From Spectra to Geography: Intelligent Mapping of RRUFF Mineral Data
This work addresses the need for accurate mineral geolocation in geology and related fields, representing a novel application of machine learning to spectral data.
This study tackled the problem of determining the geographic origin of mineral samples by developing a machine learning framework that uses Raman spectral data from the RRUFF database to classify minerals at the country level, achieving an average accuracy of 93%.
Accurately determining the geographic origin of mineral samples is pivotal for applications in geology, mineralogy, and material science. Leveraging the comprehensive Raman spectral data from the RRUFF database, this study introduces a novel machine learning framework aimed at geolocating mineral specimens at the country level. We employ a one-dimensional ConvNeXt1D neural network architecture to classify mineral spectra based solely on their spectral signatures. The processed dataset comprises over 32,900 mineral samples, predominantly natural, spanning 101 countries. Through five-fold cross-validation, the ConvNeXt1D model achieved an impressive average classification accuracy of 93%, demonstrating its efficacy in capturing geospatial patterns inherent in Raman spectra.