We discuss the performance of the multiple-grid model parametrization in seismic tomographic inversion. Rather than mapping the velocity perturbation $ Δc(x) = c(x) − c_0(x) $ on only one regular/collocated grid as many previous studies did, we obtain individual Δc(x) models on multiple grids and generate several updated velocity models during one iteration. The average of all the updated velocity models is considered to be the input model of the next iteration. Different grids should partially/fully shift from each other and/or have different grid spacings to form the multiple-grid model parametrization. The efficacy of the multiple-grid model parametrization is demonstrated through the practical example of imaging the P-wave velocity structure along the San Jacinto fault, which is one of the most seismically active faults in California. A series of synthetic recovery examples shows that the multiple-grid model parametrization generally has a better or comparable performance in capturing the heterogeneous subsurface structures than a collocated grid. The root mean square values of the traveltime residuals in the final tomographic models obtained with the multiple-grid model parametrization are smaller than those with collocated grids. Tomographic results reveal strong heterogeneities in the crust along the San Jacinto fault. Significant velocity contrasts are observable across the fault at shallow depths. A low-velocity anomaly dominates the trifurcation area of the San Jacinto fault from the middle crust to the lower crust. Relatively large earthquakes occurred at the boundaries of low-velocity structures but with high-velocity anomalies nearby. All the results suggest that the multiple-grid model parametrization can be a reliable approach in future seismic tomography studies.