Pore segmentation in electron micrographs: A probabilistic approach by ensemble machine learning
Open Access
- 19 November 2025
- journal article
- Published by Elsevier in Applied Clay Science
- Vol. 279
Abstract
No abstract availableThis publication has 19 references indexed in Scilit:
- Application of Machine Learning Techniques in Mineral Classification for Scanning Electron Microscopy - Energy Dispersive X-Ray Spectroscopy (SEM-EDS) ImagesJournal of Petroleum Science and Engineering, 2021
- Mechanical behavior of sandy facies of Opalinus Clay under different load conditionsJournal of Rock Mechanics and Geotechnical Engineering, 2020
- Mineral grains recognition using computer vision and machine learningComputers & Geosciences, 2019
- The Status of Water in Swelling Shales: An Insight from the Water Retention Properties of the Callovo-Oxfordian ClaystoneRock Mechanics and Rock Engineering, 2016
- Stochastic microstructure characterization and reconstruction via supervised learningActa Materialia, 2016
- BIB-SEM characterization of pore space morphology and distribution in postmature to overmature samples from the Haynesville and Bossier ShalesMarine and Petroleum Geology, 2015
- Pore morphology and distribution in the Shaly facies of Opalinus Clay (Mont Terri, Switzerland): Insights from representative 2D BIB–SEM investigations on mm to nm scaleApplied Clay Science, 2013
- BIB-SEM study of the pore space morphology in early mature Posidonia Shale from the Hils area, GermanyInternational Journal of Coal Geology, 2012
- Extremely randomized treesMachine Learning, 2006
- Connectionist learning proceduresArtificial Intelligence, 1989