Data di Pubblicazione:
2023
Citazione:
Automatic organofacies identification by means of Machine Learning on Raman spectra / N.A. Vergara Sassarini, A. Schito, M. Gasparrini, P. Michel, S. Corrado. - In: INTERNATIONAL JOURNAL OF COAL GEOLOGY. - ISSN 0166-5162. - 271:(2023 Apr 15), pp. 104237.1-104237.21. [10.1016/j.coal.2023.104237]
Abstract:
In this study we compare and evaluate different unsupervised clustering algorithms for organofacies
discrimination in low maturity dispersed organic matter based on Raman spectroscopic analyses. A
total of 1363 Raman spectra were collected from a set of 27 organic-rich samples from the Lower
Toarcian shale interval of the Paris Basin sub-surface. Rock-Eval pyrolysis data indicate a type II to
type III kerogen with a vitrinite reflectance (Ro%) between 0.45% and 0.65%, and Tmax between
415 °C and 438 °C. Organic petrographic observations under transmitted light reveal the presence
of organofacies composed by amorphous organic matter, opaque, and translucent phytoclasts. An
optical classification of organic particles was performed on about 40-60 fragments per sample and
used as the ground truth. Raman spectra were obtained for all the classified fragments and principal
component analysis was performed to underline the variability among spectra. Unsupervised
clustering was then applied on Raman spectra principal components. Three clustering methods were
applied to evaluate their effectiveness in predicting number, shape and density of clusters and a
contingency matrix was used to quantify their ability to predict different organofacies. Gaussian
Mixture Model (GMM) was found to be the best algorithm for organofacies identification showing
an accuracy mostly between 80% and 90%. This work outlines how unsupervised clustering of
Raman spectra of dispersed organic matter can reduce the uncertainty in thermal maturity
assessment and help the classification of highly heterogeneous organofacies when using large
datasets for Earth and planetary sciences studies.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
Raman spectroscopy; machine learning; cluster analysis; dispersed organic matter; principal component analysis; thermal maturity;
Elenco autori:
N.A. Vergara Sassarini, A. Schito, M. Gasparrini, P. Michel, S. Corrado
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