TY - JOUR
T1 - SMART mineral mapping
T2 - Synchrotron-based machine learning approach for 2D characterization with coupled micro XRF-XRD
AU - Kim, Julie J.
AU - Ling, Florence T.
AU - Plattenberger, Dan A.
AU - Clarens, Andres F.
AU - Lanzirotti, Antonio
AU - Newville, Matthew
AU - Peters, Catherine A.
N1 - Funding Information:
This material is based upon work supported by the High Meadows Environmental Institute at Princeton University. Portions of this work were performed at GeoSoilEnviroCARS (The University of Chicago, Sector 13), Advanced Photon Source (APS), Argonne National Laboratory. GeoSoilEnviroCARS is supported by the National Science Foundation – Earth Sciences (EAR – 1634415 ) and Department of Energy- GeoSciences ( DE-FG02-94ER14466 ). This research used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. Use of the Advanced Photon Source at Argonne National Laboratory was supported by the U. S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. The authors acknowledge the use of Princeton's Imaging and Analysis Center, which is partially supported by the Princeton Center for Complex Materials, a National Science Foundation (NSF)-MRSEC program (DMR-1420541).
Funding Information:
This material is based upon work supported by the High Meadows Environmental Institute at Princeton University. Portions of this work were performed at GeoSoilEnviroCARS (The University of Chicago, Sector 13), Advanced Photon Source (APS), Argonne National Laboratory. GeoSoilEnviroCARS is supported by the National Science Foundation – Earth Sciences (EAR – 1634415) and Department of Energy- GeoSciences (DE-FG02-94ER14466). This research used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. Use of the Advanced Photon Source at Argonne National Laboratory was supported by the U. S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. The authors acknowledge the use of Princeton's Imaging and Analysis Center, which is partially supported by the Princeton Center for Complex Materials, a National Science Foundation (NSF)-MRSEC program (DMR-1420541).
Publisher Copyright:
© 2021 The Authors
PY - 2021/11
Y1 - 2021/11
N2 - 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.
AB - 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.
KW - Artificial neural network
KW - Machine learning
KW - Mineral mapping
KW - Mineral spatial distribution
KW - Synchrotron x-ray microprobe
KW - X-ray fluorescence
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U2 - 10.1016/j.cageo.2021.104898
DO - 10.1016/j.cageo.2021.104898
M3 - Article
AN - SCOPUS:85111689228
SN - 0098-3004
VL - 156
JO - Computers and Geosciences
JF - Computers and Geosciences
M1 - 104898
ER -