Machine Learning for Geoscience Modelling at IAMG 2018 in Olomouc
would like to thank all the contributors to the success of the session - some very excellent talks, dynamic exchange and discussions. Very glad to see the IAMG geo-data science community actively expanding:
Clustering of environmental data using local fractality concept and machine learning. Mikhail Kanevski, Mohamed Laib
Domaining with decision trees and geostatistical simulation. Gunes Ertunc, A. Erhan Tercan
Integration of geologically interpretative features into machine learning facies classification. Julie Halotel, Vasily Demyanov
Automated lithofacies classification of the Jurassic sequence using machine learning on a large structured well database. Harald W. B?e, Kristian B. Brandsegg, Kenneth Duffaut, Alenka Crne
Neural network clustering to improve geological and engineering understanding for more reliable reservoir prediction. Elena Kharyba, Vasily Demyanov, Andrey Antropov, Luka Malencic
Methodology of fast well log interpretation based on deep learning models. Alexander Reshytko, Maria Golitsyna, Dmitry Egorov, Nikita Bukhanov, Artyom Semenikhin, Oksana Osmonalieva, Boris Belozerov
Stochastic Simulation with Generative Adversarial Networks. Lukas Mosser, Olivier Dubrule, Martin Blunt
Probabilistic inversion using forward models based on Machine Learning. Thomas Mejer Hansen, Knud Skou Cordua, Tue-Holm Jensen
Influence of input data quantity on accuracy of reservoir properties prediction with machine learning algorithms. Dmitry Egorov, Nikita Bukhanov, Boris Belozerov
Efficient uncertainty quantification of reservoir productions by stacked autoencoder-based clustering. Kyungbook Lee, Taehun Lee, Jaejun Kim, Byeongcheol Kang, Changhyup Park, Hyundon Shin, Jonggeun Choe
GemPy: Towards high dimensionality problems in structural geological modeling as Bayesian inference. Miguel de la Varga, Florian Wellmann
This is all happened thanks to Karel Hron and his team of Palacky University Olomouc for the most warm welcome and the hospitality.