Machine Learning Based Automated Detection of Seafloor Gas Seeps

Hicks, B., Ayna, C. O., Senyurek, V., Gupta, S., Skarke, A., & Gurbuz, A. (2023). Machine Learning Based Automated Detection of Seafloor Gas Seeps. OCEANS 2023 - MTS/IEEE U.S. Gulf Coast. Biloxi, MS, USA: IEEE. 1-6. DOI:10.23919/OCEANS52994.2023.10337038.

Abstract

Generating a comprehensive understanding of the locations and distribution of seafloor gas seeps is a task that has become increasingly of interest to marine research organizations and industries. Currently, the feasible scale of mapping efforts is limited by a dependence on manual inspection of water column sonar images by trained experts to detect the presence of seafloor seeps, a time-consuming, costly, and inconsistent procedure. Here, we explore the mitigation of this constraint by creating a sonar dataset labeled for seep presence and using it to develop a machine learning-based method for automatic seafloor seep detection. The presented convolutional neural network model achieves an average accuracy of more than 95% in 5-fold cross-validation.