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Marine Geophysical Exploration in the Era of Artificial Intelligence: Data,...
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Marine Geophysical Exploration in the Era of Artificial Intelligence: Data, Mechanisms, and Future Trends

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: 10 September 2026 | Viewed by 263

Editors

Dr. Guoxin Chen

E-Mail Website
Guest Editor
Ocean College, Zhejiang University, Zhoushan, China
Interests: marine geophysics; seismic exploration; full waveform inversion; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chun-Feng Li

E-Mail Website
Guest Editor
Ocean College, Zhejiang University, Zhoushan, China
Interests: marine geology and geophysics; plate tectonics and geodynamics; earth evolution and its control on resources; disasters and the environment; geophysical signal acquisition, processing, inversion and interpretation; nonlinear methods and fractals in geoscience
Special Issues, Collections and Topics in MDPI journals
Dr. Yangting Liu

E-Mail Website
Guest Editor
First Institute of Oceanography, Ministry of Natural Resources, Qingdao, China
Interests: marine seismic exploration; prestack inversion; underwater sedimentary acoustics; machine learning

Special Issue Information

Dear Colleagues,

Ever since the successful publication of our first Special Issue, “Modeling and Waveform Inversion of Marine Seismic Data”, the field of marine geophysical exploration has been undergoing a profound transformation driven by artificial intelligence. With the robust rise of a new wave of technological revolution, artificial intelligence has injected fresh momentum into marine science and enabled a broader scientific perspective for this second Special Issue.

This second collection of papers, titled “Marine Geophysical Exploration in the Era of Artificial Intelligence: Data, Mechanisms, and Future Trends”, aims to systematically present innovative applications and cutting-edge explorations of artificial intelligence technologies across the entire chain of marine geophysical exploration. Artificial intelligence is not only reshaping traditional models of data acquisition and processing but also profoundly changing our ability to monitor the marine environment, detect marine geological hazards, and understand submarine geological structures.

This Special Issue focuses on (but is not limited to) the following areas:

  • Intelligent Data Acquisition and Processing: Exploring machine learning-based methods for optimizing marine geophysical data acquisition, noise suppression, signal reconstruction, and feature extraction to enhance data quality and acquisition efficiency.
  • Intelligent Marine Seismic Imaging and Inversion: Encouraging research on waveform inversion, seismic imaging, and tomography that integrates deep learning, utilizing data-driven approaches to overcome the limitations of traditional physical models concerning nonlinearity, computational efficiency, and multi-parameter coupling.
  • Intelligent Marine Environmental Monitoring: Focusing on applications of artificial intelligence in fields such as seismological oceanography, marine geological hazard early warning systems, active submarine fault detection, and gas hydrate identification, thereby improving dynamic monitoring and risk assessment capabilities for the marine environment.
  • Intelligent Identification of Submarine Geology and Resources: Including AI-based reservoir prediction, intelligent identification of mineral, oil, and gas resources, and multi-source geophysical data fusion interpretation, contributing to the advancement of precise marine resource exploration.

At the same time, we welcome submissions on innovations in conventional marine geophysical exploration methods, particularly research results demonstrating significant breakthroughs in data acquisition techniques, forward modeling algorithms, and the construction of inversion theory.

We hope this Special Issue will gather wisdom from around the globe to promote the deep integration of artificial intelligence and marine geophysical exploration, providing new ideas, new tools, and new directions for the future development of marine scientific research. We invite researchers and industry experts to contribute their work and jointly write a new chapter in intelligent marine exploration.

Dr. Guoxin Chen
Prof. Dr. Chun-Feng Li
Dr. Yangting Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence in marine geophysics
  • marine seismic exploration
  • intelligent marine seismic imaging and inversion
  • machine learning
  • submarine geology and resources

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Published Papers (1 paper)

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Research

34 pages, 3799 KB  
Article
Simulation of 2D Shallow-Sea Acoustic Fields Using a Physics-Informed Residual Network
by Ziyue Wang, Lingyi Cong, Luotao Zhang, Shuyue Liu and Xiaobo Zhang
J. Mar. Sci. Eng. 2026, 14(13), 1154; https://doi.org/10.3390/jmse14131154 (registering DOI) - 23 Jun 2026
Abstract
Acoustic propagation in stratified shallow seas is governed by finite-depth waveguiding, impedance contrasts at the seawater–seabed interface, and coupled space–time wave dynamics. Conventional numerical solvers are accurate but often require detailed environmental priors, mesh generation, and explicit time marching, increasing the cost of [...] Read more.
Acoustic propagation in stratified shallow seas is governed by finite-depth waveguiding, impedance contrasts at the seawater–seabed interface, and coupled space–time wave dynamics. Conventional numerical solvers are accurate but often require detailed environmental priors, mesh generation, and explicit time marching, increasing the cost of simulations involving complex boundaries or repeated evaluations. This study proposes a physics-informed residual network (ResNet-PINN) for continuous simulation of two-dimensional acoustic fields in shallow-sea stratified media. The framework embeds a variable-density, variable-sound-speed acoustic pressure wave equation, initial and boundary constraints, and interface-focused collocation into network training. A Gaussian initial wave packet and temporal gating are incorporated through the output transformation to improve early-time physical consistency. The model is validated against SPECFEM2D simulations and a stratified semi-analytical modal benchmark. The results show that it captures source-region spreading, main wavefront evolution, and transmission–reflection structures near the seawater–seabed interface at an equivalent frequency of approximately 477 Hz. Supplementary tests with sloping and arched interfaces and modified boundary conditions indicate adaptability to smooth interface variations. Overall, the framework provides a physically consistent neural network strategy for continuous shallow-sea acoustic field simulation and a complementary basis for future extensions to higher-frequency propagation, more complex environments, and dynamically varying ocean conditions. Full article
(This article belongs to the Special Issue Marine Geophysical Exploration in the Era of Artificial Intelligence: Data, Mechanisms, and Future Trends)
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