Automated Detection of Seafloor Gas Seeps in Multibeam Echosounder Data With an Attention-Guided Convolutional Neural Network

Automated Detection of Seafloor Gas Seeps in Multibeam Echosounder Data With an Attention-Guided Convolutional Neural Network

Seafloor gas seeps are a globally distributed feature that hold substantial implications for ocean carbon cycling, chemosynthetic ecology, energy production, and geohazards. Detection of seafloor gas seeps currently requires human visual interpretation of sonar water column imagery by a trained indi...

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Bibliographic Details
Main Authors: Sultan Mohammad Manjur, Volkan Senyurek, Ramon Kalski, Surabhi Gupta, Adam Skarke, Ali C. Gurbuz
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Convolutional block attention module (CBAM)
convolutional neural network
gas seep
machine learning (ML)
multibeam echosounder (MBES)
sonar
Online Access:https://ieeexplore.ieee.org/document/10856333/

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https://ieeexplore.ieee.org/document/10856333/

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