能够被带入数字环境。这一博士将开发一种新的、高度自动化的工作流程，它将充分发挥光学分析的价值，并将人类的识别和分类水平与超人水平的速度和重复性相结合。这些方法将允许对矿物学和岩石学采取定量和统计可靠的方法，并迅速开发考古材料的参考数据库。这些反过来又释放了在地质学、考古学和材料科学方面进行新研究的潜力，对自然和考古材料的结构和矿物学多样性有了更深入的了解。

Petrography - the optical microscopic analysis of thin sections of materials - is the fastest and most cost-effective tool for investigating the mineralogy and microtexture of rocks, ceramics, and other solid materials. Rock, soil and archaeological thin sections have been studied for over 150 years, with samples in archives around the world. The data produced from petrographic analysis underpins the exploration for and processing of natural resources - including the metals we will need for renewable energy technologies - materials used in construction, and forensic studies of objects both recent and ancient.Petrographic analysis has traditionally required an appropriate optical microscope, access to the physical thin section samples, and an expert user to classify, measure and interpret the specimen. Samples could be viewed, and thus analysed, only one-at-a-time. Expert users can obtain qualitative data within minutes using a basic microscope, but quantitative analysis is time consuming. However, ground-breaking new optical microscopy technology (see figure) allows multiple sections to be scanned simultaneously to produce high quality images very rapidly. We will pair this advance with computer-based image analysis for classification and quantification, enabling thin section archives and previously-unavailable derived data to be brought into a digital environment.This PhD will develop a new, highly automated, workflow that will unlock the full value of optical analysis and combine human levels of identification and classification with superhuman levels of speed and reproducibility. These approaches will allow for quantitative and statistically-robust approaches to mineralogy and petrology, and for the rapid development of reference databases for archaeological materials. These in turn unlock the potential for new research in geology, archaeology and material science, with deeper insight into textural and mineralogical diversity of natural and archaeological materials.