Research paper Quantitative X-ray Map Analyser (Q-XRMA): A new GIS-based statistical approach to Mineral Image Analysis Gaetano Ortolano a , Roberto Visalli a, * , Gaston Godard b , Rosolino Cirrincione a a Department of Biological, Geological and Environmental Sciences, University of Catania, Corso Italia, 98125, Italy b Universit e Paris-Diderot, Institut de Physique du Globe de Paris, UMR CNRS 7154, F-75238, Paris cedex 05, France ARTICLE INFO Keywords: Numerical petrology Linear regression Mineral formula quantification ArcGIS ® Python ABSTRACT 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 ac- curacy. 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. 1. Introduction Advanced geological and petrographic investigations are increasingly supported by a collection of quantitative information. In this context, quantitative X-ray mapping is emerging as an innovative tool that can be applied in many fields, from basic scientific research to mining industry, cultural heritage and forensic geology, as demonstrated by the large in- vestments made by manufacturers of microanalytical devices. However, in contrast to single spot analyses, X-ray maps are characterised by semi- quantitative raw data that is not corrected, for instance, for the mean atomic number, absorption or fluorescence (ZAF) effects (Lanari et al., 2018 and references therein). In this context, to derive fully quantitative data on element concentrations, good analytical standardization pro- cedures are crucial in overcoming the intrinsic limits of X-ray mapping (Lanari et al., 2018 and references therein), such the previously mentioned ZAF effects, background noise and possible peak overlap in energy-dispersive-spectrometer (EDS) X-ray maps, or the volatilisation of light elements due to prolonged exposure under a finely focused electron beam. Taking into careful consideration the problems above, a new per- forming multiple linear regression technique was applied for the first time to calibrate the unprocessed wavelength-dispersive-spectrometer (WDS) or EDS X-ray maps using high-precision spot analyses as inter- nal standards (De Andrade et al., 2006). The developed procedure, which largely modifies the pre-existing ArcGIS ® -based image processing plat- form by Ortolano et al. (2014b) (i.e., X-ray Map Analyser), aims to cali- brate sets of X-ray maps for each classified phase. In general, image processing in materials science is mainly based on the multivariate statistical analysis of raw X-ray intensities in EDS/WDS matrices or of images with multiple raster bands (typically 8-bit images with 256 intensity levels) recording the distribution of chemical ele- ments. This is one of the techniques applied routinely in the investigation of many geo-petrological processes (e.g., Airaghi et al., 2017; Belfiore et al., 2016; Coutelas et al., 2004; Lanari et al., 2013; Loury et al., 2016; Marmo et al., 2002; M eszaros et al., 2016; Ortolano et al., 2014a; Vidal et al., 2006). The technique has been adopted also thanks to the devel- opment of semi-automated tools for classifying mineral phases and quantifying modal parameters from selected thin section micro-domains * Corresponding author. E-mail address: rvisalli@unict.it (R. Visalli). Contents lists available at ScienceDirect Computers and Geosciences journal homepage: www.elsevier.com/locate/cageo https://doi.org/10.1016/j.cageo.2018.03.001 Received 23 June 2017; Received in revised form 23 February 2018; Accepted 2 March 2018 Available online 6 March 2018 0098-3004/© 2018 Elsevier Ltd. All rights reserved. Computers and Geosciences 115 (2018) 56–65