Advancements in Marine Geology: Underwater Exploration and Geophysical Insights

A special issue of Geosciences (ISSN 2076-3263).

Deadline for manuscript submissions: 1 June 2026 | Viewed by 559

Special Issue Editors


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Guest Editor
Istituto Nazionale di Geofisica e Vulcanologia, Via di Vigna Murata 605, ROME C/o via Santa Teresa, 19032 Lerici, Italy
Interests: marine geology; gravity; magnetics; plate tectonics; seamounts

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Guest Editor
Istituto Nazionale di Geofisica e Vulcanologia, Via di vigna murata 605, ROME C/o via Santa Teresa, 19032 Lerici, Italy
Interests: marine geophysics; hydorthermal fileds; volcanic seamounts; back arc basins; seafloor spreading; bathymetry; volcanic plumbing system
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Guest Editor
CNR—ISMAR—National Research Council (CNR), Institute of Marine Sciences (ISMAR), Via Piero Gobetti 101, 40129, Bologna, Italy
Interests: bathymetry; geomorphology; marine geology; oceanic geodiversity; seafloor spreading
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Our planet is predominantly covered by water, with over 70% of its surface submerged, of which 90% is seawater. The oceans hide key geological features resulting from complex Earth processes, such as tectonic plate divergence and associated volcanism. Exploring the seafloor is essential for understanding Earth's internal dynamics, a field still in its early stages. Since the 1960s, advances in technology have progressively refined our knowledge of ocean floor morphology and tectonics. Modern investigative techniques based on remotely operated and autonomous vehicles, combined with large-scale geophysical mapping (including multibeam bathymetry, satellite altimetry, magnetics, gravity, and seismic imaging), now provide unprecedented insights into oceanic geomorphology and its underlying processes. Additionally, a deeper understanding of the oceanic environment is crucial for biodiversity conservation, directly impacting the health of our planet.

We invite research contributions across a wide spectrum of marine geoscience. Topics of interest include deep-ocean studies on seafloor morphology, tectonics, and plate interactions (convergence and divergence), as well as investigations of hydrothermal systems and their biological associations. We also encourage studies on numerical modeling and kinematics that link internal Earth processes with surface dynamics. Contributions that employ multidisciplinary approaches and innovative survey techniques are particularly welcome.

Dr. Filippo Muccini
Dr. Luca Cocchi
Dr. Camilla Palmiotto
Guest Editors

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Keywords

  • marine geology
  • seafloor spreading
  • hydrothermal system
  • submarine volcanism
  • plate tectonics
  • remotely operated vehicle
  • autonomous underwater vehicle
  • ocean geodiversity

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Published Papers (1 paper)

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21 pages, 20939 KiB  
Article
Identification and Application of Preferred Seepage Channels in Turbidite Lobe Reservoirs of Formation A in Z Oilfield
by Changhai Li
Geosciences 2025, 15(9), 328; https://doi.org/10.3390/geosciences15090328 (registering DOI) - 23 Aug 2025
Abstract
Turbidite lobe reservoirs represent critical deep-sea hydrocarbon targets, yet preferred seepage channels within them remain poorly characterized. This paper establishes a method for identifying internal preferred seepage channels in turbidite lobe reservoirs using data including seismic, core, thin section, logging, and production performance, [...] Read more.
Turbidite lobe reservoirs represent critical deep-sea hydrocarbon targets, yet preferred seepage channels within them remain poorly characterized. This paper establishes a method for identifying internal preferred seepage channels in turbidite lobe reservoirs using data including seismic, core, thin section, logging, and production performance, combined with neural network technology. A neural network model for predicting reservoir productivity types can be obtained by taking the average logging data of reservoir intervals as input and the reservoir productivity types categorized by meter oil production index calculated by actual production data as the target. By applying the trained neural network model and inputting actual logging attribute model, the reservoir productivity types of single wells are obtained. Using the attribute model of natural gamma ray, acoustic, neutron, density, deep lateral, and shallow lateral logs, which are built by using the actual logging data and Sequential Gaussian Simulation, and supervising with the single well reservoir productivity type, the reservoir productivity type at any position in the reservoir can be predicted. It predicts their spatial distribution characteristics, reveals the genetic mechanism of preferred seepage channels, and discusses the significance of identifying preferred seepage channels for oilfield development. The results show that the reservoir productivity types in the study area can be divided into five categories with progressive improvement in productivity (A, B, C, D, and E) according to the increase in oil production index per meter, among which Type E reservoirs represent typical preferred seepage channels. The attribute model of reservoir productivity types indicates that, horizontally, types E and B are locally developed in the study area, while types D, C, and A are widely distributed. The preferred seepage channels can be divided into two types according to the shape: zonal (length to width > 2:1) and sheet-like (length to width ≤ 2:1). Vertically, types C, D, and E are relatively well-developed in layers III and IV, whereas types A and B are more common in layers I and II. The vertical combination patterns of preferred seepage channels reveal four types, including homogeneous, bottom-dominated, top-dominated, and interbedded patterns. The formation of preferred seepage channels is influenced by both sedimentary and diagenetic processes, and sedimentary is the most important controlling factors. The identification of preferred seepage channels in turbidite lobe reservoirs is of great significance for formulating development policies and tapping remaining oil. Full article
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