{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1390866580903004544.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.4294/zisin.2024-9"}},{"identifier":{"@type":"NDL_BIB_ID","@value":"034034807"}},{"identifier":{"@type":"URI","@value":"http://id.ndl.go.jp/bib/034034807"}},{"identifier":{"@type":"URI","@value":"https://ndlsearch.ndl.go.jp/books/R000000004-I034034807"}},{"identifier":{"@type":"URI","@value":"https://www.jstage.jst.go.jp/article/zisin/77/0/77_2024-9/_pdf"}}],"dc:title":[{"@language":"ja","@value":"Scientific Machine Learning 地震学"},{"@language":"en","@value":"Scientific Machine Learning Seismology"},{"@language":"ja-Kana","@value":"Scientific Machine Learning ジシンガク"}],"dc:language":"ja","creator":[{"@id":"https://cir.nii.ac.jp/crid/1410866580903004544","@type":"Researcher","foaf:name":[{"@language":"ja","@value":"岡﨑 智久"},{"@language":"en","@value":"OKAZAKI Tomohisa"}],"jpcoar:affiliationName":[{"@language":"en","@value":"RIKEN Center for Advanced Intelligence Project"},{"@language":"ja","@value":"理化学研究所革新知能統合研究センター"}]}],"publication":{"publicationIdentifier":[{"@type":"PISSN","@value":"00371114"},{"@type":"LISSN","@value":"00371114"},{"@type":"EISSN","@value":"18839029"},{"@type":"EISSN","@value":"2186599X"},{"@type":"NDL_BIB_ID","@value":"000000009860"},{"@type":"ISSN","@value":"00371114"},{"@type":"NCID","@value":"AN00305741"}],"prism:publicationName":[{"@language":"en","@value":"Zisin (Journal of the Seismological Society of Japan. 2nd ser.)"},{"@language":"ja","@value":"地震　第２輯"},{"@value":"地震　第2輯"},{"@language":"ja","@value":"地震"},{"@language":"ja","@value":"地震　第２輯"},{"@language":"en","@value":"JSSJ"},{"@language":"en","@value":"Zisin"},{"@language":"en","@value":"BSSJ"},{"@language":"en","@value":"Zisin (Journal of the Seismological Society of Japan. 2nd ser.)"},{"@language":"en","@value":"Zisin1"},{"@language":"ja","@value":"地震１"},{"@language":"ja","@value":"地震　第１輯"}],"dc:publisher":[{"@language":"en","@value":"SEISMOLOGICAL SOCIETY OF JAPAN"},{"@language":"ja","@value":"公益社団法人 日本地震学会"}],"prism:publicationDate":"2025-01-29","prism:volume":"77","prism:number":"0","prism:startingPage":"101","prism:endingPage":"120"},"reviewed":"false","url":[{"@id":"http://id.ndl.go.jp/bib/034034807"},{"@id":"https://ndlsearch.ndl.go.jp/books/R000000004-I034034807"},{"@id":"https://www.jstage.jst.go.jp/article/zisin/77/0/77_2024-9/_pdf"}],"availableAt":"2025-01-29","relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1050026026867736192","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["references"],"jpcoar:relatedTitle":[{"@language":"en","@value":"Physics-Informed Neural Networks for Fault Slip Monitoring: Simulation, Frictional Parameter Estimation, and Prediction on Slow Slip Events in a Spring-Slider System"},{"@value":"Physics‐Informed Neural Networks for Fault Slip Monitoring: Simulation, Frictional Parameter Estimation, and Prediction on Slow Slip Events in a Spring‐Slider System"}]},{"@id":"https://cir.nii.ac.jp/crid/1360004233436596608","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Volcanic subsidence triggered by the 2011 Tohoku earthquake in Japan"}]},{"@id":"https://cir.nii.ac.jp/crid/1360013172862379392","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Physics-informed machine learning"}]},{"@id":"https://cir.nii.ac.jp/crid/1360013267328434304","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Physics-informed neural networks (PINNs) for fluid mechanics: a review"}]},{"@id":"https://cir.nii.ac.jp/crid/1360017280655570304","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Monotonic Neural Network for Ground-Motion Predictions to Avoid Overfitting to Recorded Sites"}]},{"@id":"https://cir.nii.ac.jp/crid/1360017282211611392","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Comment on ‘Geophysical inversion and optimal transport’ by M. Sambridge, A. Jackson and A. P. Valentine"}]},{"@id":"https://cir.nii.ac.jp/crid/1360017290175836032","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking"}]},{"@id":"https://cir.nii.ac.jp/crid/1360019301657832320","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Physics‐Informed Neural Networks (PINNs) for Wave Propagation and Full Waveform Inversions"}]},{"@id":"https://cir.nii.ac.jp/crid/1360020999900865152","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Deep-learning seismology"}]},{"@id":"https://cir.nii.ac.jp/crid/1360021393297135872","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Using a physics-informed neural network and fault zone acoustic monitoring to predict lab earthquakes"}]},{"@id":"https://cir.nii.ac.jp/crid/1360021393787817216","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Learning the solution operator of parametric partial differential equations with physics-informed DeepONets"}]},{"@id":"https://cir.nii.ac.jp/crid/1360021395157736960","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"EikoNet: Solving the Eikonal Equation With Deep Neural Networks"}]},{"@id":"https://cir.nii.ac.jp/crid/1360022497406089216","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Neural operators for accelerating scientific simulations and design"}]},{"@id":"https://cir.nii.ac.jp/crid/1360022497407102464","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Broadband Ground-Motion Simulations with Machine-Learning-Based High-Frequency Waves from Fourier Neural Operators"}]},{"@id":"https://cir.nii.ac.jp/crid/1360022497888963328","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Respecting causality for training physics-informed neural networks"}]},{"@id":"https://cir.nii.ac.jp/crid/1360022497893198976","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Bayesian Physics-Informed Neural Networks for the Subsurface Tomography Based on the Eikonal Equation"}]},{"@id":"https://cir.nii.ac.jp/crid/1360022500395458048","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence"}]},{"@id":"https://cir.nii.ac.jp/crid/1360022501514906752","@type":"Article","resourceType":"preprint","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Physics-Informed Deep Learning for Forward and Inverse Modeling of Inplane Crustal Deformation"}]},{"@id":"https://cir.nii.ac.jp/crid/1360022501529179904","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Time Variable Stress Inversion of Centroid Moment Tensor Data Using Gaussian Processes"}]},{"@id":"https://cir.nii.ac.jp/crid/1360294645713907456","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Ground-Motion Prediction Model Based on Neural Networks to Extract Site Properties from Observational Records"}]},{"@id":"https://cir.nii.ac.jp/crid/1360298754828881408","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Simulation of broad-band ground motions with consistent long-period and short-period components using the Wasserstein interpolation of acceleration envelopes"}]},{"@id":"https://cir.nii.ac.jp/crid/1360298761956309120","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Geophysical inversion and optimal transport"}]},{"@id":"https://cir.nii.ac.jp/crid/1360302865735635072","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Bayesian Seismic Tomography Based on Velocity-Space Stein Variational Gradient Descent for Physics-Informed Neural Network"}]},{"@id":"https://cir.nii.ac.jp/crid/1360302869321227264","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems"}]},{"@id":"https://cir.nii.ac.jp/crid/1360302870940585984","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Eikonal Tomography With Physics‐Informed Neural Networks: Rayleigh Wave Phase Velocity in the Northeastern Margin of the Tibetan Plateau"}]},{"@id":"https://cir.nii.ac.jp/crid/1360303972864338688","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Physics-informed deep learning of rate-and-state fault friction"}]},{"@id":"https://cir.nii.ac.jp/crid/1360303972865840896","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Seismic Traveltime Simulation for Variable Velocity Models Using Physics-Informed Fourier Neural Operator"}]},{"@id":"https://cir.nii.ac.jp/crid/1360303976491875456","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Physics-informed neural network reconciles Australian displacements and tectonic stresses"}]},{"@id":"https://cir.nii.ac.jp/crid/1360579819782913792","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data"}]},{"@id":"https://cir.nii.ac.jp/crid/1360584346290519680","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Phase Neural Operator for Multi‐Station Picking of Seismic Arrivals"}]},{"@id":"https://cir.nii.ac.jp/crid/1360584346291016192","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Wavefield Reconstruction Inversion via Physics-Informed Neural Networks"}]},{"@id":"https://cir.nii.ac.jp/crid/1360584346470242944","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"3D elastic wave propagation with a Factorized Fourier Neural Operator (F-FNO)"}]},{"@id":"https://cir.nii.ac.jp/crid/1360584346470309504","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Neural Eikonal solver: Improving accuracy of physics-informed neural networks for solving eikonal equation in case of caustics"}]},{"@id":"https://cir.nii.ac.jp/crid/1360584346470848256","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Solving the frequency-domain acoustic VTI wave equation using physics-informed neural networks"}]},{"@id":"https://cir.nii.ac.jp/crid/1360584346470940544","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Rapid Seismic Waveform Modeling and Inversion With Neural Operators"}]},{"@id":"https://cir.nii.ac.jp/crid/1360584346960539520","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks"}]},{"@id":"https://cir.nii.ac.jp/crid/1360584346964317696","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"HypoSVI: Hypocentre inversion with Stein variational inference and physics informed neural networks"}]},{"@id":"https://cir.nii.ac.jp/crid/1360585447395931520","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Wavefield solutions from machine learned functions constrained by the Helmholtz equation"}]},{"@id":"https://cir.nii.ac.jp/crid/1360585451297910656","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain"}]},{"@id":"https://cir.nii.ac.jp/crid/1360585451325106560","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Learning Free-Surface Flow with Physics-Informed Neural Networks"}]},{"@id":"https://cir.nii.ac.jp/crid/1360861711991975808","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators"}]},{"@id":"https://cir.nii.ac.jp/crid/1360865815689043456","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Recent advances in earthquake seismology using machine learning"}]},{"@id":"https://cir.nii.ac.jp/crid/1360865817572458624","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Physics-informed deep learning approach for modeling crustal deformation"}]},{"@id":"https://cir.nii.ac.jp/crid/1360865817575106688","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Seismic Wave Propagation and Inversion with Neural Operators"}]},{"@id":"https://cir.nii.ac.jp/crid/1360865818472149888","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems"}]},{"@id":"https://cir.nii.ac.jp/crid/1360865821266730368","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Eikonal Solution Using Physics-Informed Neural Networks"}]},{"@id":"https://cir.nii.ac.jp/crid/1360865821445139584","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"PINNeik: Eikonal solution using physics-informed neural networks"}]},{"@id":"https://cir.nii.ac.jp/crid/1360866922818561920","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Physics-informed deep learning quantifies propagated uncertainty in seismic structure and hypocenter determination"}]},{"@id":"https://cir.nii.ac.jp/crid/1360866922818815360","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Fault geometry invariance and dislocation potential in antiplane crustal deformation: physics-informed simultaneous solutions"}]},{"@id":"https://cir.nii.ac.jp/crid/1360866926274009600","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Physics informed neural network can retrieve rate and state friction parameters from acoustic monitoring of laboratory stick-slip experiments"}]},{"@id":"https://cir.nii.ac.jp/crid/1360866926279218560","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"A neural network based global traveltime function (GlobeNN)"}]},{"@id":"https://cir.nii.ac.jp/crid/1360866926445100928","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning"}]},{"@id":"https://cir.nii.ac.jp/crid/1360866926445165440","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Slip Tendency Analysis From Sparse Stress and Satellite Data Using Physics‐Guided Deep Neural Networks"}]},{"@id":"https://cir.nii.ac.jp/crid/1360866926446135936","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Physics-Informed Neural Operator for Learning Partial Differential Equations"}]},{"@id":"https://cir.nii.ac.jp/crid/1360866926446565504","@type":"Article","resourceType":"preprint","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Physics-Informed Deep Learning for Estimating the Spatial Distribution of Frictional Parameters in Slow Slip Regions"}]},{"@id":"https://cir.nii.ac.jp/crid/1361137043812297984","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Computational Optimal Transport with Applications to Data Sciences"}]},{"@id":"https://cir.nii.ac.jp/crid/1361137046117861888","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Enabling large‐scale viscoelastic calculations via neural network acceleration"}]},{"@id":"https://cir.nii.ac.jp/crid/1361418521339472384","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Sliced and Radon Wasserstein Barycenters of Measures"}]},{"@id":"https://cir.nii.ac.jp/crid/1361418521353816064","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Measuring the misfit between seismograms using an optimal transport distance: application to full waveform inversion"}]},{"@id":"https://cir.nii.ac.jp/crid/1361981470187842176","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Gaussian Processes for Machine Learning"}]},{"@id":"https://cir.nii.ac.jp/crid/1362825893263312768","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Generalized Seismic Phase Detection with Deep Learning"}]},{"@id":"https://cir.nii.ac.jp/crid/1362825894476717440","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations"}]},{"@id":"https://cir.nii.ac.jp/crid/1362825895328406016","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations"}]},{"@id":"https://cir.nii.ac.jp/crid/1363107369957321600","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Crustal deformation across and beyond the Los Angeles basin from geodetic measurements"}]},{"@id":"https://cir.nii.ac.jp/crid/1363670321223225216","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method"}]},{"@id":"https://cir.nii.ac.jp/crid/1363951795020231168","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"CMT data inversion using a Bayesian information criterion to estimate seismogenic stress fields"}]},{"@id":"https://cir.nii.ac.jp/crid/1363951796264054400","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Geodetic data inversion using a Bayesian information criterion for spatial distribution of fault slip"}]},{"@id":"https://cir.nii.ac.jp/crid/1364233268413443072","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data"}]},{"@id":"https://cir.nii.ac.jp/crid/1390282681489147648","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@language":"en","@value":"A decade of GEONET: 1994-2003"},{"@value":"A decade of GEONET: 1994-2003--The continuous GPS observation in Japan and its impact on earthquake studies"},{"@language":"ja-Kana","@value":"decade of GEONET 1994 2003 The continuous GPS observation in Japan and its impact on earthquake studies"},{"@value":"A decade of GEONET: 1994–2003 —The continuous GPS observation in Japan and its impact on earthquake studies—"},{"@value":"1994–2003 The continuous GPS observation in Japan and its impact on earthquake studies"},{"@value":"A decade of GEONET: 1994–2003—The continuous GPS observations in Japan and its impact on earthquake studies"},{"@value":"A decade of GEONET: 1994–2003—The continuous GPS observation in Japan and its impact on earthquake studies"}]},{"@id":"https://cir.nii.ac.jp/crid/1390294045393167872","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["references"],"jpcoar:relatedTitle":[{"@language":"en","@value":"Strain-rate Field of Japan Estimated from GNSS Data and Its Interpretation Based on Inter- and Intra-arc Deformation"},{"@language":"ja","@value":"GNSSデータに基づく日本列島の歪み速度場と島弧間および島弧内変動"},{"@language":"ja-Kana","@value":"GNSS データ ニ モトズク ニホン レットウ ノ ヒズミ ソクドジョウ ト トウコ カン オヨビ トウコ ナイ ヘンドウ"}]},{"@id":"https://cir.nii.ac.jp/crid/1390587196513759232","@type":"Article","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@language":"ja","@value":"ベイズ推定によるすべりインバージョン研究の高度化"},{"@language":"en","@value":"Advances in Fault-Slip Inversion Using Bayesian Inference"},{"@language":"ja-Kana","@value":"ベイズ スイテイ ニ ヨル スベリ インバージョン ケンキュウ ノ コウドカ"}]},{"@id":"https://cir.nii.ac.jp/crid/2051433317034586368","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Consistent estimation of strain-rate fields from GNSS velocity data using basis function expansion with ABIC"}]}],"dataSourceIdentifier":[{"@type":"JALC","@value":"oai:japanlinkcenter.org:2013942819"},{"@type":"NDL_SEARCH","@value":"oai:ndlsearch.ndl.go.jp:R000000004-I034034807"},{"@type":"CROSSREF","@value":"10.4294/zisin.2024-9"},{"@type":"CROSSREF","@value":"10.4294/zisin.2024-23_references_DOI_Vh8QfgLnjQ0mhazbXz4V1FyMxAP"}]}