# From Spectra to Geography: Intelligent Mapping of RRUFF Mineral Data

arXiv: https://arxiv.org/abs/2411.11693 · HTML: https://mlsanity.com/paper/2411.11693/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 18 Nov 2024.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution](https://mlsanity.com/paper/1708.09022/) (2017)
- [Raman Spectrum Matching with Contrastive Representation Learning](https://mlsanity.com/paper/2202.12549/) (2022)
- [Gemtelligence: Accelerating Gemstone classification with Deep Learning](https://mlsanity.com/paper/2306.06069/) (2023)

## Abstract (arXiv)

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.