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Scanning macro‐X‐ray fluorescence (XRF) spectroscopy on works of art provides researchers with rich data sets containing information about material composition and technique of material use in a compelling visual format in the form of element‐specific distribution maps. The accuracy of these maps, however, is influenced by the topography of the object, which ideally is two dimensional, relatively flat and able to be placed parallel to the data collection x-ray optics. In reality, few works of art are truly flat. Small nuances in the visualized elemental intensity may be introduced into element distribution maps by the presence of topography, whether the curve of a centuries-old panel painting, the natural warping of works on paper or parchment, or, in the most extreme cases, in actual three dimensional objects. The inability to confidently ascribe a change in signal intensity to actual elemental composition versus topographically-induced variance, therefore, presents a challenge, particularly when attempting to identify markers of artists’ techniques, compare several objects, or overlay/register images from scanning XRF with those from other imaging modalities.
To address this challenge, this paper introduces a new methodology for post-processing scanning XRF data sets to correct for elemental intensity variations as a function of topography. The method augments the acquired XRF data based on a three-dimensional reconstruction of an object and a set of elemental intensity/distance response functions. These response functions act as a calibrated guide for modifying the intensity map based on depth variation. The geometry-based parameters of local surface shape (curvature), distance of the XRF detector from the surface, region of intersection of the incident fluorescence beam with the surface, and the orientation of the incident beam with respect to the surface normal, are each accounted for in the calibration phase as a large set of pre-acquired examples. This provides a mechanism for capturing and understanding the anticipated variations in the macro-XRF data, interpolating the examples in order to smoothly estimate variations, and applying those variations as corrections to macro-XRF data collected on non-planar surfaces.
The acquisition and representation of the macro-XRF variation as a function of the geometry is explained, with an emphasis on understanding the parameters that induce the most severe errors in the XRF estimates. The representational framework for collecting, storing, and summarizing calibration data over a large number of scans is discussed, followed by several proof of concept examples, including data from one of the masterpieces of the J. Paul Getty Museum collection: Mummy portrait of a woman (JPGM #81.AP.42), also known as Isidora. This 1st century Romano-Egyptian funeral portrait on wood was originally included in mummy wrappings, and is therefore curved to match the natural curves of the embalmed subject. An XRF scan of Isidora was recently undertaken as part of a long-standing project – Ancient Panel Paintings: Examination, Analysis, and Research (APPEAR) – that seeks to increase our knowledge on the materials and manufacture of paintings of this type. The natural curvature of this panel painting, together with the rich texture typical of the encaustic technique, makes Isidora the perfect candidate to test the proposed methodology.
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