An Integrated Machine Learning Platform for Multi-Scale and Multi-Domain Applications in Geosciences

References

1. Aminzadeh, F. and de Groot, P.
   , 2006. Neural Networks and Other Soft Computing Techniques with Applications in the Oil Industry, EAGE Publications.
   [Google Scholar]
2. Barnes, A. E.
   , and K. J.Laughlin, 2002. Investigation of methods for unsupervised classification of seismic data:72nd Annual International Meeting, SEG, Expanded Abstracts, 2221–2224.
   [Google Scholar]
3. Bohling, G. C.
   , and M.Dubois, 2003. An integrated application of neural network and markov chain techniques to prediction of lithofacies from well logs: Technical Report 50, Kansas Geological Survey Open File Report.
   [Google Scholar]
4. Dell’Aversana, P., Ciurlo, B., Colombo, S.
   , 2018. Integrated Geophysics and Machine Learning for Risk Mitigation in Exploration Geosciences. Extended abstract, Conference: 80th EAGE Annual Conference and Exhibition 2018.
   [Google Scholar]
5. Hall, B.
   , 2016. Facies classification using Machine learning:The Leading Edge, 35, 906–909.
   [Google Scholar]
6. Lary, D.J., Alavi, A.H., Gandom, A.H., Walker, A.L.
   , 2016. Machine learning in geosciences and remote sensing.Geoscience Frontiers. Volume 7, Issue 1, January 2016, Pages 3–10.
   [Google Scholar]
7. Raschka, S. and Mirjalili, V.
   , 2017. Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition, PACKT Books.
   [Google Scholar]
8. Russell, S. and Norvig, P.
   , 2016. Artificial Intelligence: A Modern approach, Global Edition, published by Pearson Education, Inc., publishing as Prentice Hall.
   [Google Scholar]