Year: 2025
Type: article
Abstract: Microtextural and chemical data from minerals in igneous rocks are critical for unpacking and developing understanding of processes in magmatic systems. Recent advancements leveraging unsupervised machine learning methods offer novel approaches for phase classification without requiring prior knowledge of phases or chemistry. The present work expands on these methods through a probabilistic framework to enable a high‐throughput approach, which can result in significant improvements in segmentation of noisy EDS data collected during SEM mapping of rock specimens. We demonstrate that linear matrix decomposition methods, such as principal component analysis (PCA), can be applied to examine the chemical zonation of mineral phases without prior knowledge of their chemistry. To calibrate phase chemistry, we introduce a Bayesian Markov Chain Monte Carlo (MCMC) approach that scales high spatial resolution energy dispersive spectroscopy (EDS) data to high precision and accuracy electron probe microanalysis (EPMA) profiles. We use this technique to derive high quality and reproducible chemical data sets across large spatial fields. The proposed method couples textural and chemical observations that allow petrologists to better interpret magmatic systems and understand crustal processes.
DOI: 10.1029/2025jh000751
Institutions: University of Cambridge, University of California, Berkeley
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,500
OpenAlex ID: W4415956525