Your new experience awaits. Try the new design now and help us make it even better

ORIGINAL RESEARCH article

Front. Earth Sci.

Sec. Solid Earth Geophysics

Volume 13 - 2025 | doi: 10.3389/feart.2025.1660737

This article is part of the Research TopicFrontiers in Borehole Multi-Geophysics: Innovations and ApplicationsView all 3 articles

Uncertainty-Quantified 3D Ambient Noise Tomography Using Transdimensional Monte Carlo Inversion

Provisionally accepted
Yi  LiuYi Liu*Bo  WangBo WangDaoheng  YingDaoheng YingLingzhi  ZhuLingzhi ZhuJun  WangJun WangTuo  ZhaoTuo Zhao
  • Jilin Jianzhu University, Changchun, China

The final, formatted version of the article will be published soon.

Traditional two-step surface-wave tomography often yields discontinuous models and compound uncertainty. We present the first fully 3-D transdimensional Bayesian inversion with adaptive Voronoi parameterization and reversible-jump MCMC for near-surface engineering-scale arrays, providing voxel-level uncertainty estimates. From one week of ambient-noise records acquired by a 101-station linear array (120 m spacing) across the F1 fault zone, we extracted phase velocities via frequency–wavenumber analysis of Rayleigh waves (0.5–3 s). The resulting 3-D Vs model reveals (i) 300–800 m s⁻¹ in the upper 50 m, (ii) 2.1 ± 0.05 km s⁻¹ at 0–1 km, (iii) 2.6–2.9 ± 0.08 km s⁻¹ at 1–3 km, and (iv) 2.8–3.1 ± 0.12 km s⁻¹ at 3–5 km beneath the fault trace. Voxel-wise 1σ uncertainties range from <5 % in the shallowest 2 km to 12 % at 5 km depth. These Vs values and their uncertainties can be directly converted to engineering mechanical parameters: shear modulus G = ρVs², Young’s modulus E = 2G(1+ν), and Poisson’s ratio ν, enabling quantitative assessment of excavation stability, tunnel lining design, and slope stability across the F1 fault zone. The 3-D Bayesian framework mitigates over-fitting biases inherent in sequential inversions and offers critical, uncertainty-aware constraints for multi-stage tectonic reconstruction of the North China Craton destruction belt.

Keywords: Bayesian Monte Carlo inversion, 3D ambient noise tomography, Shear-wave, Velocity structure, transdimensional inversion, uncertainty quantification

Received: 06 Jul 2025; Accepted: 04 Sep 2025.

Copyright: © 2025 Liu, Wang, Ying, Zhu, Wang and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Yi Liu, Jilin Jianzhu University, Changchun, China

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.