{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T22:57:43Z","timestamp":1775343463411,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T00:00:00Z","timestamp":1632355200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Queiroz Galv\u00e3o Explora\u00e7\u00e3o &amp; Produ\u00e7\u00e3o","award":["QGEP 0220180261"],"award-info":[{"award-number":["QGEP 0220180261"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Seismic interpretation is a fundamental process for hydrocarbon exploration. This activity comprises identifying geological information through the processing and analysis of seismic data represented by different attributes. The interpretation process presents limitations related to its high data volume, own complexity, time consumption, and uncertainties incorporated by the experts\u2019 work. Unsupervised machine learning models, by discovering underlying patterns in the data, can represent a novel approach to provide an accurate interpretation without any reference or label, eliminating the human bias. Therefore, in this work, we propose exploring multiple methodologies based on unsupervised learning algorithms to interpret seismic data. Specifically, two strategies considering classical clustering algorithms and image segmentation methods, combined with feature selection, were evaluated to select the best possible approach. Additionally, the resultant groups of the seismic data were associated with groups obtained from well logs of the same area, producing an interpretation with aggregated lithologic information. The resultant seismic groups correctly represented the main seismic facies and correlated adequately with the groups obtained from the well logs data.<\/jats:p>","DOI":"10.3390\/s21196347","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T22:16:38Z","timestamp":1632780998000},"page":"6347","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Unsupervised Machine Learning Applied to Seismic Interpretation: Towards an Unsupervised Automated Interpretation Tool"],"prefix":"10.3390","volume":"21","author":[{"given":"Alimed","family":"Celecia","sequence":"first","affiliation":[{"name":"Electrical Engineering Department, PUC-Rio, Rio de Janeiro 22451-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8420-3937","authenticated-orcid":false,"given":"Karla","family":"Figueiredo","sequence":"additional","affiliation":[{"name":"Department of Informatics and Computer Science, Institute of Mathematics and Statistics, State University of Rio de Janeiro (UERJ), Rio de Janeiro 20550-900, Brazil"}]},{"given":"Carlos","family":"Rodriguez","sequence":"additional","affiliation":[{"name":"Tecgraf Institute, PUC-Rio, Rio de Janeiro 22451-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9790-1328","authenticated-orcid":false,"given":"Marley","family":"Vellasco","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, PUC-Rio, Rio de Janeiro 22451-900, Brazil"}]},{"given":"Edwin","family":"Maldonado","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, PUC-Rio, Rio de Janeiro 22451-900, Brazil"}]},{"given":"Marco Aur\u00e9lio","family":"Silva","sequence":"additional","affiliation":[{"name":"Department of Electronics and Telecommunications, State University of Rio de Janeiro (UERJ), Rio de Janeiro 20550-900, Brazil"}]},{"given":"Anderson","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, PUC-Rio, Rio de Janeiro 22451-900, Brazil"}]},{"given":"Renata","family":"Nascimento","sequence":"additional","affiliation":[{"name":"Tecgraf Institute, PUC-Rio, Rio de Janeiro 22451-900, Brazil"}]},{"given":"Carla","family":"Ourofino","sequence":"additional","affiliation":[{"name":"Tecgraf Institute, PUC-Rio, Rio de Janeiro 22451-900, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,23]]},"reference":[{"key":"ref_1","first-page":"14","article-title":"Encyclopedic dictionary of applied geophysics","volume":"13","author":"Sheriff","year":"2002","journal-title":"Encycl. Dict. Appl. Geophys."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Onajite, E. (2014). Understanding seismic interpretation methodology. Seismic Data Analysis Techniques in Hydrocarbon Exploration, Elsevier.","DOI":"10.1016\/B978-0-12-420023-4.00013-7"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"P33","DOI":"10.1190\/1.2716717","article-title":"Redundant and useless seismic attributes","volume":"72","author":"Barnes","year":"2007","journal-title":"Geophysics"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Onajite, E. (2014). Understanding reflection coefficient. Seismic Data Analysis Techniques in Hydrocarbon Exploration, Elsevier.","DOI":"10.1016\/B978-0-12-420023-4.00014-9"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"SAE59","DOI":"10.1190\/INT-2015-0037.1","article-title":"Geologic pattern recognition from seismic attributes: Principal component analysis and self-organizing maps","volume":"3","author":"Roden","year":"2015","journal-title":"Interpretation"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Herron, D.A. (2011). First Steps in Seismic Interpretation, Society of Exploration Geophysicists.","DOI":"10.1190\/1.9781560802938"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Nanda, N.C. (2016). Seismic Pitfalls. Seismic Data Interpretation and Evaluation for Hydrocarbon Exploration and Production, Springer.","DOI":"10.1007\/978-3-319-26491-2"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Amado, L.B.T.-R.E. (2013). Field case evaluations. Reservoir Exploration and Appraisal, Gulf Professional Publishing. Chapter 12.","DOI":"10.1016\/B978-1-85617-853-2.00003-X"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1007\/s11053-020-09784-3","article-title":"A Review of exploration, development, and production cost offshore Newfoundland","volume":"30","author":"Kaiser","year":"2021","journal-title":"Nat. Resour. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"O83","DOI":"10.1190\/geo2017-0595.1","article-title":"Seismic facies analysis using machine learning","volume":"83","author":"Wrona","year":"2018","journal-title":"Geophysics"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"SE69","DOI":"10.1190\/INT-2018-0208.1","article-title":"Seismic structure interpretation based on machine learning: A case study in coal mining","volume":"7","author":"Li","year":"2019","journal-title":"Interpretation"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Di, H., Wang, Z., and AlRegib, G. (2018, January 22\u201325). Why using CNN for seismic interpretation? An investigation. Proceedings of the 2018 SEG International Exposition and Annual Meeting, SEG 2018, Keystone, CO, USA.","DOI":"10.1190\/segam2018-2997155.1"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chevitarese, D.S., Szwarcman, D., Brazil, E.V., and Zadrozny, B. (2018, January 8\u201313). Efficient classification of seismic textures. Proceedings of the International Joint Conference on Neural Networks, Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489654"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1016\/j.petrol.2019.01.113","article-title":"Machine learning in oil and gas; a SWOT analysis approach","volume":"176","author":"Hajizadeh","year":"2019","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2245","DOI":"10.1007\/s13202-020-00895-4","article-title":"Characterization of well logs using K-mean cluster analysis","volume":"10","author":"Ali","year":"2020","journal-title":"J. Pet. Explor. Prod. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chopra, S., Marfurt, K., and Sharma, R. (2019). Unsupervised machine learning facies classification in the Delaware Basin and its comparison with supervised Bayesian facies classification. SEG Technical Program Expanded Abstracts 2019, Society of Exploration Geophysicists.","DOI":"10.1190\/segam2019-3214088.1"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1088\/1742-2140\/aa8433","article-title":"Unsupervised seismic facies analysis with spatial constraints using regularized fuzzy c-means","volume":"14","author":"Song","year":"2017","journal-title":"J. Geophys. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Figueiredo, A.M., Silva, F.B., Silva, P.M., Martins, L.D.O., Milidi\u00fa, R.L., and Gattass, M. (2015). A Clustering-based Approach to Map 3D Seismic Horizons. Proceedings of the 14th International Congress of the Brazilian Geophysical Society, Rio de Janeiro, Brazil, 3\u20136 August 2015, Society of Exploration Geophysicists.","DOI":"10.1190\/sbgf2015-233"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1850001","DOI":"10.1142\/S0218001418500015","article-title":"Fault detection based on AP clustering and PCA","volume":"32","author":"Chen","year":"2018","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"P47","DOI":"10.1190\/1.2732553","article-title":"Application of image segmentation to tracking 3D salt boundaries","volume":"72","author":"Lomask","year":"2007","journal-title":"Geophysics"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Halpert, A.D., Clapp, R.G., and Biondi, B. (2009, January 25\u201330). Seismic image segmentation with multiple attributes. Proceedings of the 79th Society of Exploration Geophysicists International Exposition and Annual Meeting 2009, SEG 2009, Houston, TX, USA.","DOI":"10.1190\/1.3255637"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Al-Shuhail, A.A., Al-Dossary, S.A., and Mousa, W.A.-H. (2017). Seismic Data Interpretation Using Digital Image Processing, Wiley.","DOI":"10.1002\/9781119125594"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"A39","DOI":"10.1190\/geo2017-0524.1","article-title":"Unsupervised seismic facies analysis via deep convolutional autoencoders","volume":"83","author":"Qian","year":"2018","journal-title":"Geophysics"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Silvany, P., Machado, M., and de Tarzo, T. (2019, January 19\u201322). Prestack seismic facies prediction via deep convolutional autoencoders: An application to a turbidite reservoir. Proceedings of the 16th International Congress of the Brazilian Geophysical Society, Rio de Janeiro, Brazil.","DOI":"10.22564\/16cisbgf2019.218"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1190\/tle37080578.1","article-title":"Using generative adversarial networks to improve deep-learning fault interpretation networks","volume":"37","author":"Lu","year":"2018","journal-title":"Lead. Edge"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5336","DOI":"10.1109\/TGRS.2020.2963848","article-title":"Dimensionality reduction with enhanced hybrid-graph discriminant learning for hyperspectral image classification","volume":"58","author":"Luo","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5085","DOI":"10.1109\/TGRS.2020.3018879","article-title":"Few-shot hyperspectral image classification with unknown classes using multitask deep learning","volume":"59","author":"Liu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","unstructured":"Chopra, S., Lubo-Robles, D., and Marfurt, K. (2018). Explorer, AAPG."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1190\/tle37060451.1","article-title":"Successful leveraging of image processing and machine learning in seismic structural interpretation: A review","volume":"37","author":"Wang","year":"2018","journal-title":"Lead. Edge"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1190\/tle37070529.1","article-title":"Convolutional neural networks for automated seismic interpretation","volume":"37","author":"Waldeland","year":"2018","journal-title":"Lead. Edge"},{"key":"ref_31","unstructured":"Schlumberger Limited (1991). Log Interpretation Principles\/Applications, Schlumberger."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Scarselli, N., Adam, J., Chiarella, D., Roberts, D.G., Bally, A.W.B.T.-R.G., and Second, E.T. (2020). An introduction to seismic reflection data: Acquisition, processing and interpretation. Regional Geology and Tectonics, Elsevier.","DOI":"10.1016\/B978-0-444-64134-2.00035-3"},{"key":"ref_33","unstructured":"Liu, H., Motoda, H., Setiono, R., and Zhao, Z. (2010). Feature selection: An ever evolving frontier in data mining. Feature Selection in Data Mining, PMLR."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1080\/14786440109462720","article-title":"On lines and planes of closest fit to systems of points in space","volume":"2","author":"Pearson","year":"1901","journal-title":"Lond. Edinb. Dublin Philos. Mag. J. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lu, Y., Cohen, I., Zhou, X.S., and Tian, Q. (2007, January 24\u201329). Feature selection using principal feature analysis. Proceedings of the 15th ACM International Conference on Multimedia, New York, NY, USA.","DOI":"10.1145\/1291233.1291297"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kuhn, M., and Johnson, K. (2013). Applied Predictive Modeling, Springer.","DOI":"10.1007\/978-1-4614-6849-3"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1109\/TPAMI.2005.95","article-title":"Automated variable weighting in k-means type clustering","volume":"27","author":"Huang","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","unstructured":"Celecia, A., Gonz\u00e1lez, R., and Vellasco, M. (2016, January 2\u20134). Feature selection methods applied to motor imagery task classification. Proceedings of the LA-CCI 2016 Latin American Conference on Computational Intelligence, Cartagena, Colombia."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/S0146-664X(77)80011-7","article-title":"Image enhancement by histogram transformation","volume":"6","author":"Hummel","year":"1977","journal-title":"Comput Graph. Image Process."},{"key":"ref_40","first-page":"299","article-title":"The watershed transformation applied to image segmentation","volume":"6","author":"Beucher","year":"1991","journal-title":"Scanning Microsc. Int."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC Superpixels Compared to State-of-the-Art Superpixel Methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Fouad, S., Randell, D., Galton, A., Mehanna, H., and Landini, G. (2017, January 11\u201313). Unsupervised superpixel-based segmentation of histopathological images with consensus clustering. Proceedings of the Medical Image Understanding and Analysis, MIUA 2017, Edinburgh, UK.","DOI":"10.1007\/978-3-319-60964-5_67"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1007\/s13246-019-00735-8","article-title":"Influence of normalization and color features on super-pixel classification: Application to cytological image segmentation","volume":"42","author":"Bechar","year":"2019","journal-title":"Australas. Phys. Eng. Sci. Med."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Liu, F., Yin, Y., Yang, G., Dong, L., and Xi, X. (October, January 29). Finger vein recognition with superpixel-based features. Proceedings of the IJCB 2014\u20132014 IEEE\/IAPR International Joint Conference on Biometrics, Clearwater, FL, USA.","DOI":"10.1109\/BTAS.2014.6996232"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.patcog.2003.07.008","article-title":"Review of shape representation and description techniques","volume":"37","author":"Zhang","year":"2004","journal-title":"Pattern Recognit."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Tighe, J., and Lazebnik, S. (2010, January 5\u201311). SuperParsing: Scalable nonparametric image parsing with superpixels. Proceedings of the European Conference on Computer Vision, ECCV 2010, Heraklion, Crete, Greece.","DOI":"10.1007\/978-3-642-15555-0_26"},{"key":"ref_47","unstructured":"Gonz\u00e1lez, R.C., and Woods, R.E. (2008). Digital Image Processing, Prentice Hall."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Shi, J., Zhang, H., and Ray, N. (2009, January 7\u201310). Solidity based local threshold for oil sand image segmentation. Proceedings of the International Conference on Image Processing, ICIP, Cairo, Egypt.","DOI":"10.1109\/ICIP.2009.5414517"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1109\/TPAMI.2006.244","article-title":"Face description with local binary patterns: Application to face recognition","volume":"28","author":"Ahonen","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_51","unstructured":"Weldon, T.P., and Higgins, W.E. (1996, January 9). Design of multiple Gabor filters for texture segmentation. Proceedings of the 1996 IEEE International Conference on Acoustics Speech, and Signal Processing Conference Proceedings, Atlanta, GA, USA."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1598","DOI":"10.1016\/j.patrec.2011.01.004","article-title":"Face recognition using histograms of oriented gradients","volume":"32","author":"Bueno","year":"2011","journal-title":"Pattern Recognit. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Kaufman, L., and Rousseeuw, P.J. (1990). Finding Groups in Data, John Wiley & Sons Inc.","DOI":"10.1002\/9780470316801"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/BF00337288","article-title":"Self-organized formation of topologically correct feature maps","volume":"43","author":"Kohonen","year":"1982","journal-title":"Biol. Cybern."},{"key":"ref_56","unstructured":"MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Berkeley Symposium on Mathematical Statistics and Probability, University of California Press."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1093\/sysbio\/45.3.380","article-title":"The Probabilistic Basis of Jaccard\u2019s Index of Similarity","volume":"45","author":"Real","year":"1996","journal-title":"Syst. Biol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1080\/01621459.1971.10482356","article-title":"Objective criteria for the evaluation of clustering methods","volume":"66","author":"Rand","year":"1971","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.procs.2019.04.026","article-title":"Application and visualization of typical clustering algorithms in seismic data analysis","volume":"151","author":"Fan","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Meyer-Baese, A., Schmid, V.B.T.-P.R., and Second, E. (2014). Feature selection and extraction. Pattern Recognition and Signal Analysis in Medical Imaging, Academic Press. Chapter 2.","DOI":"10.1016\/B978-0-12-409545-8.00002-9"},{"key":"ref_61","unstructured":"Baldi, P. (2011, January 2). Autoencoders, unsupervised learning and deep architectures. Proceedings of the 2011 International Conference on Unsupervised and Transfer Learning Workshop, Washington, DC, USA."},{"key":"ref_62","unstructured":"Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., and Weinberger, K.Q. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems 27, Curran Associates, Inc."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/19\/6347\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:03:44Z","timestamp":1760166224000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/19\/6347"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,23]]},"references-count":62,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["s21196347"],"URL":"https:\/\/doi.org\/10.3390\/s21196347","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,23]]}}}