Quantitative X-ray Map Analyser (Q-XRMA): A new GIS-based statistical approach to Mineral Image Analysis

• Published in: Computers & geosciences Vol. 115; pp. 56 - 65
• Main Authors: Ortolano, Gaetano, Visalli, Roberto, Godard, Gaston, Cirrincione, Rosolino
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2018; Elsevier
• Subjects: ArcGIS; biotite; calibration; case studies; computer software; computers; data collection; energy-dispersive X-ray analysis; geographic information systems; image analysis; Linear regression; materials science; Mineral formula quantification; Numerical petrology; plagioclase; Python; regression analysis; Sciences of the Universe; stoichiometry; X-radiation; Numerical petrology; ArcGIS; Mineral formula quantification; Linear regression; Python
• ISSN: 0098-3004; 1873-7803
• DOI: 10.1016/j.cageo.2018.03.001

We present a new ArcGIS®-based tool developed in the Python programming language for calibrating EDS/WDS X-ray element maps, with the aim of acquiring quantitative information of petrological interest. The calibration procedure is based on a multiple linear regression technique that takes into account interdependence among elements and is constrained by the stoichiometry of minerals. The procedure requires an appropriate number of spot analyses for use as internal standards and provides several test indexes for a rapid check of calibration accuracy. The code is based on an earlier image-processing tool designed primarily for classifying minerals in X-ray element maps; the original Python code has now been enhanced to yield calibrated maps of mineral end-members or the chemical parameters of each classified mineral. The semi-automated procedure can be used to extract a dataset that is automatically stored within queryable tables. As a case study, the software was applied to an amphibolite-facies garnet-bearing micaschist. The calibrated images obtained for both anhydrous (i.e., garnet and plagioclase) and hydrous (i.e., biotite) phases show a good fit with corresponding electron microprobe analyses. This new GIS-based tool package can thus find useful application in petrology and materials science research. Moreover, the huge quantity of data extracted opens new opportunities for the development of a thin-section microchemical database that, using a GIS platform, can be linked with other major global geoscience databases. •New python script code for numerical petrology purpose.•ArcGIS®-based image processing tool for calibrating mineral X-ray maps.•Element concentration prediction of WDS/EDS X-ray maps by multilinear regression.•End-member fraction maps unraveling complex zoning pattern.