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.
In smart city, traffic congestion and parking problems will be solved assisted by precise vehicle location. To address the vehicle localization problem in smart city, a novel extrinsic information aided fingerprint localization algorithm is proposed in this paper. Firstly, on the basis of massive multipleinput multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM), the angle-delay domain channel matrix (ADDCM) is extracted. Then, an amplitude ratio based non-line of sight (NLoS) identification method is provided to estimate link states. For the case of LoS existence, in order to save storage space, a tuple fingerprint is proposed to record angle of LoS path. In other case, when all APs service in NLoS scenarios, to improve the localization robustness in dynamic environments, the correlated ADDCM (CADDCM) is taken as location fingerprint which can extract the constant information related to fixed scattering clusters. A greedy-based fingerprint matching scheme is used to search the nearest reference point (RP). Furthermore, the extrinsic information provided by neighbor vehicles, such as estimated location and measured distance, is utilized to improve the localization stability. Simulation results show that the extrinsic information aided algorithm could improves the localization accuracy and robustness in dynamic environments.
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