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HypoNet Nankai PS: rapid hypocenter determination for the Nankai Trough using physics-informed neural networks with three-dimensional P- and S-wave velocity models
Ryoichiro Agata, Ayako Nakanishi, Dan Bassett +3
Rapid and accurate hypocenter determination based on three-dimensional (3D) seismic velocity models is essential for seismic monitoring in subduction zones, yet it remains computat…
Bayesian three-dimensional seismic travel-time tomography for active- and passive-source seismic data using physics-informed neural network
Ryoichiro Agata, Kazuya Shiraishi, Gou Fujie +1
Accurate 3D seismic velocity modeling through seismic travel-time tomography using both active- and passive-source data provides critical underpinning models for seismicity monitor…
Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks
Ryoichiro Agata, Tomohisa Okazaki
Physics-informed neural networks (PINNs) provide a mesh-free framework for solving PDE-constrained inverse problems, but their extension to Bayesian inversion still faces a fundame…
Three-dimensional crustal deformation analysis using physics-informed deep learning
Tomohisa Okazaki, Takeo Ito, Kazuro Hirahara +3
Earthquake-related phenomena such as seismic waves and crustal deformation impact broad regions, requiring large-scale modeling with careful treatment of artificial outer boundarie…
Quantification of Uncertainty and Its Propagation in Seismic Velocity Structure and Earthquake Source Inversion
Ryoichiro Agata
In earthquake source inversions aimed at understanding diverse fault activities on earthquake faults using seismic observation data, uncertainties in velocity structure models are…
HypoNet Nankai: Rapid hypocenter determination tool for the Nankai Trough subduction zone using physics-informed neural networks
Ryoichiro Agata, Satoru Baba, Ayako Nakanishi +1
Accurate hypocenter determination in the Nankai Trough subduction zone is essential for hazard assessment and advancing our understanding of seismic activity in the region. A handy…
Physics-informed deep learning quantifies propagated uncertainty in seismic structure and hypocenter determination
Ryoichiro Agata, Kazuya Shiraishi, Gou Fujie
Subsurface seismic velocity structure is essential for earthquake source studies, including hypocenter determination. Conventional hypocenter determination methods ignore the inher…
Bayesian seismic tomography based on velocity-space Stein variational gradient descent for physics-informed neural network
Ryoichiro Agata, Kazuya Shiraishi, Gou Fujie
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…