In situ Laser Induced Breakdown Spectroscopy as a tool to discriminate volcanic rocks and magmatic series, Iceland.

• Published in: Spectrochimica Acta Part B: Atomic Spectroscopy
• Main Authors: Roux, Clément P.M., Rakovský, J., Musset, Olivier, Monna, Fabrice, Buoncristiani, Jean-François, Pellenard, Pierre, Thomazo, Christophe
• Other Authors: Laboratoire Interdisciplinaire Carnot de Bourgogne (ICB), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS), Archéologie, Terre, Histoire, Sociétés Dijon (ARTeHiS), Ministère de la Culture et de la Communication (MCC)-Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS), Biogéosciences UMR 6282 (BGS), Funding from the Conseil Régional de Bourgogne via the Synerjinov Program and the BQR (Bonus Qualité Recherche).
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2015
• Subjects: Laser Induced Breakdown Spectroscopy; Portable laser; Punctual analysis; Compositional data; Elemental analysis; [SDU.STU.VO]Sciences of the Universe [physics]/Earth Sciences/Volcanology; Iceland
• Online Access: https://hal.archives-ouvertes.fr/hal-01096389; https://doi.org/10.1016/j.sab.2014.11.013

7 pages International audience This study evaluates the potentialities of a lab-made pLIBS (portable Laser-Induced Breakdown Spectroscopy) to sort volcanic rocks belonging to various magmatic series. An in-situ chemical analysis of 19 atomic lines, including Al, Ba, Ca, Cr, Cu, Fe, Mg, Mn, Na, Si, Sr and Ti, from 21 sampled rocks was performed during a field exploration in Iceland. Iceland was chosen both for the various typologies of volcanic rocks and the rugged conditions in the field in order to test the sturdiness of the pLIPS. Elemental compositions were also measured using laboratory ICP-AES measurements on the same samples. Based on these latter results, which can be used to identify three different groups of volcanic rocks, a classification model was built in order to sort pLIBS data and to categorize unknown samples. Using a reliable statistical scheme applied to LIBS compositional data, the classification capability of the pLIBS system is clearly demonstrated (90–100% success rate). Although this prototype does not provide quantitative measurements, its use should be of particular interest for future geological field investigations.