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HypoNet Nankai: Rapid hypocenter determination tool for the Nankai Trough subduction zone using physics-informed neural networks
Geophysics · Submitted 7 Nov 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim HypoNet Nankai offers a rapid, accurate, and easy-to-use hypocenter determination method for the Nankai Trough subduction zone, with greater data efficiency and extendibility compared to conventional approaches.
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The abstract, sentence by sentence
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 hypocenter determination tool incorporating a realistic 3D velocity structure, accessible to the scientific community, is beneficial. In this study, we developed HypoNet Nankai, a rapid hypocenter determination tool based on a physics-informed neural network (PINN) emulator (surrogate model) for travel time calculations. This tool leverages a PINN trained to predict P-wave travel times between arbitrary underground sources and surface receivers with a realistic 3D P-wave velocity structure model of the Nankai Trough subduction zone that incorporates marine seismic survey data. The PINN embeds physical laws, namely, the Eikonal equation, directly into the loss function of training and circumvents the need for labeled training data. To address the training challenges posed by small-scale features in the velocity model, we employed a simple domain decomposition approach and Fourier feature embedding. Once trained, the PINN immediately infers the P-wave travel time, enabling rapid hypocenter determination. The data size required to store NNs for travel time calculations is significantly smaller than those of conventional travel-time tables. HypoNet Nankai provides high flexibility for addition of new observation points. We verified HypoNet Nankai by comparing its performance with a widely used grid-based numerical method for forward travel time calculations and synthetic hypocenter determination. In both tests, HypoNet Nankai provided results consistent with those for the conventional method. HypoNet Nankai offers a rapid, accurate, and easy-to-use hypocenter determination method for the Nankai Trough subduction zone, with greater data efficiency and extendibility compared to conventional approaches.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Accurate hypocenter determination in the Nankai Trough subduction zone is essential for hazard assessment and advancing our understanding of seismic activity in the region. | background 1.00 | 0.06 | 0.04 | asserted 1.00 |
| s1 | A handy hypocenter determination tool incorporating a realistic 3D velocity structure, accessible to the scientific community, is beneficial. | background 0.62 | 0.06 | 0.04 | asserted 0.84 |
| s2 | In this study, we developed HypoNet Nankai, a rapid hypocenter determination tool based on a physics-informed neural network (PINN) emulator (surrogate model) for travel time calculations. | contribution 0.99 | 0.96 | 0.14 | asserted 1.00 |
| s3 | This tool leverages a PINN trained to predict P-wave travel times between arbitrary underground sources and surface receivers with a realistic 3D P-wave velocity structure model of the Nankai Trough subduction zone that incorporates marine seismic survey data. | method 0.94 | 0.90 | 0.07 | asserted 1.00 |
| s4 | The PINN embeds physical laws, namely, the Eikonal equation, directly into the loss function of training and circumvents the need for labeled training data. | method 0.99 | 0.88 | 0.05 | asserted 1.00 |
| s5 | To address the training challenges posed by small-scale features in the velocity model, we employed a simple domain decomposition approach and Fourier feature embedding. | method 0.99 | 0.95 | 0.02 | asserted 1.00 |
| s6 | Once trained, the PINN immediately infers the P-wave travel time, enabling rapid hypocenter determination. | method 0.74 | 0.75 | 0.09 | asserted 1.00 |
| s7 | The data size required to store NNs for travel time calculations is significantly smaller than those of conventional travel-time tables. | result 0.86 | 0.77 | 0.07 | asserted 1.00 |
| s8 | HypoNet Nankai provides high flexibility for addition of new observation points. | contribution 0.76 | 0.67 | 0.05 | asserted 1.00 |
| s9 | We verified HypoNet Nankai by comparing its performance with a widely used grid-based numerical method for forward travel time calculations and synthetic hypocenter determination. | method 0.79 | 0.96 | 0.05 | asserted 0.99 |
| s10 | In both tests, HypoNet Nankai provided results consistent with those for the conventional method. | result 1.00 | 0.94 | 0.13 | asserted 0.96 |
| s11 | HypoNet Nankai offers a rapid, accurate, and easy-to-use hypocenter determination method for the Nankai Trough subduction zone, with greater data efficiency and extendibility compared to conventional approaches. | interpretation 0.68 | 0.73 | 0.93 | asserted 1.00 |
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Original abstract
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 hypocenter determination tool incorporating a realistic 3D velocity structure, accessible to the scientific community, is beneficial. In this study, we developed HypoNet Nankai, a rapid hypocenter determination tool based on a physics-informed neural network (PINN) emulator (surrogate model) for travel time calculations. This tool leverages a PINN trained to predict P-wave travel times between arbitrary underground sources and surface receivers with a realistic 3D P-wave velocity structure model of the Nankai Trough subduction zone that incorporates marine seismic survey data. The PINN embeds physical laws, namely, the Eikonal equation, directly into the loss function of training and circumvents the need for labeled training data. To address the training challenges posed by small-scale features in the velocity model, we employed a simple domain decomposition approach and Fourier feature embedding. Once trained, the PINN immediately infers the P-wave travel time, enabling rapid hypocenter determination. The data size required to store NNs for travel time calculations is significantly smaller than those of conventional travel-time tables. HypoNet Nankai provides high flexibility for addition of new observation points. We verified HypoNet Nankai by comparing its performance with a widely used grid-based numerical method for forward travel time calculations and synthetic hypocenter determination. In both tests, HypoNet Nankai provided results consistent with those for the conventional method. HypoNet Nankai offers a rapid, accurate, and easy-to-use hypocenter determination method for the Nankai Trough subduction zone, with greater data efficiency and extendibility compared to conventional approaches.
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Furthermore, we used an exponential decay rate of 0.9 every 20,000 steps, following [ref] .
An Expert's Guide to Training Physics-informed Neural Networks · Training details · 0.94
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