SMART mineral mapping: Synchrotron-based machine learning approach for 2D characterization with coupled micro XRF-XRD

Julie J. Kim, Florence T. Ling, Dan A. Plattenberger, Andres F. Clarens, Antonio Lanzirotti, Matthew Newville, Catherine A. Peters

Research output: Contribution to journalArticlepeer-review

Abstract

A Synchrotron-based Machine learning Approach for RasTer (SMART) mineral mapping was developed for the purpose of training a mineral classifier for characterization of millimeter-sized areas of rock thin sections with micron-scale resolution. An Artificial Neural Network (ANN) was used to extract relationships between coupled micro x-ray fluorescence (μXRF) data for element abundances and micro x-ray diffraction (μXRD) data for mineral identity. Once trained, the resulting classifier, i.e., the SMART mineral mapper, can identify minerals using only μXRF data. This is the real value of this machine learning approach because μXRF data are relatively fast to collect and interpret whereas μXRD data take longer to collect and interpret. Training and testing of an initial mapper were done with 192 coupled μXRF-μXRD data points sampled from a 0.25 mm2 area of a shale from the Eagle Ford formation, which was scanned with 2 μm resolution. All data used in this work were obtained from the Advanced Photon Source synchrotron beamline 13-ID-E at Argonne National Laboratory. Three minerals were mapped in the Eagle Ford rock sample, for which there were 8 elements characterized. In the testing phase, the minerals were correctly classified with accuracy of 97 % and higher. The trained SMART mapper was applied for self-similar upscaling by mapping a 14 mm2 scan of the Eagle Ford sample. Generated maps captured micro-scale features characteristic of the stratified texture of the rock, and the identified minerals agreed well with bulk XRD analysis of the powdered rock. The SMART mapper was also applied to a scan of a 6-mineral mixture of known composition to demonstrate ability to distinguish minerals of similar chemistry. The trained SMART mapper is transferable to scans from other x-ray microprobes because of the μXRF data normalization that accounts for sample- and beamline-specific properties like thickness, detector configuration, and incident energy.

Original languageEnglish (US)
Article number104898
JournalComputers and Geosciences
Volume156
DOIs
StatePublished - Nov 2021

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computers in Earth Sciences

Keywords

  • Artificial neural network
  • Machine learning
  • Mineral mapping
  • Mineral spatial distribution
  • Synchrotron x-ray microprobe
  • X-ray fluorescence

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