Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.13937 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866908596602667008 |
|---|---|
| author | Ang, Iye Szin Findl, Martin Johannes Hauzinger, Elisabeth Sedlazeck, Klaus Philipp Savolainen, Jyrki Bakker, Ronald Galler, Robert Rueckert, Elmar |
| author_facet | Ang, Iye Szin Findl, Martin Johannes Hauzinger, Elisabeth Sedlazeck, Klaus Philipp Savolainen, Jyrki Bakker, Ronald Galler, Robert Rueckert, Elmar |
| contents | Automated rock classification from mineral composition presents a significant challenge in geological applications, with critical implications for material recycling, resource management, and industrial processing. While existing methods using One dimensional Convolutional Neural Network (1D-CNN) excel at mineral identification through Raman spectroscopy, the crucial step of determining rock types from mineral assemblages remains unsolved, particularly because the same minerals can form different rock types depending on their proportions and formation conditions. This study presents a novel knowledge-enhanced deep learning approach that integrates geological domain expertise with spectral analysis. The performance of five machine learning methods were evaluated out of which the 1D-CNN and its uncertainty-aware variant demonstrated excellent mineral classification performance (98.37+-0.006% and 97.75+-0.010% respectively). The integrated system's evaluation on rock samples revealed variable performance across lithologies, with optimal results for limestone classification but reduced accuracy for rocks sharing similar mineral assemblages. These findings not only show critical challenges in automated geological classification systems but also provide a methodological framework for advancing material characterization and sorting technologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13937 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Rock Classification through Knowledge-Enhanced Deep Learning: A Hybrid Mineral-Based Approach Ang, Iye Szin Findl, Martin Johannes Hauzinger, Elisabeth Sedlazeck, Klaus Philipp Savolainen, Jyrki Bakker, Ronald Galler, Robert Rueckert, Elmar Computational Engineering, Finance, and Science Automated rock classification from mineral composition presents a significant challenge in geological applications, with critical implications for material recycling, resource management, and industrial processing. While existing methods using One dimensional Convolutional Neural Network (1D-CNN) excel at mineral identification through Raman spectroscopy, the crucial step of determining rock types from mineral assemblages remains unsolved, particularly because the same minerals can form different rock types depending on their proportions and formation conditions. This study presents a novel knowledge-enhanced deep learning approach that integrates geological domain expertise with spectral analysis. The performance of five machine learning methods were evaluated out of which the 1D-CNN and its uncertainty-aware variant demonstrated excellent mineral classification performance (98.37+-0.006% and 97.75+-0.010% respectively). The integrated system's evaluation on rock samples revealed variable performance across lithologies, with optimal results for limestone classification but reduced accuracy for rocks sharing similar mineral assemblages. These findings not only show critical challenges in automated geological classification systems but also provide a methodological framework for advancing material characterization and sorting technologies. |
| title | Rock Classification through Knowledge-Enhanced Deep Learning: A Hybrid Mineral-Based Approach |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2510.13937 |