{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:30:58Z","timestamp":1760236258143,"version":"build-2065373602"},"reference-count":70,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T00:00:00Z","timestamp":1636761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Petr\u00f3leo Brasileiro S.A. (Petrobras)","award":["Cooperation Agreement 2017\/00777-6"],"award-info":[{"award-number":["Cooperation Agreement 2017\/00777-6"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Distinguishing between natural and anthropic oil slicks is a challenging task, especially in the Gulf of Mexico, where these events can be simultaneously observed and recognized as seeps or spills. In this study, a powerful data analysis provided by machine learning (ML) methods was employed to develop, test, and implement a classification model (CM) to distinguish an oil slick source (OSS) as natural or anthropic. A robust database containing 4916 validated oil samples, detected using synthetic aperture radar (SAR), was employed for this task. Six ML algorithms were evaluated, including artificial neural networks (ANN), random forest (RF), decision trees (DT), naive Bayes (NB), linear discriminant analysis (LDA), and logistic regression (LR). Using RF, the global CM achieved a maximum accuracy value of 73.15. An innovative approach evaluated how external factors, such as seasonality, satellite configurations, and the synergy between them, limit or improve OSS predictions. To accomplish this, specific classification models (SCMs) were derived from the global ones (CMs), tuning the best algorithms and parameters according to different scenarios. Median accuracies revealed winter and spring to be the best seasons and ScanSAR Narrow B (SCNB) as the best beam mode. The maximum median accuracy to distinguish seeps from spills was achieved in winter using SCNB (83.05). Among the tested algorithms, RF was the most robust, with a better performance in 81% of the investigated scenarios. The accuracy increment provided by the well-fitted models may minimize the confusion between seeps and spills. This represents a concrete contribution to reducing economic and geologic risks derived from exploration activities in offshore areas. Additionally, from an operational standpoint, specific models support specialists to select the best SAR products and seasons for new acquisitions, as well as to optimize performances according to the available data.<\/jats:p>","DOI":"10.3390\/rs13224568","type":"journal-article","created":{"date-parts":[[2021,11,14]],"date-time":"2021-11-14T20:51:53Z","timestamp":1636923113000},"page":"4568","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Improved Classification Models to Distinguish Natural from Anthropic Oil Slicks in the Gulf of Mexico: Seasonality and Radarsat-2 Beam Mode Effects under a Machine Learning Approach"],"prefix":"10.3390","volume":"13","author":[{"given":"\u00cdtalo","family":"de Oliveira Matias","sequence":"first","affiliation":[{"name":"Software Engineering Laboratory (LES), Department of Informatics, Pontifical Catholic University (PUC-Rio), 225, Marqu\u00eas de S\u00e3o Vicente Street, G\u00e1vea, Rio de Janeiro 22451-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8616-8645","authenticated-orcid":false,"given":"Patr\u00edcia Carneiro","family":"Genovez","sequence":"additional","affiliation":[{"name":"Software Engineering Laboratory (LES), Department of Informatics, Pontifical Catholic University (PUC-Rio), 225, Marqu\u00eas de S\u00e3o Vicente Street, G\u00e1vea, Rio de Janeiro 22451-900, Brazil"}]},{"given":"Sarah Barr\u00f3n","family":"Torres","sequence":"additional","affiliation":[{"name":"Software Engineering Laboratory (LES), Department of Informatics, Pontifical Catholic University (PUC-Rio), 225, Marqu\u00eas de S\u00e3o Vicente Street, G\u00e1vea, Rio de Janeiro 22451-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8506-3470","authenticated-orcid":false,"given":"Francisco F\u00e1bio","family":"de Ara\u00fajo Ponte","sequence":"additional","affiliation":[{"name":"Software Engineering Laboratory (LES), Department of Informatics, Pontifical Catholic University (PUC-Rio), 225, Marqu\u00eas de S\u00e3o Vicente Street, G\u00e1vea, Rio de Janeiro 22451-900, Brazil"}]},{"given":"Anderson Jos\u00e9 Silva","family":"de Oliveira","sequence":"additional","affiliation":[{"name":"Software Engineering Laboratory (LES), Department of Informatics, Pontifical Catholic University (PUC-Rio), 225, Marqu\u00eas de S\u00e3o Vicente Street, G\u00e1vea, Rio de Janeiro 22451-900, Brazil"}]},{"given":"Fernando Pellon","family":"de Miranda","sequence":"additional","affiliation":[{"name":"Petrobras Research and Development Center (CENPES), Av. Hor\u00e1cio Macedo 950, Cidade Universit\u00e1ria, Federal University of Rio de Janeiro, Rio de Janeiro 21941-915, Brazil"}]},{"given":"Gil M\u00e1rcio","family":"Avellino","sequence":"additional","affiliation":[{"name":"Petrobras Research and Development Center (CENPES), Av. Hor\u00e1cio Macedo 950, Cidade Universit\u00e1ria, Federal University of Rio de Janeiro, Rio de Janeiro 21941-915, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ward, C. (2017). Oil and Gas Seeps in the Gulf of Mexico. Habitats and Biota of the Gulf of Mexico: Before the Deepwater Horizon Oil Spill, Springer.","DOI":"10.1007\/978-1-4939-3447-8"},{"key":"ref_2","unstructured":"Committee on Oil in the Sea, and Divisions of Earth and Life Studies and Transportation Research Board, National Research Council (2003). 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