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

Abstract

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.

Development and Application of Predictive Models to Distinguish Seepage Slicks from Oil Spills on Sea Surfaces Employing SAR Sensors and Artificial Intelligence: Geometric Patterns Recognition under a Transfer Learning Approach

“…They are commonly used along with radiometric and textural features to distinguish oil slicks from false alarms [34,38,[62][63][64], or to extract dark spots from the ocean [35,38,39]. However, their use to discriminate seepage slicks from oil spills is recent [2,22,[42][43][44].…”

“…Particularly, the use of SAR data to detect likely seepage slicks on sea surfaces represents an important instrument for reducing exploratory risk [4,5,22], as well as for protecting petroleum companies against penalties by events, not human-induced [22]. The location and persistence of detected slicks can be correlated with inverse oil drifting models [23,24], 3D-seismic and other remotely sensed geophysical data increasing confidence and strengthening evidence of active petroleum systems.…”

“…Historically, the architecture of these systems uses as a basis the discriminatory potential [18,[33][34][35][36][37][38][39][40][41] of different radiometric, textural, polarimetric, geometric, and/or contextual handcrafted features extracted from SAR data, following four usual steps [18,37]: (i) SAR pre-processing; (ii) dark spot detection [35,38,39]; (iii) feature extraction and selection [33][34][35][36]; and (iv) oil slick classification. However, the versatility of ML to recognize patterns and learn meaningful and consistent information for predicting the oil slick source (OSS) as natural or anthropic is under investigation [2,22,[42][43][44], consolidating a new and important research topic.…”

“…Furthermore, a critical point in intersectoral Research, Development, and Innovation (RD&I) projects integrating academia and the private sector is to generate positive returns on the investments carried out to develop ML systems [49]. In this sense, this project goes further by foreseeing a broader architecture designed for the operational deployment in the Petrobras proprietary software named GeoqView [22,43,44]. Under a ground-breaking view, the migration from the developer to the production environment combines the datadriven and knowledge-driven approaches, using automatic inferences to strengthen the experts' interpretation when searching for new exploratory frontiers.…”

Log Transformed Coherency Matrix for Differentiating Scattering Behaviour of Oil Spill Emulsions Using SAR Images

“…for oil spill detection and discrimination for look alikes [10,31,36,37]. The researcher in [38] evaluated the impact of 6 machine learning approaches such as ANN, RF, Decision Tree, Navie Bayes, LDA, Logistic Regression for effective oil spill detection and develops an algorithm for prediction of the best season for image acquisition of oil spill for Gulf of Mexico region. Among all traditional approaches, ANN and SVM have been majorly used by researchers for oil spill detection and characterization.…”