Customised display of large mineralogical (XRD) data: Geological advantages and applications

X-ray diffraction mineralogical analysis of geological sequences is a well-established procedure in both academia and industry, rendering a large volume of data in short-analytical time. Yet, standard data treatment and resulting interpretations present limitations related to the inherent complexities of natural geological materials (e.g. compositional variety, structural ordering), and are often time consuming and focussed on a very detailed inspection. Several alternatives were evaluated in terms of advantages and disadvantages to the main goal of generating a user-friendly, fast and intuitive way of processing a large volume of X-ray diffraction data. The potential of using raw X-ray diffraction data to interpret mineralogical diversity and relative phase abundances along sedimentary successions is explored here. A Python based program was tailored to assist in raw data organisation. After this automated step, a 3D surface computation renders the final result within minutes. This single-image representation can also be integrated with complementary information (sedimentary logs or other features of interest) for contrast and/or comparison in multi-proxy studies. The proposed approach was tested on a set of 81 bulk and clay-fraction diffractograms (intensity in counts per second—cps and respective angle—º2Ɵ) obtained from a Cenomanian mixed carbonate–siliciclastic stratigraphic succession, here explored by combining mineralogical (XY) and stratigraphic/geological information (Z). The main goal is to bypass preliminary data treatment, avoid time-consuming interpretation and unintended, but common, user-induced bias. Advantages of 3D modelling include fast processing and single-image solutions for large volumes of XRD data, combining mineralogical and stratigraphic information. This representation adds value by incorporating field (stratigraphic/sedimentological) information that complements and contextualises obtained mineralogical data. Limitations of using raw intensity data were evaluated by comparison with the results obtained via other standard data interpretation methods (e.g. semi-quantitative estimation). A visual and statistical contrast comparison confirmed a good equilibrium between computation speed and precision/utility of the final output.