Interactive Machine Learning Improves Accuracy of Coal Porosity Segmentation in Focused Ion Beam–Scanning Electron Microscopy Images
Author:
Affiliation:
1. School of Energy Resources, China University of Geosciences, Beijing 100083, China
2. Institute of Earth Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
Funder
Israel Science Foundation
National Outstanding Youth Science Fund Project of National Natural Science Foundation of China
Council for Higher Education
China Scholarship Council
Publisher
American Chemical Society (ACS)
Subject
Energy Engineering and Power Technology,Fuel Technology,General Chemical Engineering
Link
https://pubs.acs.org/doi/pdf/10.1021/acs.energyfuels.3c01754
Reference49 articles.
1. Fractal characterization of adsorption-pores of coals from North China: An investigation on CH4 adsorption capacity of coals
2. Petrophysical characterization of coals by low-field nuclear magnetic resonance (NMR)
3. Micro-nano-scale pore stimulation of coalbed methane reservoirs caused by hydraulic fracturing experiments
4. Pore structure and its impact on CH4 adsorption capacity and flow capability of bituminous and subbituminous coals from Northeast China
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