Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Seismic Data Processing and Imaging

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: closed (20 August 2024) | Viewed by 17171

Share This Special Issue

Special Issue Editor

Prof. Dr. Yoshiyuki Kaneda

E-Mail Website
Guest Editor

Institute of Education, Research and Regional Corporation for Crisis Management Shikoku (IECMS), Kagawa University, Takamatsu 760-0016, Kagawa, Japan
Interests: resilient science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are delighted to announce a Special Issue of Applied Sciences dedicated to the cutting-edge field of Seismic Data Processing and Imaging. Seismic exploration has long been a cornerstone of subsurface characterization, playing a pivotal role in energy exploration, seismological/geological research, and environmental monitoring. As technology continues to advance, this Special Issue aims to showcase the latest advancements, challenges, and breakthroughs in the processing and imaging of seismic data.

Seismic data processing and imaging are vital components in transforming raw seismic measurements into accurate and interpretable subsurface images. The contributions to this Special Issue will address a wide spectrum of topics, including, but not limited to, seismic data acquisition, noise reduction and denoising techniques, migration algorithms, full waveform inversion, anisotropic media imaging, machine learning applications in seismic imaging, and the integration of various data sources for more accurate subsurface characterization.

We invite researchers, scholars, and experts to contribute their original research, case studies, and reviews to provide a comprehensive overview of the state-of-the-art in seismic data processing and imaging. This Special Issue will serve as a platform via which to share their insights, methodologies, and experiences, fostering collaboration and knowledge exchange within the seismic exploration community.

Prof. Dr. Yoshiyuki Kaneda
Guest Editor

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. Applied Sciences 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 2400 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 data
data processing
imaging
exploration
subsurface characterization
signal processing
seismogenic zone
DAS
AI

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (11 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

14 pages, 30591 KiB

Open AccessArticle

Accurate Pseudo-Spectral Acoustic Wave Modelling with Time Dispersion Elimination

by Huahui Zeng, Yanxiang Wang, Yang Zhou, Huijie Meng, Qigang Zhou and Baozhong Jin

Appl. Sci. 2024, 14(19), 8725; https://doi.org/10.3390/app14198725 - 27 Sep 2024

Viewed by 882

Abstract

We propose an accurate method for modeling acoustic wave propagation. The spatial derivatives are calculated using Fourier transform to reduce spatial numerical dispersion. The standard staggered grid is adopted to suppress the non-causal ringing artifacts as in the traditional pseudo-spectral method. Moreover, to eliminate time dispersion arising from the discretization of the time derivative, an additional time-dispersion elimination term is introduced. As a result, the present method not only retains the advantages of the conventional pseudo-spectral method such as coarser spatial sampling or higher spatial derivative approximation accuracy, but also achieves higher temporal derivative approximation accuracy due to the adoption of the additional time-dispersion elimination term. Numerical examples demonstrate that the temporal dispersion elimination process can be contaminated by spatial numerical dispersion. Thus, the temporal and spatial numerical dispersions should be handled simultaneously, as proposed in this paper, to achieve accurate acoustic simulation. Full article

(This article belongs to the Special Issue Seismic Data Processing and Imaging)

► Show Figures

Figure 1

17 pages, 8700 KiB

Open AccessArticle

Microzonation Approach for Analyzing Regional Seismic Response: A Case Study of the Dune Deposit in Concón, Chile

by Edgar Giovanny Diaz-Segura, Juan Carlos Vielma and Jorge Eduardo Oviedo-Veas

Appl. Sci. 2024, 14(18), 8458; https://doi.org/10.3390/app14188458 - 19 Sep 2024

Viewed by 1129

Abstract

Urban areas located on complex geological formations, such as dune deposits, require detailed seismic risk assessments that extend beyond standard seismic codes. This study focuses on the city of Concón, Chile, where a significant portion of the urban area is situated on a coastal dune deposit. The research integrates seismic microzonation with a three-dimensional finite element model (3D FEM) to comprehensively evaluate the regional seismic response. Field data from 208 strategically distributed points were collected and combined with geotechnical and geomorphological information to construct a detailed 3D model of the region. This model allowed for the simulation of seismic behavior under various conditions, highlighting the limitations of general seismic codes in capturing local variations in seismic response. The results underscore the importance of considering local geological conditions in structural design, particularly in areas with irregular topography and complex subsurface conditions. This study concludes that incorporating microzonation into urban planning and seismic analysis can significantly enhance infrastructure resilience and disaster preparedness, providing a replicable approach for other cities facing similar geological challenges. Full article

(This article belongs to the Special Issue Seismic Data Processing and Imaging)

► Show Figures

Figure 1

29 pages, 32138 KiB

Open AccessArticle

Seismic Identification and Characterization of Deep Strike-Slip Faults in the Tarim Craton Basin

by Fei Tian, Wenhao Zheng, Aosai Zhao, Jingyue Liu, Yunchen Liu, Hui Zhou and Wenjing Cao

Appl. Sci. 2024, 14(18), 8235; https://doi.org/10.3390/app14188235 - 12 Sep 2024

Cited by 2 | Viewed by 1483

Abstract

Through hydrocarbon explorations, deep carbonate reservoirs within a craton were determined to be influenced by deep strike-slip faults, which exhibit small displacements and are challenging to identify. Previous research has established a correlation between seismic attributes and deep geological information, wherein large-scale faults can cause abrupt waveform discontinuities. However, due to the inherent limitations of seismic datasets, such as low signal-to-noise ratios and resolutions, accurately characterizing complex strike-slip faults remains difficult, resulting in increased uncertainties in fault characterization and reservoir prediction. In this study, we integrate advanced techniques such as principal component analysis and structure-oriented filtering with a fault-centric imaging approach to refine the resolution of seismic data from the Tarim craton. Our detailed evaluation encompassed 12 distinct seismic attributes, culminating in the creation of a sophisticated model for identifying strike-slip faults. This model incorporates select seismic attributes and leverages fusion algorithms like K-means, ellipsoid growth, and wavelet transformations. Through the technical approach introduced in this study, we have achieved multi-scale characterization of complex strike-slip faults with throws of less than 10 m. This workflow has the potential to be extended to other complex reservoirs governed by strike-slip faults in cratonic basins, thus offering valuable insights for hydrocarbon exploration and reservoir characterization in similar geological settings. Full article

(This article belongs to the Special Issue Seismic Data Processing and Imaging)

► Show Figures

Figure 1

17 pages, 19811 KiB

Open AccessArticle

Efficient Method for Enhancing Reverse-Time Migration Images Using Vertical Seismic Profiling Data

by Cai Lu and Youming Liu

Appl. Sci. 2024, 14(16), 7268; https://doi.org/10.3390/app14167268 - 19 Aug 2024

Cited by 1 | Viewed by 1112

Abstract

Vertical seismic profiling has garnered widespread attention in the industry as a supplement to seismic exploration due to its higher data quality compared to surface seismic data. However, its unique observation system in which geophones are only distributed within observation wells results in uneven coverage of subsurface structures. This can lead to significant noise when directly applying conventional reverse-time migration techniques used in surface seismic imaging. This study addresses the issue of noise suppression in reverse-time migration imaging associated with walk-away vertical seismic profiling and presents two main innovations. First, a common-receiver reverse-time migration imaging method is proposed, which uses the observation signals as excitation signals for the corresponding shots after reverse-time processing. Second, an excitation-time-constrained cross-correlation imaging condition is introduced to eliminate non-contributing portions of the wavefield, thereby modifying the traditional cross-correlation imaging condition to include an excitation time constraint. The combination of these methods enhances imaging quality by effectively suppressing noise, as demonstrated through theoretical analysis and numerical simulations with synthetic models. Full article

(This article belongs to the Special Issue Seismic Data Processing and Imaging)

► Show Figures

Figure 1

16 pages, 10540 KiB

Open AccessArticle

MA_W-Net-Based Dual-Output Method for Microseismic Localization in Strong Noise Environments

by Qiang Li, Fengjiao Zhang and Liguo Han

Appl. Sci. 2024, 14(13), 5668; https://doi.org/10.3390/app14135668 - 28 Jun 2024

Cited by 1 | Viewed by 917

Abstract

With the continuous depletion of conventional oil and gas reservoir resources, the beginning of exploration and development of unconventional oil and gas reservoir resources has led to the rapid development of microseismic monitoring technology. Addressing the challenges of low signal-to-noise ratio and inaccurate localization in microseismic data, we propose a new neural network MA_W-Net based on the U-Net network with the following improvements: (1) The foundational U-Net model was refined by evolving the single-channel decoder into a two-channel decoder, aimed at enhancing microseismic event localization and noise suppression capabilities. (2) The integration of attention mechanisms such as the convolutional block attention module (CBAM), coordinate attention (CA), and squeeze-and-excitation (SE) into the encoder to bolster feature extraction. We use synthetic data for evaluating the proposed method. Comparing with the normal U-net network, our accuracy in seismic recordings with a signal-to-noise ratio of −15 is improved from 78 percent to 93.5 percent, and the average error is improved from 2.60 m to 0.76 m. The results show that our method can accurately localize microseismic events and denoising processes from microseismic records with a low signal-to-noise ratio. Full article

(This article belongs to the Special Issue Seismic Data Processing and Imaging)

► Show Figures

Figure 1

16 pages, 15717 KiB

Open AccessArticle

Signal Reconstruction of Arbitrarily Lack of Frequency Bands from Seismic Wavefields Based on Deep Learning

by Xin Li, Fengjiao Zhang and Liguo Han

Appl. Sci. 2024, 14(11), 4922; https://doi.org/10.3390/app14114922 - 6 Jun 2024

Cited by 1 | Viewed by 1235

Abstract

Due to the limitations of seismic exploration instruments and the impact of the high frequencies absorption by the earth layers during subsurface propagation of seismic waves, recorded seismic data usually lack high and low frequency information that is needed to accurately image geological structures. Traditional methods face challenges such as limitations of model assumptions and poor adaptability to complex geological conditions. Therefore, this paper proposes a deep learning method that introduces the attention mechanism and Bi-directional gated recurrent unit (BiGRU) into the Transformer neural network. This approach can simultaneously capture both global and local characteristics of time series data, establish mappings between different frequency bands, and achieve information compensation and frequency extension. The results show that the BiGRU-Extended Transformer network is capable of compensating and extending the synthetic seismic data sets with the limited frequency band. It has certain generalization capabilities and stability and can effectively handle various problems in the data reconstruction process, which is better than traditional methods. Full article

(This article belongs to the Special Issue Seismic Data Processing and Imaging)

► Show Figures

Figure 1

19 pages, 9204 KiB

Open AccessArticle

Full-Wavefield Migration Using an Imaging Condition of Global Normalization Multi-Order Wavefields: Application to a Synthetic Dataset

by Hongyu Zhu, Deli Wang and Lingxiang Li

Appl. Sci. 2024, 14(4), 1389; https://doi.org/10.3390/app14041389 - 8 Feb 2024

Viewed by 1289

Abstract

In marine seismic exploration, seismic signals comprise primaries that undergo first-order scattering, as well as multiples resulting from multi-order scattering events. Surface-related multiples involve multi-order scattering at the free surface interface between seawater and air and exhibit a smaller reflection angle and broader illumination compared to primaries. Internal multiples, originating from multi-order scattering among stratified layers, provide additional illumination compensation beneath the reflecting interface. However, in conventional primary migration, different-order wavefields may result in crosstalk artifacts. To address this issue, we developed a least-squares migration (LSM) method based on the multi-order wavefield global normalization condition. This methodology investigates the illumination effects and crosstalk artifacts associated with different-order surface-related and internal multiples, and then modifies the global normalization condition by incorporating an illumination compensation perspective. Virtual sources, represented by surface-related multiples and internal multiples, are integrated into the source compensation term, ultimately yielding a multi-order wavefield normalization condition. This normalization condition is subsequently combined with least-squares full-wavefield migration (LSFWM). Numerical experiments demonstrate that the normalization condition of multi-order wavefields can resolve the problem of weak deep imaging energy and promote the suppression of multiple crosstalk artifacts in the least-squares algorithm. Full article

(This article belongs to the Special Issue Seismic Data Processing and Imaging)

► Show Figures

Figure 1

13 pages, 7437 KiB

Open AccessArticle

Iterative Interferometric Denoising Filter for Traveltime Picking

by Hanqing Qiao, Yicheng Zhou, Sherif M. Hanafy and Cai Liu

Appl. Sci. 2024, 14(2), 733; https://doi.org/10.3390/app14020733 - 15 Jan 2024

Viewed by 1244

Abstract

Traveltime picking accuracy is frequently affected by incoherent or random data noise. Within this context, we put forth a new denoising method called iterative interferometric denoising filtering. This method leverages the pseudo-Wigner distribution function to capture the offset and time-symmetric patterns of source wavelets convolved in seismic signals. Incoherent or random noises without this characteristic are eliminated via this approach. The processed data have waveform information distortion and more frequency components. However, the traveltime information can be considered correct, and the improved signal-to-noise ratio makes traveltime picking much more convenient. Our method’s practical applications in a synthetic and in two field datasets show that this technology can increase the signal-to-noise ratio, and the picked traveltime information can be used in traveltime tomography. These two field datasets were collected near the Aqaba Gulf and the Qademah fault, located in King Abdullah Economic City. Full article

(This article belongs to the Special Issue Seismic Data Processing and Imaging)

► Show Figures

Figure 1

16 pages, 9036 KiB

Open AccessArticle

Robust Elastic Full-Waveform Inversion Based on Normalized Cross-Correlation Source Wavelet Inversion

by Qiyuan Qi, Wensha Huang, Donghao Zhang and Liguo Han

Appl. Sci. 2023, 13(24), 13014; https://doi.org/10.3390/app132413014 - 6 Dec 2023

Cited by 2 | Viewed by 1534

Abstract

The elastic full-waveform inversion (EFWI) method efficiently utilizes the amplitude, phase, and travel time information present in multi-component seismic recordings to create detailed parameter models of subsurface structures. Within full-waveform inversion (FWI), accurate source wavelet estimation significantly impacts both the convergence and final result quality. The source wavelet, serving as the initial condition for the wave equation’s forward modeling algorithm, directly influences the matching degree between observed and synthetic data. This study introduces a novel method for estimating the source wavelet utilizing cross-correlation norm elastic waveform inversion (CNEWI) and outlines the EFWI algorithm flow based on this CNEWI source wavelet inversion. The CNEWI method estimates the source wavelet by employing normalized cross-correlation processing on near-offset direct waves, thereby reducing the susceptibility to strong amplitude interference such as bad traces and surface wave residuals. The proposed CNEWI method exhibits a superior computational efficiency compared to conventional L2-norm waveform inversion for source wavelet estimation. Numerical experiments, including in ideal scenarios, with seismic data with bad traces, and with multi-component data, validate the advantages of the proposed method in both source wavelet estimation and EFWI compared to the traditional inversion method. Full article

(This article belongs to the Special Issue Seismic Data Processing and Imaging)

► Show Figures

Figure 1

18 pages, 3637 KiB

Open AccessArticle

Microseismic Data-Direct Velocity Modeling Method Based on a Modified Attention U-Net Architecture

by Yixiu Zhou, Liguo Han, Pan Zhang, Jingwen Zeng, Xujia Shang and Wensha Huang

Appl. Sci. 2023, 13(20), 11166; https://doi.org/10.3390/app132011166 - 11 Oct 2023

Cited by 2 | Viewed by 1404

Abstract

In microseismic monitoring, the reconstruction of a reliable velocity model is essential for precise seismic source localization and subsurface imaging. However, traditional methods for microseismic velocity inversion face challenges in terms of precision and computational efficiency. In this paper, we use deep learning (DL) algorithms to achieve precise and efficient real-time microseismic velocity modeling, which holds significant importance for ensuring engineering safety and preventing geological disasters in microseismic monitoring. Given that this task was approached as a non-linear regression problem, we adopted and modified the Attention U-Net network for inversion. Depending on the degree of coupling among microseismic events, we trained the network using both single-event and multi-event simulation records as feature datasets. This approach can achieve velocity modeling when dealing with inseparable microseismic records. Numerical tests demonstrate that the Attention U-Net can automatically uncover latent features and patterns between microseismic records and velocity models. It performs effectively in real time and achieves high precision in velocity modeling for Tilted Transverse Isotropy (TTI) velocity structures such as anticlines, synclines, and anomalous velocity models. Furthermore, it can provide reliable initial models for traditional methods. Full article

(This article belongs to the Special Issue Seismic Data Processing and Imaging)

► Show Figures

Figure 1

Review

Jump to: Research

27 pages, 2400 KiB

Open AccessReview

Application of Distributed Acoustic Sensing in Geophysics Exploration: Comparative Review of Single-Mode and Multi-Mode Fiber Optic Cables

by Muhammad Rafi, Khairul Arifin Mohd Noh, Abdul Halim Abdul Latiff, Daniel Asante Otchere, Bennet Nii Tackie-Otoo, Ahmad Dedi Putra, Zaky Ahmad Riyadi and Dejen Teklu Asfha

Appl. Sci. 2024, 14(13), 5560; https://doi.org/10.3390/app14135560 - 26 Jun 2024

Cited by 2 | Viewed by 3847

Abstract

The advent of fiber optic technology in geophysics exploration has grown in its use in the exploration, production, and monitoring of subsurface environments, revolutionizing the way data are gathered and interpreted critically to speed up decision-making and reduce expense and time. Distributed Acoustic Sensing (DAS) has been increasingly utilized to build relationships in complex geophysics environments by utilizing continuous measurement along fiber optic cables with high spatial resolution and a frequency response of up to 10 KHz. DAS, as fiber optic technology examining backscattered light from a laser emitted inside the fiber and measuring strain changes, enables the performance of subsurface imaging in terms of real-time monitoring for Vertical Seismic Profiling (VSP), reservoir monitoring, and microseismic event detection. This review examines the most widely used fiber optic cables employed for DAS acquisition, namely Single-Mode Fiber (SMF) and Multi-Mode Fiber (MMF), with the different deployments and scopes of data used in geophysics exploration. Over the years, SMF has emerged as a preferred type of fiber optic cable utilized for DAS acquisition and, in most applications examined in this review, outperformed MMF. On the other side, MMF has proven to be preferable when used to measure distributed temperature. Finally, the fiber optic cable deployment technique and acquisition parameters constitute a pivotal preliminary step in DAS data preprocessing, offering a pathway to improve imaging resolution based on DAS measurement as a future scope of work. Full article

(This article belongs to the Special Issue Seismic Data Processing and Imaging)

► Show Figures

Journal Menu

Journal Browser

Seismic Data Processing and Imaging

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (11 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI