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Applied Sciences
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Seismic Data Processing and Imaging
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Seismic Data Processing and Imaging

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

Deadline for manuscript submissions: 20 August 2024 | Viewed by 4338

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: earthquake and tsunami monitoring; disaster mitigation science; human resource cultivation
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

Published Papers (7 papers)

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Research

Jump to: Review

16 pages, 10540 KiB  
Article
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 (registering DOI) - 28 Jun 2024
Viewed by 116
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 [...] Read more.
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)
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16 pages, 15717 KiB  
Article
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
Viewed by 331
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 [...] Read more.
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)
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19 pages, 9204 KiB  
Article
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 603
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 [...] Read more.
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)
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13 pages, 7437 KiB  
Article
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 771
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 [...] Read more.
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)
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16 pages, 9036 KiB  
Article
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
Viewed by 776
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 [...] Read more.
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)
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18 pages, 3637 KiB  
Article
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 1 | Viewed by 837
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 [...] Read more.
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)
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Review

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27 pages, 2400 KiB  
Review
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
Viewed by 329
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 [...] Read more.
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)
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