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New Technologies, Methods and Studies for Seismic and Radar Subsurface...
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New Technologies, Methods and Studies for Seismic and Radar Subsurface Exploration

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 8433

Special Issue Editors

Dr. Filippo Carboni

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Guest Editor
Institute of Earth and Environmental Sciences, University of Freiburg, Freiburg, Germany
Interests: structural geology; seismic interpretation; remote sensing; planetary geology; 3D geological models
Dr. Maurizio Ercoli

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Guest Editor
Department of Physics and Geology, University of Perugia, Perugia, Italy
Interests: ground penetrating radar (GPR); seismic reflection; pre-conditioning techniques and seismic attribute analysis; seismic interpretation
Prof. Dr. Ramon Carbonell

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Guest Editor
GEO3BCN-CSIC, Barcelona, Spain
Interests: seismic imaging; multi-component seismic reflection data (normal incidence and wide-angle) from acquisition to interpretation and modelling; seismic wave propagation through complex media; inverse problems in seismics (such as resolution); subsurface multi-parameter model building through innovative approaches integrating multi-disciplinary geophysical data (including AI/ML)

Special Issue Information

Dear Colleagues,

Remote sensing surveys, such as seismic and radar techniques, have been employed for decades to unravel subsurface geology at different scales and levels of resolution on Earth. More recently, radar techniques have been used to study planetary bodies (i.e., Mars) via their inclusion in rovers and satellites. In recent years, research and industrial studies based on seismic and radar surveys have made great progress in theory, numerical simulations, experiments, and observations. Thanks to new technologies used to acquire, process/reprocess and interpret subsurface data, the resolution and overall efficiency, as well as the data quantity and quality, have drastically improved, reducing the logistic efforts and, thus, the environmental impact.

This Special Issue focuses on the results obtained from the development and application of new configurations, methods and technologies in seismic and radar exploration to enhance the interpretability of subsurface geological features on Earth and other planetary bodies. We welcome contributions that propose more exhaustive, integrated and detailed subsurface geological models for geological, environmental and energy exploration, geodynamics, earthquake, seismotectonics, etc., studies at various scales.

In this Special Issue, original research articles and reviews are welcome; potential research areas may include, but are not limited to, the following:

  • Passive and active seismic surveys;
  • Terrestrial, planetary analogues and planetary radar (and GPR) surveys;
  • Theory and numerical simulations;
  • Laboratory experiments;
  • Processing and re-processing;
  • Pre-conditioning techniques and attribute analysis, including AI tools;
  • Applications in geological and environmental studies and case histories.

We look forward to receiving your contributions; please feel free to share this call for papers with anyone you know who is interested in these topics.

Dr. Filippo Carboni
Dr. Maurizio Ercoli
Prof. Dr. Ramon Carbonell
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 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. Remote Sensing 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 2700 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

  • geological exploration
  • geophysics
  • seismic survey
  • radar surveys
  • seismic and radar processing and interpretation
  • planetary geology

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (8 papers)

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Research

43 pages, 20513 KiB  
Article
Ground-Truth-Free 3D Seismic Denoising Based on Diffusion Models: Achieving Effective Constraints Through Embedded Self-Supervised Noise Modeling
by Zhonghan Zhang, Guihe Qin, Yanhua Liang, Minghui Sun, Yingqing Wang and Jiaru Song
Remote Sens. 2025, 17(6), 1061; https://doi.org/10.3390/rs17061061 - 17 Mar 2025
Viewed by 385
Abstract
Three-dimensional (3D) seismic data, essential for revealing subsurface structures and exploring oil and gas resources, are often contaminated by noise with an unknown prior distribution. Existing denoising research faces great challenges due to the scarcity of ground truth and the difficulty in obtaining [...] Read more.
Three-dimensional (3D) seismic data, essential for revealing subsurface structures and exploring oil and gas resources, are often contaminated by noise with an unknown prior distribution. Existing denoising research faces great challenges due to the scarcity of ground truth and the difficulty in obtaining prior knowledge of noise distributions. Moreover, few algorithms are specifically designed to leverage the unique spatial structural information inherent in 3D seismic data, leading to inefficient utilization of this valuable information during denoising. To address these issues, we propose Self-Supervised Seismic Denoising using the Denoising Diffusion Probabilistic Model (SSDn-DDPM), an algorithm specifically tailored for 3D seismic data that utilizes diffusion generative models for self-supervised blind denoising. The algorithm begins with self-supervised modeling of seismic noise to estimate its distribution. Subsequently, spatial structural information of 3D seismic data is leveraged to improve the accuracy of noise distribution estimation. Furthermore, the algorithm integrates the noise distribution estimation network into the diffusion model to further guide and refine the sampling process, thereby optimizing computational complexity and improving detail representation. Finally, it performs self-supervised 3D seismic noise suppression using the diffusion probabilistic model. In the experimental section, we comprehensively compare the proposed algorithm with six different types of seismic denoising methods. Various comparative experiments demonstrate that the proposed algorithm achieves exceptional denoising performance on 3D seismic data, even without ground truth or any prior knowledge about the noise distribution. Full article
(This article belongs to the Special Issue New Technologies, Methods and Studies for Seismic and Radar Subsurface Exploration)
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13 pages, 2926 KiB  
Article
Detecting Lunar Subsurface Water Ice Using FMCW Ground Penetrating Radar: Numerical Analysis with Realistic Permittivity Variations
by Shunya Takekura, Hideaki Miyamoto and Makito Kobayashi
Remote Sens. 2025, 17(6), 1050; https://doi.org/10.3390/rs17061050 - 17 Mar 2025
Viewed by 610
Abstract
This study investigates the detectability of a putative layer of regolith containing water ice in the lunar polar regions using ground penetrating radar (GPR). Numerical simulations include realistic variations in the relative permittivity of the lunar regolith, considering both density and, for the [...] Read more.
This study investigates the detectability of a putative layer of regolith containing water ice in the lunar polar regions using ground penetrating radar (GPR). Numerical simulations include realistic variations in the relative permittivity of the lunar regolith, considering both density and, for the first time, the effects of temperature on permittivity profiles. We follow the case of previous theoretical studies of water migration, which suggest that water ice accumulates at depths ranging from a few centimeters to tens of centimeters, appropriate depths to explore using GPR. In particular, frequency-modulated continuous wave (FMCW) radar is well-suited for this purpose due to its high range resolution and robust signal-to-noise ratio. This study evaluates two scenarios for the presence of lunar water ice: (1) a layer of regolith containing water ice at a depth of 5 cm, with a thickness of 5 cm, and (2) a layer of regolith containing water ice at a depth of 20 cm, with a thickness of 10 cm. Our computational results show that FMCW GPR, equipped with a dynamic range of 90 dB, is capable of detecting reflections from the interfaces of these layers, even under conditions of low water ice content and using antennas with low directivity. In addition, optimized antenna offsets improve the resolution of the upper and lower interfaces, particularly when applied to the surface of ancient crater ejecta. This study highlights the critical importance of understanding subsurface density and temperature structures for the accurate detection of water-ice-bearing regolith layers. Full article
(This article belongs to the Special Issue New Technologies, Methods and Studies for Seismic and Radar Subsurface Exploration)
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20 pages, 57393 KiB  
Article
Seismic Interferometry for Single-Channel Data: A Promising Approach for Improved Offshore Wind Farm Evaluation
by Rui Wang, Bin Hu, Hairong Zhang, Peizhen Zhang, Canping Li and Fengying Chen
Remote Sens. 2025, 17(2), 325; https://doi.org/10.3390/rs17020325 - 17 Jan 2025
Viewed by 655
Abstract
Single-channel seismic (SCS) methods play a crucial role in offshore wind farm assessments, offering rapid and continuous imaging of the subsurface. Conventional SCS methods often fall short in resolution and signal completeness, leading to potential misinterpretations of geological structures. In this study, we [...] Read more.
Single-channel seismic (SCS) methods play a crucial role in offshore wind farm assessments, offering rapid and continuous imaging of the subsurface. Conventional SCS methods often fall short in resolution and signal completeness, leading to potential misinterpretations of geological structures. In this study, we propose the application of seismic interferometry as a powerful tool to address these challenges by utilizing multiple reflections that are usually considered as noise. First, we demonstrate the feasibility of using seismic interferometry to approximate the primary wavefield. Then, we evaluate a series of seismic interferometry applied in SCS data, including cross-correlation, deconvolution, and cross-coherence, and determine the most appropriate one for our purpose. Finally, by comparing and analyzing the differences in amplitude, continuity, time–frequency properties, etc., between conventional primary wavefield information and reconstructed primary wavefield information by seismic interferometry, it is proved that incorporating multiples as supplementary information through seismic interferometry significantly enhances data reliability and resolution. The introduction of seismic interferometry provides a more detailed and accurate geological assessment crucial for optimal site selection in offshore wind farm development. Full article
(This article belongs to the Special Issue New Technologies, Methods and Studies for Seismic and Radar Subsurface Exploration)
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19 pages, 5023 KiB  
Article
Effective First-Break Picking of Seismic Data Using Geometric Learning Methods
by Zhongyang Wen and Jinwen Ma
Remote Sens. 2025, 17(2), 232; https://doi.org/10.3390/rs17020232 - 10 Jan 2025
Viewed by 819
Abstract
Automatic first-break(FB) picking is a key task in seismic data processing, with numerous applications in the field. Over the past few years, both unsupervised and supervised learning algorithms have been applied to 2D seismic arrival time picking and obtained good picking results. In [...] Read more.
Automatic first-break(FB) picking is a key task in seismic data processing, with numerous applications in the field. Over the past few years, both unsupervised and supervised learning algorithms have been applied to 2D seismic arrival time picking and obtained good picking results. In this paper, we introduce a strategy of optimizing certain geometric properties of the target curve for first-break picking which can be implemented in both unsupervised and supervised learning modes. Specifically, in the case of unsupervised learning, we design an effective curve evolving algorithm according to the active contour(AC) image segmentation model, in which the length of the target curve and the fitting region energy are minimized together. It is interpretable, and its effectiveness and robustness are demonstrated by the experiments on real world seismic data. We further investigate three schemes of combining it with human interaction, which is shown to be highly useful in assisting data annotation or correcting picking errors. In the case of supervised learning especially for deep learning(DL) models, we add a curve loss term based on the target curve geometry of first-break picking to the typical loss function. It is demonstrated by various experiments that this curve regularized loss function can greatly enhance the picking quality. Full article
(This article belongs to the Special Issue New Technologies, Methods and Studies for Seismic and Radar Subsurface Exploration)
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15 pages, 12447 KiB  
Article
Iterative Separation of Blended Seismic Data in Shot Domain Using Deep Learning
by Liyun Ma, Liguo Han and Pan Zhang
Remote Sens. 2024, 16(22), 4167; https://doi.org/10.3390/rs16224167 - 8 Nov 2024
Viewed by 840
Abstract
Accurate deblending techniques are essential for the successful application of blended seismic acquisition. Deep-learning-based deblending methods typically begin by performing a pseudo-deblending operation on blended data, followed by further processing in either the common-shot domain or a non-common-shot domain. In this study, we [...] Read more.
Accurate deblending techniques are essential for the successful application of blended seismic acquisition. Deep-learning-based deblending methods typically begin by performing a pseudo-deblending operation on blended data, followed by further processing in either the common-shot domain or a non-common-shot domain. In this study, we propose an iterative deblending framework based on deep learning, which directly addresses the blended data in the shot domain, eliminating the need for pseudo-deblending and domain transformation. This framework is built around a unique architecture, termed WNETR, which derives its name from its W-shaped network structure that combines U-Net and Transformer. During testing, the trained WNETR is incorporated into the iterative framework to extract useful signals iteratively. Tests on synthetic data validate the effectiveness of the proposed deblending iterative framework. Full article
(This article belongs to the Special Issue New Technologies, Methods and Studies for Seismic and Radar Subsurface Exploration)
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16 pages, 6800 KiB  
Article
Seismic Imaging of the Arctic Subsea Permafrost Using a Least-Squares Reverse Time Migration Method
by Sumin Kim, Seung-Goo Kang, Yeonjin Choi, Jong-Kuk Hong and Joonyoung Kwak
Remote Sens. 2024, 16(18), 3425; https://doi.org/10.3390/rs16183425 - 14 Sep 2024
Viewed by 1258
Abstract
High-resolution seismic imaging allows for the better interpretation of subsurface geological structures. In this study, we employ least-squares reverse time migration (LSRTM) as a seismic imaging method to delineate the subsurface geological structures from the field dataset for understanding the status of Arctic [...] Read more.
High-resolution seismic imaging allows for the better interpretation of subsurface geological structures. In this study, we employ least-squares reverse time migration (LSRTM) as a seismic imaging method to delineate the subsurface geological structures from the field dataset for understanding the status of Arctic subsea permafrost structures, which is pertinent to global warming issues. The subsea permafrost structures in the Arctic continental shelf, located just below the seafloor at a shallow water depth, have an abnormally high P-wave velocity. These structural conditions create internal multiples and noise in seismic data, making it challenging to perform seismic imaging and construct a seismic P-wave velocity model using conventional methods. LSRTM offers a promising approach by addressing these challenges through linearized inverse problems, aiming to achieve high-resolution, subsurface imaging by optimizing the misfit between the predicted and the observed seismic data. Synthetic experiments, encompassing various subsea permafrost structures and seismic survey configurations, were conducted to investigate the feasibility of LSRTM for imaging the Arctic subsea permafrost from the acquired seismic field dataset, and the possibility of the seismic imaging of the subsea permafrost was confirmed through these synthetic numerical experiments. Furthermore, we applied the LSRTM method to the seismic data acquired in the Canadian Beaufort Sea (CBS) and generated a seismic image depicting the subsea permafrost structures in the Arctic region. Full article
(This article belongs to the Special Issue New Technologies, Methods and Studies for Seismic and Radar Subsurface Exploration)
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19 pages, 13105 KiB  
Article
Enhanced Offshore Wind Farm Geophysical Surveys: Shearlet-Sparse Regularization in Multi-Channel Predictive Deconvolution
by Yang Zhang, Deli Wang, Bin Hu, Junming Zhang, Xiangbo Gong and Yifei Chen
Remote Sens. 2024, 16(16), 2935; https://doi.org/10.3390/rs16162935 - 10 Aug 2024
Viewed by 1324
Abstract
This study introduces a novel multi-channel predictive deconvolution method enhanced by Shearlet-based sparse regularization, aimed at improving the accuracy and stability of subsurface seismic imaging, particularly in offshore wind farm site assessments. Traditional multi-channel predictive deconvolution techniques often struggle with noise interference, limiting [...] Read more.
This study introduces a novel multi-channel predictive deconvolution method enhanced by Shearlet-based sparse regularization, aimed at improving the accuracy and stability of subsurface seismic imaging, particularly in offshore wind farm site assessments. Traditional multi-channel predictive deconvolution techniques often struggle with noise interference, limiting their effectiveness. By integrating Shearlet transform into the multi-channel predictive framework, our approach leverages its directional and multiscale properties to enhance sparsity and directionality in seismic data representation. Tests on both synthetic and field data demonstrate that our method not only provides more accurate seismic images but also shows significant resilience to noise, compared to conventional methods. These findings suggest that the proposed technique can substantially improve geological feature identification and has great potential for enhancing the efficiency of seabed surveys in marine renewable energy development. Full article
(This article belongs to the Special Issue New Technologies, Methods and Studies for Seismic and Radar Subsurface Exploration)
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21 pages, 9028 KiB  
Article
Multi-Shot Simultaneous Deghosting for Virtual-Shot Gathers via Integrated Sparse and Nuclear Norm Constraint Inversion
by Junming Zhang, Deli Wang, Bin Hu, Xiangbo Gong, Yifei Chen and Yang Zhang
Remote Sens. 2024, 16(12), 2075; https://doi.org/10.3390/rs16122075 - 7 Jun 2024
Viewed by 1283
Abstract
Seismic interferometry is a key technology in geophysical exploration, having achieved significant developments in constructing virtual seismic responses, overcoming the limitation of traditional exploration. However, non-physical reflections in virtual-shot gathers pose challenges for data processing and interpretation. This study focuses on deghosting in [...] Read more.
Seismic interferometry is a key technology in geophysical exploration, having achieved significant developments in constructing virtual seismic responses, overcoming the limitation of traditional exploration. However, non-physical reflections in virtual-shot gathers pose challenges for data processing and interpretation. This study focuses on deghosting in virtual-shot gather data processing. We propose a novel method that integrates sparse and nuclear norm constraint inversion for multi-shot simultaneous deghosting. Initially, a pseudo 3D data cube is created to enhance computational efficiency and lay the foundation for subsequent continuity regularization. Subsequently, an inversion framework is constructed to improve deghosting precision and stability by combining sparse and nuclear norm constraint inversion. Both synthetic and field examples demonstrate the superiority of our method, offering a new paradigm for virtual-shot gather data processing, and representing a major advancement in overcoming the inherent limitations of seismic interferometry. Full article
(This article belongs to the Special Issue New Technologies, Methods and Studies for Seismic and Radar Subsurface Exploration)
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