Google Earth Engine app using Sentinel 1 SAR and deep learning for ocean seep methane detection and monitoring

Abstract

We present a comprehensive methodological framework and application designed to enhance the processing capabilities of SAR imagery. Our approach utilizes cloud computing and deep learning techniques for the search, detection, and monitoring of hydrocarbon slicks on the ocean surface originating from subsea oil and gas sources. Our methodology, which specifically focuses on identifying and monitoring natural methane seeps, is based on an efficient semi-automatic approach and multi-temporal analysis. It has been tested in 6 locations of known floating oil slicks by natural methane seeps activity and two oil spills around the globe. Leveraging the capabilities of Google Earth Engine, our application allowed us to synergize the advantages and coverage of free Sentinel 2 imagery, the parallel processing power of GEE cloud, and the accuracy of deep learning algorithms to develop models for slick behavior under diverse climatic and hydrodynamic conditions. Our results may be useful to the hydrocarbon industry by reducing exploration and processing costs of remote prospecting data and deriving accurate and indirect estimates of the deposits.