Bayesian seismic tomography based on velocity-space Stein variational gradient descent for physics-informed neural network

In this study, we propose a Bayesian seismic tomography inference method using physics-informed neural networks (PINN). PINN represents a recent advance in deep learning, offering the possibility to enhance physics-based simulations and inverse analyses. PINN-based deterministic seismic tomography uses two separate neural networks (NNs) to predict seismic velocity and travel time. Naive Bayesian NN (BNN) approaches are unable to handle the high-dimensional spaces spanned by the weight parameters of these two NNs. Hence, we reformulate the problem to perform the Bayesian estimation exclusively on the NN predicting seismic velocity, while the NN predicting travel time is used only for deterministic travel time calculations, with the help of the adjoint method. Furthermore, we perform BNN by introducing a function-space Stein variational gradient descent (SVGD), which performs particle-based variational inference in the space of the function predicted by the NN (i.e., seismic velocity), instead of in the traditional weight space. The result is a velocity-space SVGD for the PINN-based seismic tomography model (vSVGD-PINN-ST) that decreases the complexity of the problem thus enabling a more accurate and physically consistent Bayesian estimation, as confirmed by synthetic tests in one- and two-dimensional tomographic problem settings. The method allows PINN to be applied to Bayesian seismic tomography practically for the first time. Not only that, it can be a powerful tool not only for geophysical but also for general PINN-based Bayesian estimation problems associated with compatible NNs formulations and similar, or reduced, complexity

Analysis of Degradation in Graphene-based Spin Valves

The degradation mechanisms of multilayer graphene spin valves are investigated. The spin injection signals in graphene spin valves have been reported to be linearly dependent on the drain bias voltage, which indicates that the spin polarization of injected spins in graphene is robust against the bias voltage. We present that the disappearance of this robustness is due to two different degradation mechanisms of the spin valves. Our findings indicate that the disappearance of the robustness is due to degradation rather than an intrinsic characteristic of graphene. Thus, the robustness can be greatly enhanced if degradation can be prevented.Comment: 14 pages, 4 figures (To appear in Applied Physics Express

トウキョウワン ナイワン ノ フッツミサキ スナハマ カイガン ニ シュツゲン スル シチギョ

This report presents the inventory of fishes collected by a small seine-net at a sandy beach in Futtsu, the inner Tokyo Bay from May to November 2005. A total of 102 specimens representing >13 species of six families were collected, the most dominant species being 36 specimens of Favnoigobius gymnauchen , followed by 24 of Gymnogobius breunigii . Both the dominant species were categorized as “resident” and transient”, respectively, and other species as “passers-by and strays” in the life-style category.東京湾内湾の富津岬の砂浜海岸で2005 年5 月から11 月にかけて小型地曳網で採集された魚類の種類と大きさ、および生活史型と利用様式の調査結果を資料として示した。総計で102 個体13 種以上の魚類が採集された。最も多かったのはヒメハゼの36 個体で、次いでビリンゴの24 個体であった。これら2 種は各々滞在型と一時滞在型であったが、他の11 種は通過・偶来型であった。小出一也, 白石瑛子, 河野 博: 東京海洋大学魚類学研究室河野 博: 東京海洋大学大学院海洋科学系海洋環境学部