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Imaging geomechanical properties of shales with infrared light
Jungin Lee
, Olivia J. Cook
,
Andrea P. Argüelles
,
Yashar Mehmani
Engineering Science and Mechanics
Leone Family Department of Energy and Mineral Engineering (EME)
Institute of Energy and the Environment (IEE)
Materials Research Institute (MRI)
Research output
:
Contribution to journal
›
Article
›
peer-review
10
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Scopus citations
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Keyphrases
Shale
100%
Infrared Spectra
100%
Infrared Light
100%
Geomechanical Properties
100%
Hyperspectral
66%
Linear Regression
33%
Artificial Neural Network
33%
Machine Learning Algorithms
33%
Indentation
33%
Mechanical Response
33%
Hydro
33%
Labeled Data
33%
Training Data
33%
Mid-infrared
33%
Shortwave
33%
Rock Composition
33%
Thermomechanical Properties
33%
X-ray Attenuation
33%
Point Measurement
33%
Lithology
33%
Scratch Test
33%
Utah
33%
Acoustic Velocity
33%
Line Measurement
33%
Convolutional Neural Network
33%
Green River Formation
33%
Infrared Reflectivity Spectra
33%
Pixel-wise
33%
Supervised Machine Learning
33%
Data Cube
33%
Experimental Workflow
33%
Rock Faces
33%
Engineering
Infrared Light
100%
Machine Learning Algorithm
100%
Hyperspectral Image
100%
Indentation
50%
Reflectance Spectrum
50%
Mechanical Response
50%
Larger Quantity
50%
Phase Composition
50%
Convolutional Neural Network
50%
Rock Face
50%
Earth and Planetary Sciences
Infrared Radiation
100%
Attenuation Coefficient
66%
Machine Learning
66%
Hyperspectral Image
66%
Reflectance
33%
Field Survey
33%
Acoustic Attenuation
33%
Utah
33%
Random Sampling
33%
Acoustic Velocity
33%
X Ray
33%