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Applied Sciences
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Advanced Data Analysis and Imaging Technologies for Seismic Reservoir Monitoring
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Advanced Data Analysis and Imaging Technologies for Seismic Reservoir Monitoring

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 2412

Special Issue Editors

Dr. Fengjiao Zhang

E-Mail Website
Guest Editor
College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
Interests: reflection seismic method; full waveform inversion; seismic interferometry; passive and active seismic data imaging; geological storage of CO2; seismic reservoir characterization; time-lapse seismic monitoring
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhuo Xu

E-Mail Website
Guest Editor
College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
Interests: exploration seismic data processing methods; passive source seismic data processing and imaging methods; data-driven active source seismic wavefield reconstruction systems

Special Issue Information

Dear Colleagues,

Seismic reservoir monitoring plays a crucial role in modern geophysical exploration. It is not only vital for oil and gas exploration, development, and production management, it also significant for understanding and predicting geological disasters, for assessing the potential of renewable resources, and for environmental monitoring. Seismic exploration methods allow for the high-precision imaging of subsurface structures and offer quantitative interpretations of reservoir parameters, such as porosity, lithology, and rock–fluid properties through various inversion algorithms. The effective analysis of seismic data using advanced data analysis, inversion, and imaging techniques has become particularly important. This Special Issue aims to collect the latest advancements and innovative methods in seismic data processing, imaging, and interpretation research, especially those technologies that can enhance the accuracy and efficiency of seismic reservoir monitoring. We encourage submissions that cover research from active and passive seismic data analysis to seismic processing inversion and imaging, as well as studies utilizing machine learning and artificial intelligence to optimize the seismic reservoir monitoring and interpretation processes. We look forward to receiving both original research articles and case studies.

Dr. Fengjiao Zhang
Prof. Dr. Zhuo Xu
Guest Editors

Manuscript Submission Information

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Keywords

  • seismic reservoir monitoring
  • seismic data analysis
  • seismic imaging
  • machine learning
  • seismic inversion
  • full waveform inversion
  • multi-source seismic imaging

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Published Papers (3 papers)

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Research

18 pages, 11121 KiB  
Article
Separation of Body and Surface Wave Background Noise and Passive Seismic Interferometry Based on Synchrosqueezed Continuous Wavelet Transform
by Xiaolong Li, Fengjiao Zhang, Zhuo Xu and Xiangbo Gong
Appl. Sci. 2025, 15(7), 3917; https://doi.org/10.3390/app15073917 - 2 Apr 2025
Viewed by 349
Abstract
Passive seismic interferometry is a technique that reconstructs virtual seismic records using ambient noise, such as random noise or microseisms. The ambient noise in passive seismic data contains rich information, with surface waves being useful for the inversion of shallow subsurface structures, while [...] Read more.
Passive seismic interferometry is a technique that reconstructs virtual seismic records using ambient noise, such as random noise or microseisms. The ambient noise in passive seismic data contains rich information, with surface waves being useful for the inversion of shallow subsurface structures, while body waves are employed for deep-layer inversion. However, due to the low signal-to-noise ratio in actual passive seismic data, different types of seismic waves mix together, making them difficult to distinguish. This issue not only affects the dispersion measurements of surface waves but also interferes with the imaging accuracy of reflected waves. Therefore, it is crucial to extract the target waves from passive source data. In practical passive seismic data, body wave noise and surface wave noise often overlap in frequency bands, making it challenging to separate them effectively using conventional methods. The synchrosqueezed continuous wavelet transform, as a high-resolution time–frequency analysis method, can effectively capture the variations in frequency of passive seismic data. This study performs time–frequency analysis of passive seismic data using synchrosqueezed continuous wavelet transform. It combines wavelet thresholding and Gaussian filtering to separate body wave noise from surface wave noise. Furthermore, wavelet cross-correlation is applied to separately obtain high-quality virtual seismic records for both surface waves and body waves. Full article
(This article belongs to the Special Issue Advanced Data Analysis and Imaging Technologies for Seismic Reservoir Monitoring)
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21 pages, 33924 KiB  
Article
Multiparameter Inversion of Seismic Pre-Stack Amplitude Variation with Angle Based on a New Propagation Matrix Method
by Qianlong Ding, Shuangquan Chen, Jinsong Shen, Zuzhi Hu and Guoquan Wang
Appl. Sci. 2025, 15(5), 2636; https://doi.org/10.3390/app15052636 - 28 Feb 2025
Cited by 1 | Viewed by 652
Abstract
The classical pre-stack seismic inversion technique uses the Zoeppritz equation and its simplified versions to calculate the PP and PS reflection coefficients at different incidence angles, aiding in inverting the subsurface velocity and density parameters. Despite its widespread application, the amplitude variation with [...] Read more.
The classical pre-stack seismic inversion technique uses the Zoeppritz equation and its simplified versions to calculate the PP and PS reflection coefficients at different incidence angles, aiding in inverting the subsurface velocity and density parameters. Despite its widespread application, the amplitude variation with angle (AVA) inversion based on the Zoeppritz equation has limitations regarding the accuracy. The AVA neglects transmission losses and the effects of multiple reflections during seismic wave propagation, resulting in reduced resolution. In contrast, the propagation matrix theory offers a comprehensive range of reflection coefficients for P- and S-waves in multilayered media at arbitrary incidence angles, thereby theoretically enhancing the inversion accuracy. However, the seismic responses obtained using this method exist in the slowness–frequency domain and require constant slowness for consistency along a profile. This assumption is violated when variations in the P-wave velocity occur within the subsurface, affecting the incidence angle of propagating seismic waves. This study modifies the propagation matrix theory to compute AVA seismic responses and applies it to pre-stack multiparameter inversion. The effectiveness of the modified method was validated by deriving theoretical AVA seismic responses and comparing them to solutions from a typical layered media model. The modified theory was also employed for seismic pre-stack inversion. Numerical simulations and field data tests demonstrated that the new propagation matrix method offers a high accuracy and stability. Full article
(This article belongs to the Special Issue Advanced Data Analysis and Imaging Technologies for Seismic Reservoir Monitoring)
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18 pages, 1747 KiB  
Article
GA-PSO Algorithm for Microseismic Source Location
by Yaning Han, Fanyu Zeng, Liangbin Fu and Fan Zheng
Appl. Sci. 2025, 15(4), 1841; https://doi.org/10.3390/app15041841 - 11 Feb 2025
Viewed by 953
Abstract
Accurate source location is a critical component of microseismic monitoring and early warning systems. To improve the accuracy of microseismic source location, this manuscript proposes a GA-PSO algorithm that combines the Genetic Algorithm (GA) with Particle Swarm Optimization (PSO). The GA-PSO algorithm enhances [...] Read more.
Accurate source location is a critical component of microseismic monitoring and early warning systems. To improve the accuracy of microseismic source location, this manuscript proposes a GA-PSO algorithm that combines the Genetic Algorithm (GA) with Particle Swarm Optimization (PSO). The GA-PSO algorithm enhances the PSO algorithm by dynamically adjusting the balance between global exploration and local exploitation through a sinusoidal function for the nonlinear adjustment of both learning factors, and an adaptive inertia weight that decreases quadratically with iterations. Additionally, the precision of the solutions is further improved through the crossover and mutation operations of the GA. In the simulated location model, the GA-PSO algorithm demonstrated the smallest error value, outperforming both the GA and PSO algorithm in terms of accuracy. Furthermore, the GA-PSO algorithm exhibited minimal sensitivity to wave speed fluctuations of ±1%, ±3%, and ±5%, maintaining the error within 0.5 m. The validation through the blasting experiment at the Shizhuyuan mine further confirmed the enhanced accuracy of the GA-PSO algorithm, with a location error of 20.08 m, representing an improvement of 59% over the GA and 43% over the PSO algorithm. Full article
(This article belongs to the Special Issue Advanced Data Analysis and Imaging Technologies for Seismic Reservoir Monitoring)
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