Abstract

Assessing accuracy of numerical methods for spontaneous rupture simulation is challenging because we lack analytical solutions for reference. Previous comparison of a boundary integral method (bi) and finite-difference method (called dfm) that explicitly incorporates the fault discontinuity at velocity nodes (traction- at-split-node scheme) shows that both converge to a common, grid-independent solution and exhibit nearly identical power-law convergence rates with respect to grid spacing Δx. We use this solution as a reference for assessing two other proposed finite-difference methods, the thick fault (tf) and stress glut (sg) methods, both of which approximate the fault-jump conditions through inelastic increments to the stress components (inelastic-zone schemes). The tf solution fails to match the qualitative rupture behavior of the reference solution and has quantitative misfits in root- mean-square rupture time of ∼30% for the smallest computationally feasible Δx (with ∼9 grid-point resolution of cohesive zone, denoted c = 9). For sufficiently small values of Δx, the sg method reproduces the qualitative features of the reference solution, but rupture velocity remains systematically low for sg relative to the reference solution, and sg lacks the well-defined power-law convergence seen for bi and dfm. The rupture-time error for sg, with c ∼ 9, remains well above uncertainty in the reference solution, and the split-node method attains comparable accuracy with c 1/4 as large (and computation timescales as (c)4). Thus, accuracy is highly sensitive to the formulation of the fault-jump conditions: The split-node method attains power-law convergence. The sg inelastic-zone method achieves solutions that are qualitatively meaningful and quantitatively reliable to within a few percent, but convergence is uncertain, and sg is computationally inefficient relative to the split-node approach. The tf inelastic-zone method does not achieve qualitatively meaningful solutions to the 3D test problem and is sufficiently computationally inefficient that it is not feasible to explore convergence quantitatively.

You do not currently have access to this article.