Treffer: VISION TRANSFORMERS FOR X-RAY DIFFRACTION PATTERNS ANALYSIS

Title:
VISION TRANSFORMERS FOR X-RAY DIFFRACTION PATTERNS ANALYSIS
Contributors:
Institut Denis Poisson (IDP), Université d'Orléans (UO)-Université de Tours (UT)-Centre National de la Recherche Scientifique (CNRS), Bureau de Recherches Géologiques et Minières (BRGM), Institut universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), ANR-20-THIA-0017,AI.i0 PhD Fellowship,AI.iO Artificial Intelligence in Orléans: Apprentissage à partir de données hétérogènes et de connaissances expert. Application aux sciences géologiques et environnementales(2020)
Source:
ICASSP 2025 - 2025 IEEE International Conference on Acoustics. :1-5
Publisher Information:
CCSD; IEEE, 2025.
Publication Year:
2025
Collection:
collection:BRGM
collection:UNIV-TOURS
collection:CNRS
collection:UNIV-ORLEANS
collection:INSMI
collection:IDP
collection:ANR
collection:ANR-IA-20
collection:ANR-IA
collection:BRGM-TEST
Subject Geographic:
Original Identifier:
HAL: hal-04696156
Document Type:
Konferenz conferenceObject<br />Conference papers
Language:
English
Relation:
info:eu-repo/semantics/altIdentifier/doi/10.1109/ICASSP49660.2025.10887635
DOI:
10.1109/ICASSP49660.2025.10887635
Rights:
info:eu-repo/semantics/OpenAccess
Accession Number:
edshal.hal.04696156v1
Database:
HAL

Weitere Informationen

Understanding materials properties depends largely on the ability to determine its components, and in particular its mineral phases. Powder X-ray diffraction (XRD) is a powerful tool for such purposes. This paper presents a Transformerbased vision model (ViT) for mineral phase identification, and proportion inference to quantify the mineral phases present in a material. Our analysis shows that the tokenization strategy is a critical step for XRD pattern analysis. The results obtained for both tasks are excellent and more robust than those obtained with a CNN. The proposed approach also makes it possible to introduce visualization tools for signal analysis, to better understand how information flows through the model and how data is classified or quantified.