Advances in Fractional Dynamics and Their Applications in Seismology

A special issue of Fractal and Fractional (ISSN 2504-3110). This special issue belongs to the section "Mathematical Physics".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1219

Special Issue Editors


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Guest Editor
National Key Laboratory of Deep Oil and Gas and the School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China
Interests: exploration seismology; earthquake seismology; computational seismology; fractional operator; fractional Laplacians
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
The School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China
Interests: seismic wave propagation and imaging; fractional viscoelastic wave equation; fractional finite difference method

Special Issue Information

Dear Colleagues,

Seismology is an interdisciplinary science of mathematics, physics, and computational methods, dedicated to probing the Earth's interior and locating subsurface resources such as hydrocarbons and minerals through the analysis of seismic wavefields. In recent years, fractional dynamics—governed by fractional differential equations—have shown great promise in modeling complex wave propagation phenomena that are difficult to capture with conventional wave equations. Applications of fractional models in seismology include seismic wave simulation and imaging in viscoacoustic and viscoelastic media, quasi-P and quasi-S wavefield decomposition and simulation in anisotropic formations, and one-way wavefield extrapolation and imaging. Efficient and robust numerical methods for solving fractional equations are crucial for advancing seismic modeling, imaging, and inversion in complex geological settings.

The aim of this Special Issue is to showcase recent advances in fractional dynamics and their applications in seismology. Topics of interest include, but are not limited to, the following:

  • Seismic wave modeling in viscous and anisotropic media with fractional equations;
  • Seismic data processing with fractional derivatives;
  • High-performance computing methods for fractional wave equations;
  • One-way wavefield approximations using fractional operators;
  • Seismic imaging in viscoelastic and anisotropic media;
  • Seismic tomography and full waveform inversion involving fractional calculations.

Prof. Dr. Jidong Yang
Dr. Bingluo Gu
Guest Editors

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Keywords

  • fractional wave equation
  • fractional laplacian
  • time-/space-domain fractional derivatives
  • seismic modeling, imaging, and inversion

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Published Papers (1 paper)

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Research

19 pages, 14054 KB  
Article
Application of a Fractional Laplacian-Based Adaptive Progressive Denoising Method to Improve Ambient Noise Crosscorrelation Functions
by Kunpeng Yu, Jidong Yang, Shanshan Zhang, Jianping Huang, Weiqi Wang and Tiantao Shan
Fractal Fract. 2025, 9(12), 802; https://doi.org/10.3390/fractalfract9120802 - 7 Dec 2025
Viewed by 856
Abstract
Extracting high-quality surface wave dispersion curves from crosscorrelation functions (CCFs) of ambient noise data is critical for seismic velocity inversion and subsurface structure interpretation. However, the non-uniform spatial distribution of noise sources may introduce spurious noise into CCFs, significantly reducing the signal-to-noise ratio [...] Read more.
Extracting high-quality surface wave dispersion curves from crosscorrelation functions (CCFs) of ambient noise data is critical for seismic velocity inversion and subsurface structure interpretation. However, the non-uniform spatial distribution of noise sources may introduce spurious noise into CCFs, significantly reducing the signal-to-noise ratio (SNR) of empirical Green’s functions (EGFs) and degrading the accuracy of dispersion measurement and velocity inversion. To mitigate this issue, this study aims to develop an effective denoising approach that enhances the quality of CCFs and facilitates more reliable surface wave extraction. We propose a fractional Laplacian-based adaptive progressive denoising (FLAPD) method that leverages local gradient information and a fractional Laplacian mask to estimate noise variance and construct a fractional bilateral kernel for iterative noise removal. We applied the proposed method to the CCFs from 79 long-period seismic stations in Shandong, China. The results demonstrate that the denoising method enhanced the data quality substantially, increasing the number of reliable dispersion curves from 1094 to 2196, and allowing an increased number of temporal sampling points to be retrieved from previously low-SNR curves. This helps to expand the spatial coverage and results in a more accurate velocity inversion result than that without denoising. This study provides a robust denoising solution for ambient noise tomography in regions with complex noise source distributions. Full article
(This article belongs to the Special Issue Advances in Fractional Dynamics and Their Applications in Seismology)
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