Copertina

Three-dimensional crustal deformation analysis using physics-informed deep learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Okazaki, Tomohisa, Ito, Takeo, Hirahara, Kazuro, Agata, Ryoichiro, Kano, Masayuki, Ueda, Naonori
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Geophysics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Physics-informed deep learning links geodetic data and fault friction
di: Fukushima, Rikuto, et al.
Pubblicazione: (2026)

Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks
di: Agata, Ryoichiro, et al.
Pubblicazione: (2026)

Physics-informed deep learning quantifies propagated uncertainty in seismic structure and hypocenter determination
di: Agata, Ryoichiro, et al.
Pubblicazione: (2024)

Quantification of Uncertainty and Its Propagation in Seismic Velocity Structure and Earthquake Source Inversion
di: Agata, Ryoichiro
Pubblicazione: (2024)

HypoNet Nankai: Rapid hypocenter determination tool for the Nankai Trough subduction zone using physics-informed neural networks
di: Agata, Ryoichiro, et al.
Pubblicazione: (2024)

Physics-Informed Linear Model (PILM): Analytical Representations and Application to Crustal Strain Rate Estimation
di: Okazaki, Tomohisa
Pubblicazione: (2025)

Scientific Machine Learning Seismology
di: Okazaki, Tomohisa
Pubblicazione: (2024)

PINN-based short-term forecasting of fault slip evolution during the 2010 slow slip event in the Bungo Channel, Japan
di: Kano, Masayuki, et al.
Pubblicazione: (2026)

OkadaTorch: A Differentiable Programming of Okada Model to Calculate Displacements and Strains from Fault Parameters
di: Someya, Masayoshi, et al.
Pubblicazione: (2025)

Groundwater depletion in North West India and its response on crustal deformation
di: Shubham K. Rajewar, et al.
Pubblicazione: (2026)

A new True Triaxial Apparatus with pore fluid system for rock deformation under representative crustal stress conditions
di: Stanton-Yonge, Ashley, et al.
Pubblicazione: (2025)

Monitoring of tectonic deformation in the seismic gap of the Mentawai Islands using ALOS-1 and ALOS-2
di: Razi, Pakhrur, et al.
Pubblicazione: (2025)

Enhanced receiver function imaging of crustal structures using symmetric autoencoders
di: Koireng, T. Rengneichuong, et al.
Pubblicazione: (2024)

Postseismicity of slow-slip doublets discerned on the outermost of the Nankai Trough subduction megathrust
di: Sato, Dye SK, et al.
Pubblicazione: (2025)

Three-dimensional multi-physics simulation and sensitivity analysis of cyclic hydrogen storage in salt caverns
di: Honório, Hermínio Tasinafo, et al.
Pubblicazione: (2024)

Integrating Newton's Laws with deep learning for enhanced physics-informed compound flood modelling
di: Radfar, Soheil, et al.
Pubblicazione: (2025)

Virtual receiver functions via conditional diffusion transformers for robust crustal imaging
di: Koireng, Tiente R., et al.
Pubblicazione: (2026)

Advancing resistivity-chargeability modeling for complex subsurface characterization using machine learning and deep learning
di: Akingboye, Adedibu Sunny, et al.
Pubblicazione: (2025)

Bayesian full waveform inversion with learned prior using deep convolutional autoencoder
di: Hu, Shuhua, et al.
Pubblicazione: (2025)

Deep‐salt: Complete three‐dimensional salt segmentation from inaccurate migrated subsurface offset gathers using deep learning
di: Ana P. O. Muller, et al.
Pubblicazione: (2024)

Towards deep learning for seismic demultiple
di: Mario R. Fernandez, et al.
Pubblicazione: (2025)

Seismic traveltime simulation for variable velocity models using physics-informed Fourier neural operator
di: Song, Chao, et al.
Pubblicazione: (2023)

Generative deep learning improves reconstruction of global historical climate records
di: Qian, Zhen, et al.
Pubblicazione: (2026)

Generalized Runcorn's theorem and crustal magnetism
di: Smolić, Ivica
Pubblicazione: (2024)

A fine-tuning workflow for automatic first-break picking with deep learning
di: Mardan, Amir, et al.
Pubblicazione: (2024)

Assessing the risk of future Dunkelflaute events for Germany using generative deep learning
di: Strnad, Felix, et al.
Pubblicazione: (2025)

Simultaneous P‐ and S‐wave seismic traveltime tomography using physics‐informed neural networks
di: Chao Song, et al.
Pubblicazione: (2025)

Towards a multi-physics multi-scale approach of deep geothermal exploration
di: Darnet, M., et al.
Pubblicazione: (2024)

Multi-frequency wavefield solutions for variable velocity models using meta-learning enhanced low-rank physics-informed neural network
di: Cheng, Shijun, et al.
Pubblicazione: (2025)

A deep learning framework for marine acoustic and seismic monitoring with distributed acoustic sensing
di: Zhang, Chun, et al.
Pubblicazione: (2026)

Exploring Ultra Rapid Data Assimilation Based on Ensemble Transform Kalman Filter with the Lorenz 96 Model
di: Kawasaki, Fumitoshi, et al.
Pubblicazione: (2025)

Accurate identification of salt domes using deep learning techniques: Transformers, generative artificial intelligence and liquid state machines
di: Kamal Souadih, et al.
Pubblicazione: (2024)

PINNPStomo: Simultaneous P- and S-wave seismic traveltime tomography using physics-informed neural networks with a new factored eikonal equation
di: Song, Chao, et al.
Pubblicazione: (2024)

Three‐dimensional gravity inversion based on the Hamiltonian Monte Carlo method
di: Ya Xu, et al.
Pubblicazione: (2025)

Accurate background velocity model building method based on iterative deep learning in sparse transform domain
di: Chen, Guoxin
Pubblicazione: (2024)

Three‐dimensional electromagnetic inversion of transfer function data from controlled sources
di: Paula Rulff, et al.
Pubblicazione: (2024)

Applications of deep learning‐based resolution‐enhanced seismic data in fault identification
di: Lei Lin, et al.
Pubblicazione: (2025)

Fast approximate simulation of seismic waves with deep learning
di: Moseley, Benjamin, et al.
Pubblicazione: (2018)

GaborPINN: Efficient physics informed neural networks using multiplicative filtered networks
di: Huang, Xinquan, et al.
Pubblicazione: (2023)

Velocity model building via combining seismic slope tomography and supervised deep learning
di: Yao Huang, et al.
Pubblicazione: (2024)