1,4,1,2- 1Laboratory of Seismology and Physics of Earth's Interior, School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China (xinliu_geo@outlook.com)
- 2Mengcheng National Geophysical Observatory, University of Science and Technology of China, Mengcheng Hefei, China (hjyao@ustc.edu.cn)
- 3CAS Center for Excellence in Comparative Planetology, University of Science and Technology of China, Hefei, China (hjyao@ustc.edu.cn)
- 4Institute of Advanced Technology, University of Science and Technology of China, Hefei, China (xinliu_geo@outlook.com)
- 5School of Earth Sciences and Engineering, Sun Yat-sen University, Zhuhai, China (fanghj23@mail.sysu.edu.cn)
Surface-wave azimuthal anisotropy provides important constraints on crustal deformation and stress fields. However, its imaging in direct surface-wave tomography remains challenging due to strong parameter trade-offs and the need for carefully tuned smoothing and damping in conventional grid-based inversions, particularly when isotropic velocity and anisotropic parameters are inverted simultaneously (e.g., Fang et al., 2015; Liu et al., 2019).
In this study, we extend the Poisson-Voronoi parameterization of Fang et al. (2020) to direct surface-wave azimuthal anisotropy tomography using a data-adaptive ensemble framework. Model parameters are represented on multiple Poisson-Voronoi realizations that are stochastically generated and subsequently refined according to raypath density, allowing the parameterization to adapt to spatial variations in data coverage. For each realization, the inversion is performed in a reduced parameter space, and individual solutions are combined through a misfit-based ensemble strategy in which poorly constrained realizations are down-weighted. This ensemble-based formulation requires only a limited number of control parameters, minimizes subjective regularization choices, and enables straightforward assessment of model uncertainty and stability across realizations, making the approach largely automated and accessible for users without extensive experience in inverse theory.
We apply the method to a dense seismic array deployed in southwestern China using Rayleigh-wave phase velocity dispersion measurements extracted from ambient noise interferometry. The resulting azimuthal anisotropy model reveals coherent and geologically interpretable patterns associated with major tectonic structures, demonstrating the effectiveness of data-adaptive Poisson-Voronoi ensemble inversion for imaging surface-wave azimuthal anisotropy in dense array settings.
References
Fang, H., Yao, H., Zhang, H., Huang, Y., & van der Hilst, R. (2015). Direct inversion of surface wave dispersion for three‐dimensional shallow crustal structure based on ray tracing: methodology and application. Geophysical Journal International, 201(3), 1251–1263. https://doi.org/10.1093/gji/ggv080
Fang, H. et al. (2020) Parsimonious Seismic Tomography with Poisson Voronoi Projections: Methodology and Validation, Seismological Research Letters, 91(1), pp. 343–355. Available at: https://doi.org/10.1785/0220190141.
Liu, C., Yao, H., Yang, H. Y., Shen, W., Fang, H., Hu, S., & Qiao, L. (2019). Direct Inversion for Three‐Dimensional Shear Wave Speed Azimuthal Anisotropy Based on Surface Wave Ray Tracing: Methodology and Application to Yunnan, Southwest China. Journal of Geophysical Research: Solid Earth, 124(11), 1139411413.
How to cite: Liu, X., Yao, H., Fang, H., and Liu, Y.: Direct Surface-Wave Tomography of Azimuthal Anisotropy Using a Data-Adaptive Ensemble Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7800, https://doi.org/10.5194/egusphere-egu26-7800, 2026.
