A Predictive Model for Maceral Discrimination by Means of Raman Spectra on Dispersed Organic Matter: A Case Study from the Carpathian Fold-and-Thrust Belt (Ukraine)

• Main Authors: Andrea Schito, Alexandra Guedes, Bruno Valentim, Amanda Vergara Sassarini, Sveva Corrado
• Format: Article
• Language: English
• Published: MDPI AG 2021-05-01
• Series: Geosciences
• Subjects: Raman spectroscopy; dispersed organic matter; vitrinite reflectance; principal component analysis; partial least square discriminant analysis; machine learning
• Online Access: https://www.mdpi.com/2076-3263/11/5/213

In this study, we propose a predictive model for maceral discrimination based on Raman spectroscopic analyses of dispersed organic matter. Raman micro-spectroscopy was coupled with optical and Rock-Eval pyrolysis analyses on a set of seven samples collected from Mesozoic and Cenozoic successions of the Outer sector of the Carpathian fold and thrust belt. Organic petrography and Rock-Eval pyrolysis evidence a type II/III kerogen with complex organofacies composed by the coal maceral groups of the vitrinite, inertinite, and liptinite, while thermal maturity lies at the onset of the oil window spanning between 0.42 and 0.61 R_o%. Micro-Raman analyses were performed, on approximately 30–100 spectra per sample but only for relatively few fragments was it possible to perform an optical classification according to their macerals group. A multivariate statistical analysis of the identified vitrinite and inertinite spectra allows to define the variability of the organofacies and develop a predictive PLS-DA model for the identification of vitrinite from Raman spectra. Following the first attempts made in the last years, this work outlines how machine learning techniques have become a useful support for classical petrography analyses in thermal maturity assessment.