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 Δ x . 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 Δ x (with ∼9 grid-point resolution of cohesive zone, denoted N c = 9). For sufficiently small values of Δ x , 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 N c ∼ 9, remains well above uncertainty in the reference solution, and the split-node method attains comparable accuracy with N c 1/4 as large (and computation timescales as ( N 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.
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