Transfer learning reconstructs submarine topography for global mid-ocean ridges Article Swipe
Yinghui Jiang
,
Sijin Li
,
Yanzi Yan
,
Bingqing Sun
,
Josef Strobl
,
Liyang Xiong
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.jag.2024.104182
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.jag.2024.104182
Mid-ocean ridges are unique, tectonically active geographical units on Earth that profoundly control the ocean environment and dynamics at the global scale. However, high-resolution topographic data from mid-ocean ridges are rarely available due to the difficulty in detecting ocean floors, which further limits ocean research at the global scale. Here, we divide the global mid-ocean ridge system into 2805 tiles and reconstruct their high-resolution topography by using a transfer learning approach with freely available low-resolution digital elevation models (DEMs) and limited high-resolution DEMs. A high-frequency terrain feature-based deep residual network is proposed to generate high-resolution global mid-ocean ridge DEMs. In this network, topographic knowledge related to mid-ocean ridges is integrated and quantified to improve the learning efficiency and reconstruction quality of the network. A series of verifications and evaluations demonstrate the reliability of reconstructed topographies for submarine topography research. We observe that reconstructed topography can achieve good environmental understanding and information acquisition in the global mid-ocean ridge range. We find that the complexity of the previous terrain environment is underestimated by 26.63% in terms of the slope gradient and by 14.95% in terms of terrain relief, while a 101.10% information improvement can be obtained for the reconstructed topography. The reconstructed topography indicates that diverse and intricate topographical environments of mid-ocean ridges exist among different ocean regions. The proposed transfer learning method for reconstructing high-resolution mid-ocean ridge topographies is valuable and can be utilized for reconstructing information in regions that are difficult to observe directly and lack sufficient data.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.jag.2024.104182
- OA Status
- gold
- Cited By
- 1
- References
- 49
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https://openalex.org/W4402946169Canonical identifier for this work in OpenAlex
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https://doi.org/10.1016/j.jag.2024.104182Digital Object Identifier
- Title
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Transfer learning reconstructs submarine topography for global mid-ocean ridgesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
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2024-09-29Full publication date if available
- Authors
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Yinghui Jiang, Sijin Li, Yanzi Yan, Bingqing Sun, Josef Strobl, Liyang XiongList of authors in order
- Landing page
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https://doi.org/10.1016/j.jag.2024.104182Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.jag.2024.104182Direct OA link when available
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Submarine, Geology, Geography, Oceanography, SeismologyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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49Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.unique, | 3 |
| abstract_inverted_index.However, | 22 |
| abstract_inverted_index.approach | 70 |
| abstract_inverted_index.directly | 243 |
| abstract_inverted_index.dynamics | 17 |
| abstract_inverted_index.generate | 93 |
| abstract_inverted_index.gradient | 177 |
| abstract_inverted_index.learning | 69, 115, 219 |
| abstract_inverted_index.network, | 101 |
| abstract_inverted_index.network. | 122 |
| abstract_inverted_index.obtained | 193 |
| abstract_inverted_index.previous | 165 |
| abstract_inverted_index.proposed | 91, 217 |
| abstract_inverted_index.regions. | 215 |
| abstract_inverted_index.research | 44 |
| abstract_inverted_index.residual | 88 |
| abstract_inverted_index.transfer | 68, 218 |
| abstract_inverted_index.utilized | 232 |
| abstract_inverted_index.valuable | 228 |
| abstract_inverted_index.Mid-ocean | 0 |
| abstract_inverted_index.available | 31, 73 |
| abstract_inverted_index.detecting | 37 |
| abstract_inverted_index.different | 213 |
| abstract_inverted_index.difficult | 240 |
| abstract_inverted_index.elevation | 76 |
| abstract_inverted_index.indicates | 201 |
| abstract_inverted_index.intricate | 205 |
| abstract_inverted_index.knowledge | 103 |
| abstract_inverted_index.mid-ocean | 27, 54, 96, 106, 155, 209, 224 |
| abstract_inverted_index.research. | 138 |
| abstract_inverted_index.submarine | 136 |
| abstract_inverted_index.complexity | 162 |
| abstract_inverted_index.difficulty | 35 |
| abstract_inverted_index.efficiency | 116 |
| abstract_inverted_index.integrated | 109 |
| abstract_inverted_index.profoundly | 11 |
| abstract_inverted_index.quantified | 111 |
| abstract_inverted_index.sufficient | 246 |
| abstract_inverted_index.topography | 64, 137, 143, 200 |
| abstract_inverted_index.acquisition | 151 |
| abstract_inverted_index.demonstrate | 129 |
| abstract_inverted_index.environment | 15, 167 |
| abstract_inverted_index.evaluations | 128 |
| abstract_inverted_index.improvement | 190 |
| abstract_inverted_index.information | 150, 189, 235 |
| abstract_inverted_index.reconstruct | 61 |
| abstract_inverted_index.reliability | 131 |
| abstract_inverted_index.topographic | 24, 102 |
| abstract_inverted_index.topography. | 197 |
| abstract_inverted_index.environments | 207 |
| abstract_inverted_index.geographical | 6 |
| abstract_inverted_index.tectonically | 4 |
| abstract_inverted_index.topographies | 134, 226 |
| abstract_inverted_index.environmental | 147 |
| abstract_inverted_index.feature-based | 86 |
| abstract_inverted_index.reconstructed | 133, 142, 196, 199 |
| abstract_inverted_index.topographical | 206 |
| abstract_inverted_index.understanding | 148 |
| abstract_inverted_index.verifications | 126 |
| abstract_inverted_index.high-frequency | 84 |
| abstract_inverted_index.low-resolution | 74 |
| abstract_inverted_index.reconstructing | 222, 234 |
| abstract_inverted_index.reconstruction | 118 |
| abstract_inverted_index.underestimated | 169 |
| abstract_inverted_index.high-resolution | 23, 63, 81, 94, 223 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/14 |
| sustainable_development_goals[0].score | 0.5099999904632568 |
| sustainable_development_goals[0].display_name | Life below water |
| citation_normalized_percentile.value | 0.8255068 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |