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Deep learning for pockmark detection: Implications for quantitative seafloor characterization

November 11, 2022

Occurring globally, pockmarks are seafloor depressions associated with seabed fluid escape. Pockmark ubiquity and morphologic heterogeneity result in an irregular seafloor that can be difficult to quantitatively describe. To address this challenge, we test the hypothesis that deep-learning based object detection and segmentation can be used to develop data-driven models for pockmark identification and characterization. This study describes the development, testing, and deployment of eight separate deep learning-based pockmark detection models using publicly available, gridded bathymetric data from the Belfast Bay, Maine, USA, Blue Hill Bay, Maine, USA, and Passamaquoddy Bay, New Brunswick, Canada estuarine pockmark fields. The models tested include three types of convolutional neural network architectures, as well as a generative adversarial network. We find that the data-driven models consistently resolve pockmarks from the background seafloor, allowing for quick and accurate delineation of pockmarks in a variety of seabed habitats. With these delineations we examine and compare the morphology of the muddy estuarine pockmark fields. We then compare these morphometric results to pockmark fields in two distinct settings, the sandy German Bight and the Aquitaine continental slope. We find that in all the pockmark fields a power law relationship, generally, exists between pockmark area and pockmark depth, though this relationship deteriorates with the smallest pockmarks, suggesting that there may be a minimum size needed for geomorphic stability. These results show that the training data and trained models developed here can be applied for quick detection and characterization of pockmarks where other high-resolution bathymetry is available, demonstrating the value of data-driven detection models for characterizing morphologically complex seafloors. Last, the morphologic characteristics of pockmarks identified in this study will aid future studies in relating pockmark size to environmental characteristics like seabed sediment texture and regional gradient.

Citation Information

Publication Year 2023
Title Deep learning for pockmark detection: Implications for quantitative seafloor characterization
DOI 10.1016/j.geomorph.2022.108524
Authors Mark Lundine, Laura L. Brothers, Arthur Trembanis
Publication Type Article
Publication Subtype Journal Article
Series Title Geomorphology
Index ID 70238452
Record Source USGS Publications Warehouse
USGS Organization Woods Hole Coastal and Marine Science Center