Skip to content

Does benchtop micro-XRF fill volcano petrology's value gap? An appraisal using the Tajogaite eruption time-series, La Palma, Canary Islands

Matthew Pankhurst 1,2, Andrew Menzies3, Ester Jolis4, Radoslaw Michallik4, Mia Tiljander4, Olivia Barbee5, Jane Scarrow6, Katy Chamberlain7, Alan Butcher8

  • Affiliations: 1Department of Earth Sciences, University of Geneva, Switzerland; 2Gaiaxiom, Copenhagen, Denmark; 3Bruker Nano Analytics GmbH, Berlin, Germany; 4Geological Survey of  Finland, Espoo, Finland; 5Xnovo Technology, Køge, Denmark; 6Department of Mineralogy and Petrology, University of Granada, Granada, Spain; 7School of Environmental Sciences, University of Liverpool, Liverpool, UK; 8Hafren Scientific Ltd, Welshpool, UK

  • Presentation type: Poster

  • Presentation time: Tuesday 16:30 - 18:30, Room Poster Hall

  • Poster Board Number: 239

  • Programme No: 1.6.14

  • Theme 1 > Session 6

Abstract

Volcano petrology brings the most value to monitoring efforts when it is not only accurate, representative, and insightful, but also rapid, frequent and integrated. Yet, petrology is logistically challenging and traditionally expensive to implement at the scales required. Here we hypothesize that petrology's value can be maximised by combining state-of-the-art, benchtop, microscale X-ray fluorescence (μXRF) and automated mineralogy methods (e.g. AMICS or open-source). μXRF requires only a sawn surface to analyse and provides rapid textural and per-pixel compositional analysis at the microscale. A typical thin-section sized area can be analysed at 25 μm resolution in <4 hours with no coating required, and whole-rock composition determination by signal integration. We analysed >40 lava samples from the 2021 Tajogiate eruption of Cumbre Vieja, La Palma multiple times using different instrument settings. We evaluated the results with direct reference to published petrological time-series data, including that  from the same sample surfaces analysed by QEMSCAN, EPMA, and LA-ICP-MS, as well as their splits analysed by destructive XRF and XRD. A value matrix was populated with the overall goal of comparing the scientific insights achieved against the time it took to recover them. We found that a well-calibrated μXRF coupled with tuned data-processing algorithms can produce accurate, representative, and insightful petrology from multiple samples in a 24 hour period. The datasets also support targeted and specialised analytical methods with higher resolution and sensitivity that leads to workflow efficiency gains, demonstrating that benchtop μXRF fills a value gap between high-resolution field sampling and high-resolution petrological analysis.