Morphological Processes and Evolution of Marine Geomorphology: Observations, Modeling and Applications

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Geological Oceanography".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 2393

Special Issue Editors


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Guest Editor
Key Laboratory of Submarine Geosciences, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China
Interests: underwater target detection; binocular vision; semi-global stereo matching; disparity map optimization; 3D Reconstruction

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Guest Editor
Jiangsu Key Laboratory of Coastal Ocean Resources Development and Environmental Security, Hohai University, Nanjing 210024, China
Interests: estuarine and coastal geomorphology; morphological evolution; morphodynamic observation and modelling

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Guest Editor
Shandong Provincial Key Laboratory of Marine Environment and Geological Engineering, College of Environmental Science and Engineering, Ocean University of China, Qingdao 266100, China
Interests: marine engineering geology; wave-seabed interactions; submarine sediment gravity flows; seafloor in-situ test and observation
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Special Issue Information

Dear Colleagues,

Submarine and coastal geomorphology play a fundamental role in revealing the basic characteristics, evolution, and dynamical processes of the seafloor, serving as a foundation for submarine geosciences. From shallow coasts to the deep sea, the seafloor exhibits a wide range of forms, reflecting the influence of a range of oceanographic, sedimentary, tectonic, and biological processes on different scales of time and space. To date, only 80% of the world’s seafloor has been mapped using modern sounding technology, leaving a vast majority unexplored. Fortunately, technological advancements have significantly enhanced our ability to explore and model these environments and processes with greater accuracy. Consequently, studying the shape, processes, and evolution of coastal and submarine geomorphology has become a top priority for academic and research institutions, government authorities, and industries worldwide.

In recent years, significant progress has been made in the acquisition, processing, and interpretation of seafloor data, as well as in the development of numerical modeling methodologies. These advancements have greatly improved our understanding of coastal and submarine geomorphology and the processes that shape them. Furthermore, the availability of high-resolution spatial and temporal data has led to the application of seafloor geomorphology products in various fields. This research topic aims to provide a venue for sharing our increased knowledge on the nature of morphologic processes and evolution of coastal and submarine geomorphology. Additionally, we aim to showcase the latest techniques in observation and modeling, as well as the diverse applications of seafloor geomorphology products.

Prof. Dr. Ziyin Wu
Prof. Dr. Zeng Zhou
Prof. Dr. Xiaolei Liu
Guest Editors

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Keywords

  • coastal and submarine geomorphic processes
  • coastal and submarine geomorphic evolution
  • seafloor observation techniques
  • advances in seafloor data analysis and mining
  • modelling coastal and submarine geomorphic processes
  • applied geomorphology and geohazard assessment

Published Papers (3 papers)

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Research

17 pages, 24700 KiB  
Article
Teleseismic Indication of Magmatic and Tectonic Activities at Slow- and Ultraslow-Spreading Ridges
by Kaixuan Yan, Jie Chen and Tao Zhang
J. Mar. Sci. Eng. 2024, 12(4), 605; https://doi.org/10.3390/jmse12040605 - 30 Mar 2024
Viewed by 475
Abstract
Magmatic and tectonic processes in the formation of oceanic lithosphere at slow–ultraslow-spreading mid-ocean ridges (MORs) are more complicated relative to faster-spreading ridges, as their melt flux is overall low, with highly spatial and temporal variations. Here, we use the teleseismic catalog of magnitudes [...] Read more.
Magmatic and tectonic processes in the formation of oceanic lithosphere at slow–ultraslow-spreading mid-ocean ridges (MORs) are more complicated relative to faster-spreading ridges, as their melt flux is overall low, with highly spatial and temporal variations. Here, we use the teleseismic catalog of magnitudes over 4 between 1995 and 2020 from the International Seismological Center to investigate the characteristics of magmatic and tectonic activities at the ultraslow-spreading Southwest Indian Ridge and Arctic Gakkel Ridge and the slow-spreading North Mid-Atlantic Ridge and Carlsberg Ridge (total length of 14,300 km). Using the single-link cluster analysis technique, we identify 78 seismic swarms (≥8 events), 877 sequences (2–7 events), and 3543 single events. Seismic swarms often occur near the volcanic center of second-order segments, presumably relating to relatively robust magmatism. By comparing the patterns of seismicity between ultraslow- and slow-spreading ridges, and between melt-rich and melt-poor regions of the Southwest Indian Ridge with distinct seafloor morphologies, we demonstrate that a lower spreading rate and a lower melt supply correspond to a higher seismicity rate and a higher potential of large volcano-induced seismic swarms, probably due to a thicker and colder lithosphere with a higher degree of along-axis melt focusing there. Full article
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19 pages, 35252 KiB  
Article
Erosional and Depositional Features along the Axis of a Canyon in the Northern South China Sea and Their Implications: Insights from High-Resolution AUV-Based Geophysical Data
by Xishuang Li, Lejun Liu, Bigui Huang, Qingjie Zhou and Chengyi Zhang
J. Mar. Sci. Eng. 2024, 12(4), 599; https://doi.org/10.3390/jmse12040599 - 30 Mar 2024
Viewed by 450
Abstract
Autonomous Underwater Vehicle (AUV)-based multibeam bathymetry, sub-bottom profiles, and side-scan sonar images were collected in 2009 and 2010 to map the geomorphic features along the axial zone of a canyon (referred to as C4) within the canyon system developed on the northern slope [...] Read more.
Autonomous Underwater Vehicle (AUV)-based multibeam bathymetry, sub-bottom profiles, and side-scan sonar images were collected in 2009 and 2010 to map the geomorphic features along the axial zone of a canyon (referred to as C4) within the canyon system developed on the northern slope of the South China Sea. These data significantly improved the spatial resolution of acoustic data, leading to a better understanding of the sedimentary processes within the modern canyon system. The bathymetric data reveal that sections across the canyon axis exhibit either asymmetrical or symmetrical characteristics, which differ from the overall asymmetrical pattern of the entire canyon. This suggests that the overall asymmetrical pattern of the canyon is not primarily due to axial incision. Various morphological elements were identified along the canyon axis, including failure scars, undulating features, knickpoints, flat terraces, furrows, and mass transport deposits (MTDs). Landslides, predominantly located in the upper canyon, were formed after at least 5000 years BP. Between the beginning of the canyon and a water depth of approximately 1300 m, there are alternating flat terraces and knickpoints. The large knickpoints’ low slope gradients are likely formed by the presence of undulating features. The internal configurations of undulating features suggest that they are depositional structures rather than sediment deformation. The formation of small-scale furrows below a depth of 1200 m may be associated with occasional gravity flows down the canyon. It is suggested that the canyon was generally inactive during the Holocene but experienced sporadic processes of sediment erosion, transport, and re-deposition in the axial zone that were triggered by landslide events occasionally in the upper canyon. Full article
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12 pages, 8148 KiB  
Article
Multi-Module Fusion Model for Submarine Pipeline Identification Based on YOLOv5
by Bochen Duan, Shengping Wang, Changlong Luo and Zhigao Chen
J. Mar. Sci. Eng. 2024, 12(3), 451; https://doi.org/10.3390/jmse12030451 - 03 Mar 2024
Viewed by 772
Abstract
In recent years, the surge in marine activities has increased the frequency of submarine pipeline failures. Detecting and identifying the buried conditions of submarine pipelines has become critical. Sub-bottom profilers (SBPs) are widely employed for pipeline detection, yet manual data interpretation hampers efficiency. [...] Read more.
In recent years, the surge in marine activities has increased the frequency of submarine pipeline failures. Detecting and identifying the buried conditions of submarine pipelines has become critical. Sub-bottom profilers (SBPs) are widely employed for pipeline detection, yet manual data interpretation hampers efficiency. The present study proposes an automated detection method for submarine pipelines using deep learning models. The approach enhances the YOLOv5s model by integrating Squeeze and Excitation Networks (SE-Net) and S2-MLPv2 attention modules into the backbone network structure. The Slicing Aided Hyper Inference (SAHI) module is subsequently introduced to recognize original large-image data. Experimental results conducted in the Yellow Sea region demonstrate that the refined model achieves a precision of 82.5%, recall of 99.2%, and harmonic mean (F1 score) of 90.0% on actual submarine pipeline data detected using an SBP. These results demonstrate the efficiency of the proposed method and applicability in real-world scenarios. Full article
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