Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6922278 | Computers & Geosciences | 2017 | 79 Pages |
Abstract
Textures and compositions are critical information for interpreting rock formation. Existing methods to integrate both types of information favor high-resolution images of mineral compositions over small areas or low-resolution images of larger areas for phase identification. The method in this paper produces images of individual phases in which textural and compositional details are resolved over three orders of magnitude, from tens of micrometers to tens of millimeters. To construct these images, called Phase Composition Maps (PCMs), we make use of the resolution in backscattered electron (BSE) images and calibrate the gray scale values with mineral analyses by energy-dispersive X-ray spectrometry (EDS). The resulting images show the area of a standard thin section (roughly 40 mm Ã 20 mm) with spatial resolution as good as 3.5 μm/pixel, or more than 81 000 pixels/mm2, comparable to the resolution of X-ray element maps produced by wavelength-dispersive spectrometry (WDS). Procedures to create PCMs for mafic igneous rocks with multivariate linear regression models for minerals with solid solution (olivine, plagioclase feldspar, and pyroxenes) are presented and are applicable to other rock types. PCMs are processed using threshold functions based on the regression models to image specific composition ranges of minerals. PCMs are constructed using widely-available instrumentation: a scanning-electron microscope (SEM) with BSE and EDS X-ray detectors and standard image processing software such as ImageJ and Adobe Photoshop. Three brief applications illustrate the use of PCMs as petrologic tools: to reveal mineral composition patterns at multiple scales; to generate crystal size distributions for intracrystalline compositional zones and compare growth over time; and to image spatial distributions of minerals at different stages of magma crystallization by integrating textures and compositions with thermodynamic modeling.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Kyle V. Willis, LeeAnn Srogi, Tim Lutz, Frederick C. Monson, Meagen Pollock,