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.
|Title of host publication||Proceedings of the First EAGE/PESGB Workshop on Machine Learning (London2018)|
|Publisher||European Association of Geoscientists and Engineers|
|Publication status||Published - 2018|
|Event||First EAGE/PESGB Workshop Machine Learning - Olympia London, London, United Kingdom|
Duration: 29 Nov 2018 → 30 Nov 2018
|Conference||First EAGE/PESGB Workshop Machine Learning|
|Period||29/11/2018 → 30/11/2018|