GNGTS 2021 - Atti del 39° Convegno Nazionale

499 GNGTS 2021 S essione 3.3 the Delaware Basin and its comparison with supervised Bayesian facies classification: SEG Technical Program Expanded Abstracts, 2619-2623. Coléu T., M. Poupon, K. Azbel, (2003), Unsupervised seismic facies classification: A review and comparison of techniques and implementation: The Leading Edge, 22, 10, 921-1056. Fedi M., G. Florio, (2001), Detection of potential fields source boundaries by enhanced horizontal derivative method: Geophys Prospect, 49 , 1, 40–58. Fraser S. J., G. A. Wilson, H. C. Leif., et al., (2012), Self-organizing maps for pseudo-lithological classification of 3D airborne electromagnetic, gravity gradiometry andmagnetic inversions: ASEG Extended Abstracts, 1-4. Infante-Paez L., K. Marfurt, (2019), Using machine learning as an aid to seismic geomorphologic, which attributes are the best input?: Interpretation, 7 , No. 3, 1A-T725. Kaski S., K. Lagus, (1996), Comparing self-organizing maps, in C. von der Malsburg, W. von Seelen, J.C. Vorbrüggen, B. Sendhoff, eds., Artificial Neural Networks — ICANN 96, 1112 , 809-814. Kohonen T., (1997), Exploration of very large databases by self-organizingmaps: Proceedings of International Conference on Neural Networks (ICNN’97), 1 , PL1–PL6. Roden R., T. Smith, D. Sacrey, (2015), Geologic pattern recognition from seismic attributes: Principal component analysis and self-organizing maps. Interpretation, 3 , No. 4, SAE59–SAE83. Waheed U. B., S. Al-Zahrani, S. M. Hanafy, (2019), Machine learning algorithms for automatic velocity picking: K-means vs. DBSCAN: SEG Technical Program Expanded Abstracts, 5110-5114. Autore di riferimento: carmine.cutaneo@gmail.com

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