Using the traditional microscope for mineral grain orientation determination: A prototype image analysis pipeline for optic-axis mapping (POAM)

This paper reports on the development of an open-source image analysis software 'pipeline' dedicated to petrographic microscopy. Using conventional rock thin sections and images from a standard polarising microscope, the pipeline can classify minerals and subgrains into objects and obtain information about optic-axis orientation. Five metamorphic rocks were chosen to test and illustrate the method. Thin sections were imaged using reflected and cross- and plane-polarised transmitted light. Images were taken at different angles of the polariser and analyser (360 degrees with 10 degrees steps), both with and without the full-lambda plate. The resulting image stacks were analysed with a modular pipeline for optic-axis mapping (POAM). POAM consists of external and internal software packages that register, segment, classify, and interpret the visible light spectra using object-based image analysis (OBIAS). The mapped fields-of-view and grain orientation stereonets of interest are presented in the context of whole-slide images.Two innovations are reported. First, we used hierarchical tree region merging on blended multimodal images to classify individual grains of rock-forming minerals into objects. Second, we assembled a new optical mineralogy algorithm chain that identifies the mineral slow axis orientation. The c-axis orientation results were verified with scanning electron microscopy electron backscattered diffraction (SEM-EBSD) data. For quartz (uniaxial) in a granite mylonite the test yielded excellent correspondence of c-axis azimuth and good agreement for inclination. For orthorhombic orthopyroxene in a deformed garnet harzburgite, POAM produced acceptable results for slow axis azimuth. In addition, the method identified slight anisotropy in garnet that would not be appreciated by traditional microscopy.We propose that our method is ideally suited for two commonly performed tasks in mineralogy. First, for mineral grain classification of entire thin sections scans on blended images to provide automated modal abundance estimates and grain size distribution. Second, for prospective fields of view of interest, POAM can rapidly generate slow axis crystal orientation maps from multiangle image stacks on conventionally prepared thin sections for targeting detailed SEM-EBSD studies. This article reports on the use of the traditional petrographic microscope to study rock thin sections containing hundreds of mineral grains using repurposed and new image analysis software. The software is based on optical mineralogy principles and algorithms that were tested using five metamorphic rocks with strikingly good performance in interactive image segmentation, mineral classification and orientation estimation. First, the polarised light microscopy photos are acquired in all the available illumination modes by rotating the stage. Second, they are imported into external software for alignment and object-based image analysis of individual grains. The user is free to modify how he averages the input microscopy images to improve the machine learning classification of the grains and what algorithms to use according to the studied sample. Third, a new MatLab script streams and rescales the data configuring a 'pipeline' for optic-axis orientation mapping (POAM). In comparison with similar techniques, POAM new algorithm increases the realism of the orientation maps by simulating and matching the mineral visible light colours with the Michel-Levy interference colour chart. Lastly, the mineral target maps (quartz, garnet, and olivine) are presented in a consistent format, allowing comparison with validation or standard sample datasets. The method was demonstrated with a ground-truth X-ray diffraction technique that measured quartz crystallographic c-axis. Therefore, under certain reliability and constraint criteria, POAM constitutes a free precheck to any orientation-dependent spectroscopic method in virtually any lithology.