Gaussian Mixture Models for Robust Unsupervised Scanning-Electron Microscopy Image Segmentation of North Sea Chalk

Jesper Sören Dramsch, Frédéric Amour, Mikael Lüthje

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Abstract

Scanning-Electron images from North Sea Chalk are studied for important rock properties. To relieve this manual labor, we investigated several standard image processing methods that underperformed on complicated chalk. Due to the lack of manually labeled data, deep neural networks could not be adequately applied. Gaussian Mixture Models learnt a two-fold representation that separated the background well from the rock. Subsequent morphological filtering cleans up the prediction and enables automatic analysis.
Original languageEnglish
Title of host publicationProceedings of the First EAGE/PESGB Workshop on Machine Learning (London2018)
PublisherEuropean Association of Geoscientists and Engineers
Publication date2018
Pages28-30
ISBN (Print)978-1-5108-7668-2
DOIs
Publication statusPublished - 2018
EventFirst EAGE/PESGB Workshop Machine Learning - Olympia London, London, United Kingdom
Duration: 29 Nov 201830 Nov 2018

Conference

ConferenceFirst EAGE/PESGB Workshop Machine Learning
LocationOlympia London
CountryUnited Kingdom
CityLondon
Period29/11/201830/11/2018

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