Machine Learning Based Automated Detection of Seafloor Gas Seeps-Reference-Cited by-同舟云学术

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Machine Learning Based Automated Detection of Seafloor Gas Seeps

Published:2023-09-25 Issue: Volume: Page:
ISSN:
Container-title:OCEANS 2023 - MTS/IEEE U.S. Gulf Coast
language:
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Author:

Hicks Bruce¹,Ayna Cemre Omer¹,Senyurek Volkan²,Gupta Surabhi³^ORCID,Skarke Adam⁴,Gurbuz Ali Cafer¹

Affiliation:

1. Mississippi State University,Electrical and Computer Engineering,Starkville,MS,USA

2. Geosystems Research Institute, Mississippi State University,Starkville,MS,USA

3. Mississippi State University,Department of Wildlife, Fisheries, and Aquaculture,Starkville,MS,USA

4. Mississippi State University,Department of Geosciences,Starkville,MS

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10336884/10336949/10337038.pdf?arnumber=10337038

Reference18 articles.

1. The origins of shallow gas

2. Methane hydrates and contemporary climate change;Ruppel;Nature Eduction Knowledge,2011

3. Analysis of bubble plume distributions to evaluate methane hydrate decomposition on the continental slope

4. Widespread methane leakage from the sea floor on the northern US Atlantic margin

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