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; Satoru Baba; Yasuyuki Nakamura; Gou Fujie

Home/Archive/arXiv

arXiv · 2610.27561

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·Satoru Baba·Yasuyuki Nakamura·Gou Fujie

View original source ↗Read source document ↗Export metadata ↓

Abstract

Rapid and accurate hypocenter determination based on three-dimensional (3D) seismic velocity models is essential for seismic monitoring in subduction zones, yet it remains computationally demanding. HypoNet Nankai, a hypocenter determination package that uses physics-informed neural network (PINN) travel-time inference for a realistic 3D P-wave velocity model of the Nankai Trough subduction zone, was designed to address this problem, but supported only P-wave arrival times. Here, we present HypoNet Nankai PS, which extends the framework to both P- and S-wave travel times. We first constructed an S-wave velocity model from the existing P-wave model using empirical conversion relations for marine sediments and the crust. Because the resulting S-wave velocity structure has lower velocities and therefore considerably steeper travel-time gradients, the original PINN training procedure designed for the P-wave velocity structure was insufficient. We therefore revised the training procedure by reconsidering the distribution of collocation points, the NN architecture, and the optimizer. Benchmark comparisons with the fast marching method showed that the revised approach substantially reduced the root-mean-square deviations for both P- and S-waves compared with the original algorithm. Recovery tests using synthetic earthquakes were also performed. When applied to 223 earthquakes recorded by DONET stations off the Kii Peninsula, the system yielded hypocenters generally consistent with the JMA catalog beneath the network, suggesting that the 3D velocity model captures travel-time variations accommodated by JMA's 1D model and station corrections. Farther seaward, the estimated hypocenters were substantially shallower than those in the JMA catalog and were located in the oceanic crust and uppermost mantle, consistent with previous relocations that used DONET data and 3D velocity models.

Explore related subjects

physics.geo-ph cs.LG

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Ryoichiro Agata, Ayako Nakanishi, Dan Bassett, Satoru Baba, Yasuyuki Nakamura, Gou Fujie. 2026-10-20. HypoNet Nankai PS: rapid hypocenter determination for the Nankai Trough using physics-informed neural networks with three-dimensional P- and S-wave velocity models. https://arxiv.org/abs/2610.27561

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Dual Reverse Time Migration: Efficient Least-squares Imaging with Multiple Scattering in Dual Space

Least-squares reverse time migration (LSRTM) estimates a broadband reflectivity image by iteratively minimizing the data misfit. Despite its superior imaging performance over conventional reverse time migration (RTM), LSRTM is fundamentally limited by the first-order Born approximation and incurs a high computational cost due to the iterative solution of a source-dependent model-space Hessian. We propose dual RTM, a seismic imaging framework that reformulates the inverse problem in the dual space, where the optimization variable is the scattering information of all orders, rather than being limited to the first-order scattering of the Born approximation. Unlike LSRTM, which directly estimates the reflectivity model, dual RTM reconstructs the missing scattering data (dual variable) through a fixed-point iteration governed by the full wave equation, enabling the recovery of higher-order scattering and multiple reflections without repeated background velocity updates. Furthermore, dual RTM replaces the source-dependent model-space Hessian with a source-independent data-space Hessian. A hybrid time--frequency formulation enables this Hessian to be assembled and inverted once in the Fourier domain as an offline preprocessing step, reducing the online computational cost of each dual iteration to that of a single RTM application. Numerical experiments on challenging synthetic benchmarks demonstrate that dual RTM suppresses acquisition footprints, improves amplitude fidelity, and enhances deep imaging where primary reflections alone provide insufficient illumination. These results establish dual RTM as an efficient bridge between conventional RTM/LSRTM and nonlinear waveform inversion for high-resolution large-scale seismic imaging.

physics.geo-ph↗

arXiv2026

A Targeted Quadrature Framework for Simulating Large-Scale 3D Anisotropic Electromagnetic Measurements

A new efficient and accurate method is presented for simulating frequency-domain borehole electromagnetic (EM) measurements acquired in the presence of three-dimensional (3D) variations of anisotropic subsurface conductivity. The method is based on solving the quasi-static Maxwell equations with a finite-volume discretization via goal-oriented block-quadrature reduced-order modeling. Discretization is performed with a Lebedev grid that enables accurate and conservative solutions in the presence of any form of anisotropic electrical conductivity. Likewise, the method makes use of a new effective-medium approximation to locally account for non-conformal boundaries and large contrasts in electrical conductivity, especially in the vicinity of EM sources and receivers. The finite-volume discretization yields a large symmetric linear system of equations, which is reduced to a set of smaller structured problems via block Lanczos recursion. The formulation also enables the efficient calculation of the adjoint solution, which is necessary for gradient-based inversion of the measurements to estimate the associated spatial distribution of electrical conductivity, i.e., to solve the inverse problem. The method is applied and verified for borehole ultra-deep azimuthal resistivity (UDAR) measurements commonly used in subsurface geosteering and well navigation. Efficiency, robustness, and scalability are demonstrated using synthetic UDAR data generated in a three-dimensional formation inspired by North Sea geology. The numerical results confirm suitability for integration into real-time UDAR processing frameworks.

physics.geo-ph↗

arXiv2026

Bayesian Geophysical Inversion using Stein Mixture Variational Inference for Multimodal or Complex Solutions

Bayesian geophysical inversion provides a probabilistic framework to estimate subsurface properties, in which uncertainties are represented by a so-called posterior probability density function (pdf). Estimating this pdf is computationally challenging when it is high dimensional or complex, particularly if it may be multimodal. We introduce Stein mixture variational inference (SMVI) for efficient Bayesian geophysical inversion with multimodal or complex posterior solutions. The method combines parametric variational inference with Stein variational gradient descent (SVGD). In SMVI, the posterior pdf is approximated by a mixture (sum) of distributions, in which component distributions are optimised jointly using the SVGD update rule. This construction combines computational efficiency with the flexibility to represent a range of plausible Earth models and allows components to be updated in parallel. Here, SMVI is first tested on two synthetic seismic inverse problems, demonstrating that SMVI can recover non-Gaussian, complex and multimodal posterior solutions accurately and efficiently. Then, we apply SMVI to real seismic data by performing Love wave tomography of the British Isles, and obtain inversion results that are consistent with previous Monte Carlo solutions. Around the East Irish Sea sedimentary basins, SMVI captures bimodal uncertainty associated with ambiguity in basin boundary locations, revealing alternative subsurface structures compatible with the observations. These results indicate that SMVI generalises various previous variational methods, and provides a practical and effective framework for quantifying complex uncertainties and supporting geological interpretation of seismic images.

physics.geo-ph↗