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Modeling and Waveform Inversion of Marine Seismic Data
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Modeling and Waveform Inversion of Marine Seismic Data

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: 5 February 2025 | Viewed by 1845

Special Issue 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 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
Assistant 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,

As marine resource exploration deepens, accurate seismic imaging and subsurface structure characterization have become critical. This Special Issue, “Modeling and Waveform Inversion of Marine Seismic Data”, focuses on the latest advances in marine seismic wave simulation and inversion theory and related research reviews, which are critical to marine seismic exploration. This Special Issue focuses on seismic wave forward modeling and various aspects of modeling geophysical parameters of subsurface media, including the finite difference method, the spectral element method, and other numerical techniques for the high-fidelity simulation of wave propagation and inversion, including tomography, full waveform inversion (FWI), artificial intelligence, and other inversion strategies that address challenges such as noise, computational efficiency, and nonlinearity. This Special Issue hopes to receive and publish a series of the latest research results, aiming to promote cooperation and stimulate further research in the field of marine seismic exploration. It is a valuable resource for geophysicists, researchers, and industry professionals who want to use seismic data to explore marine resources more accurately and efficiently.

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

Dr. Yangting Liu
Guest Editor Assistant

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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind 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 monthly 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

  • seismic forward modeling
  • waveform inversion
  • artificial intelligence
  • marine seismic data processing

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Published Papers (2 papers)

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Research

19 pages, 9233 KiB  
Article
Numerical Modeling on Ocean-Bottom Seismograph P-Wave Receiver Function to Analyze Influences of Seawater and Sedimentary Layers
by Wenfei Gong, Hao Hu, Aiguo Ruan, Xiongwei Niu, Wei Wang and Yong Tang
J. Mar. Sci. Eng. 2024, 12(11), 2053; https://doi.org/10.3390/jmse12112053 - 13 Nov 2024
Viewed by 593
Abstract
It is challenging to apply the receiver function method to teleseisms recorded by ocean-bottom seismographs (OBSs) due to a specific working environment that differs from land stations. Teleseismic incident waveforms reaching the area beneath stations are affected by multiple reflections generated by seawater [...] Read more.
It is challenging to apply the receiver function method to teleseisms recorded by ocean-bottom seismographs (OBSs) due to a specific working environment that differs from land stations. Teleseismic incident waveforms reaching the area beneath stations are affected by multiple reflections generated by seawater and sediments and noise resulting from currents. Furthermore, inadequate coupling between OBSs and the seabed basement and the poor fidelity of OBSs reduce the signal-to-noise ratio (SNR) of seismograms, leading to the poor quality of extracted receiver functions or even the wrong deconvolution results. For instance, the poor results cause strong ambiguities regarding the Moho depth. This study uses numerical modeling to analyze the influences of multiple reflections generated by seawater and sediments on H-kappa stacking and the neighborhood algorithm. Numerical modeling shows that seawater multiple reflections are mixed with the coda waves of the direct P-wave and slightly impact the extracted receiver functions and can thus be ignored in subsequent inversion processing. However, synthetic seismograms have strong responses to the sediments. Compared to the waveforms of horizontal and vertical components, the sedimentary responses are too strong to identify the converted waves clearly. The extracted receiver functions correspond to the above influences, resulting in divergent results of H-kappa stacking (i.e., the Moho depth and crustal average VP/VS ratio are unstable and have great uncertainties). Fortunately, waveform inversion approaches (e.g., the neighborhood algorithm) are available and valid for obtaining the S-wave velocity structure of the crust–upper mantle beneath the station, with sediments varying in thickness and velocity. Full article
(This article belongs to the Special Issue Modeling and Waveform Inversion of Marine Seismic Data)
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21 pages, 7259 KiB  
Article
Integrating Multimodal Deep Learning with Multipoint Statistics for 3D Crustal Modeling: A Case Study of the South China Sea
by Hengguang Liu, Shaohong Xia, Chaoyan Fan and Changrong Zhang
J. Mar. Sci. Eng. 2024, 12(11), 1907; https://doi.org/10.3390/jmse12111907 - 25 Oct 2024
Viewed by 798
Abstract
Constructing an accurate three-dimensional (3D) geological model is crucial for advancing our understanding of subsurface structures and their evolution, particularly in complex regions such as the South China Sea (SCS). This study introduces a novel approach that integrates multimodal deep learning with multipoint [...] Read more.
Constructing an accurate three-dimensional (3D) geological model is crucial for advancing our understanding of subsurface structures and their evolution, particularly in complex regions such as the South China Sea (SCS). This study introduces a novel approach that integrates multimodal deep learning with multipoint statistics (MPS) to develop a high-resolution 3D crustal P-wave velocity structure model of the SCS. Our method addresses the limitations of traditional algorithms in capturing non-stationary geological features and effectively incorporates heterogeneous data from multiple geophysical sources, including 44 wide-angle seismic crustal structure profiles obtained by ocean bottom seismometers (OBSs), gravity anomalies, magnetic anomalies, and topographic data. The proposed model is rigorously validated against existing methods such as Kriging interpolation and MPS alone, demonstrating superior performance in reconstructing both global and local spatial features of the crustal structure. The integration of diverse datasets significantly enhances the model’s accuracy, reducing errors and improving the alignment with known geological information. The resulting 3D model provides a detailed and reliable representation of the SCS crust, offering critical insights for studies on tectonic evolution, resource exploration, and geodynamic processes. This work highlights the potential of combining deep learning with geostatistical methods for geological modeling, providing a robust framework for future applications in geosciences. The flexibility of our approach also suggests its applicability to other regions and geological attributes, paving the way for more comprehensive and data-driven investigations of Earth’s subsurface. Full article
(This article belongs to the Special Issue Modeling and Waveform Inversion of Marine Seismic Data)
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