Anisotropic tomography of the East Japan subduction zone: influence of inversion algorithms

Abstract

Summary An important element of seismic tomography is the inversion process. In this work we use P-wave arrival times of local earthquakes recorded at onshore and offshore seismic stations in East Japan to investigate the influence of two well-known inversion algorithms (LSQR and L-BFGS-B) on anisotropic tomography. Our synthetic tests show that a large damping parameter in the LSQR algorithm can lead to a stable and fast convergence, but it can result in many small value disturbances. The L-BFGS-B algorithm, which has second-order convergence, could converge fast to the optimal solution without damping regularization, but an inappropriate bound on the unknown parameters makes them hard to be recovered fully and causes strong trade-off between isotropic velocity and azimuthal anisotropy. If appropriate control parameters are adopted, the two inversion algorithms lead to almost the same results, though the L-BFGS-B provides a more efficient convergence and leads to a slightly better fit to the data than LSQR does. The two algorithms are applied to investigate the 3-D P-wave velocity (Vp) structure and azimuthal anisotropy of the East Japan subduction zone. Our results show that high-Vp anomalies and trench-normal fast-velocity directions (FVDs) exist in the forearc crust beneath the Pacific Ocean off South Hokkaido, which may reflect a cold and hydrated forearc crust with aligned microcracks or fractures. Significant low-Vp anomalies and trench-parallel FVDs exist at 40–80 km depths beneath Hokkaido, reflecting a water-rich mantle wedge with aligned B-type olivine. In the subducting Pacific slab, strong anisotropy with trench-parallel FVDs is revealed, reflecting localized horizontal bending of the slab.

Big Mantle Wedge and Intraplate Volcanism in Alaska: Insight From Anisotropic Tomography

“…To solve Equation 2, we adopt the limited‐memory Broyden‐Fletcher‐Goldfarb‐Shanno algorithm (L‐BFGS‐B; Morales & Nocedal, 2011) to minimize the objective function. The advantages of the L‐BFGS‐B algorithm are that it does not require damping regularization and it converges faster with a solution fitting the data slightly better than the LSQR algorithm (Jia & Zhao, 2023; Z. Wang & Zhao, 2019).…”