Quantifying Posterior Uncertainty in Array‐Based Surface Wave Tomography: A Hybrid Strategy Combining Bootstrap and Resolution Matrix

Abstract

Array‐based surface wave tomography has significantly enhanced the resolution of crustal imaging; however, rigorous quantification of posterior uncertainty in these regularized inversions remains a critical challenge. Conventional statistical methods, such as raw bootstrap resampling, often underestimate uncertainties in data‐sparse regions due to the dominance of regularization terms. This underestimation creates an artificial precision that risks misinterpreting inversion artifacts as robust structural features. To address this, we propose a hybrid uncertainty quantification strategy tailored for regional‐scale dense arrays, integrating data‐driven bootstrap resampling with model‐based resolution matrix correction. By utilizing the resolution matrix to explicitly debias the bootstrap variance, our approach provides a first‐order uncertainty metric for identifying and down‐weighting poorly constrained model estimates in the inversion's null‐space while retaining sensitivity to data inconsistencies in well‐resolved regions. We applied this workflow to ambient noise data from the ChinArray‐Himalaya project, extracting multimodal dispersion curves via the frequency‐Bessel (F‐J) transform. The derived hybrid uncertainty metric serves as a quantitative validity mask, successfully delineating reliable imaging regions for both fundamental and higher‐order modes, thereby facilitating objective, automated quality control for large‐scale data sets. Semi‐synthetic robustness tests further demonstrate that, unlike analytical or raw bootstrap methods, the hybrid strategy reliably distinguishes among data gaps (null‐spaces) and data conflicts. Ultimately, incorporating this resolution‐corrected uncertainty to weight subsequent 1D shear wave velocity inversions significantly suppresses regularization artifacts, laying a robust statistical foundation for constructing physically reliable 3D structural models.