Deep Learning-Based Sequential Processing of Multibeam Echosounder Images for Automated Detection of Seafloor Gas Seep Occurrence

Seafloor gas seeps, primarily methane, play a crucial role in the global carbon cycle, influencing ocean chemistry, biodiversity, and geohazards while signaling potential natural gas reserves. Detecting and monitoring these seeps over time is crucial to understanding underwater resources and their environmental impacts. However, current detection methods rely heavily on human interpretation of water column imagery, limiting efficiency and scalability of the detection process. This study proposes a supervised machine learning (ML) framework that mimics human visual interpretation by analyzing sequences of multibeam echosounder (MBES) water column data, enabling automated seep detection by capturing temporal patterns across frames. Instead of processing only single frames, ML-based detection is developed on a sequence of MBES frames at a time. The model is evaluated on MBES sequences of varying lengths and confidence levels and its performance is compared with single-frame-based detection. Although both approaches perform similarly on low-confidence seep sequences, the sequential model demonstrates a clear advantage in high-confidence cases. Sequential analysis reduces the high false positive rate of the single-frame method by improving precision from 66% to 78%, maintains a more balanced recall of 71%, and achieves a higher overall accuracy of 87%. This approach offers a more robust and reliable seafloor gas seep detection system compared to single-frame-based analysis, highlighting the benefits of leveraging temporal context in water column data.