Mitigating cycle skipping in full waveform inversion using max-pooling-based approximate envelope and shot patching
Fuente:
arXiv
Guardado en:
| Autores principales: | Mu, Xinru, Saad, Omar M., Wang, Shaowen, Alkhalifah, Tariq |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Inspired by machine learning optimization: can gradient-based optimizers solve cycle skipping in full waveform inversion given sufficient iterations?
por: Mu, Xinru, et al.
Publicado: (2025)
por: Mu, Xinru, et al.
Publicado: (2025)
Full waveform inversion with CNN-based velocity representation extension
por: Mu, Xinru, et al.
Publicado: (2025)
por: Mu, Xinru, et al.
Publicado: (2025)
SiameseLSRTM: Enhancing least-squares reverse time migration with a Siamese network
por: Mu, Xinru, et al.
Publicado: (2025)
por: Mu, Xinru, et al.
Publicado: (2025)
Geological and Well prior assisted full waveform inversion using conditional diffusion models
por: Wang, Fu, et al.
Publicado: (2024)
por: Wang, Fu, et al.
Publicado: (2024)
Addressing cycle‐skipping in full‐waveform inversion using acoustic wave energy
por: Zhonglei Li, et al.
Publicado: (2024)
por: Zhonglei Li, et al.
Publicado: (2024)
An efficient pseudoelastic pure P‐mode wave equation and the implementation of the free surface boundary condition
por: Xinru Mu, et al.
Publicado: (2024)
por: Xinru Mu, et al.
Publicado: (2024)
Physics-informed waveform inversion using pretrained wavefield neural operators
por: Huang, Xinquan, et al.
Publicado: (2025)
por: Huang, Xinquan, et al.
Publicado: (2025)
Physics-informed conditional diffusion model for generalizable elastic wave-mode separation
por: Cheng, Shijun, et al.
Publicado: (2025)
por: Cheng, Shijun, et al.
Publicado: (2025)
SeparationPINN: Physics-Informed Neural Networks for Seismic P- and S-Wave Mode Separation
por: Mu, Xinru, et al.
Publicado: (2025)
por: Mu, Xinru, et al.
Publicado: (2025)
High-resolution velocity model estimation with neural operator and the time-shift imaging condition
por: Ma, Xiao, et al.
Publicado: (2025)
por: Ma, Xiao, et al.
Publicado: (2025)
Signal Enhancement in Distributed Acoustic Sensing Data Using a Guided Unsupervised Deep Learning Network
por: Saad, Omar M., et al.
Publicado: (2024)
por: Saad, Omar M., et al.
Publicado: (2024)
A new practical and effective source-independent full-waveform inversion with a velocity-distribution supported deep image prior: Applications to two real datasets
por: Song, Chao, et al.
Publicado: (2025)
por: Song, Chao, et al.
Publicado: (2025)
Conditional Image Prior for Uncertainty Quantification in Full Waveform Inversion
por: Yang, Lingyun, et al.
Publicado: (2024)
por: Yang, Lingyun, et al.
Publicado: (2024)
Learned frequency-domain scattered wavefield solutions using neural operators
por: Huang, Xinquan, et al.
Publicado: (2024)
por: Huang, Xinquan, et al.
Publicado: (2024)
Velocity model building from seismic images using a Convolutional Neural Operator
por: Ma, Xiao, et al.
Publicado: (2025)
por: Ma, Xiao, et al.
Publicado: (2025)
Meta-learning-enhanced implicit full waveform inversion
por: Wang, Zefeng, et al.
Publicado: (2026)
por: Wang, Zefeng, et al.
Publicado: (2026)
A Deep-Learning-Driven Optimization-Based Inverse Solver for Accelerating the Marchenko Method
por: Wang, Ning, et al.
Publicado: (2025)
por: Wang, Ning, et al.
Publicado: (2025)
Bayesian time‐lapse full waveform inversion using Hamiltonian Monte Carlo
por: P. D. S. de Lima, et al.
Publicado: (2024)
por: P. D. S. de Lima, et al.
Publicado: (2024)
A Deep‐Learning‐Driven Optimization‐Based Inverse Solver for Accelerating the Marchenko Method
por: Ning Wang, et al.
Publicado: (2025)
por: Ning Wang, et al.
Publicado: (2025)
Bayesian full waveform inversion with learned prior using deep convolutional autoencoder
por: Hu, Shuhua, et al.
Publicado: (2025)
por: Hu, Shuhua, et al.
Publicado: (2025)
Velocity Model Building and Editing with Guided Denoising Diffusion Implicit Models
por: Brandolin, Francesco, et al.
Publicado: (2026)
por: Brandolin, Francesco, et al.
Publicado: (2026)
Meta-PINN: Meta learning for improved neural network wavefield solutions
por: Cheng, Shijun, et al.
Publicado: (2024)
por: Cheng, Shijun, et al.
Publicado: (2024)
Discovery of physically interpretable wave equations
por: Cheng, Shijun, et al.
Publicado: (2024)
por: Cheng, Shijun, et al.
Publicado: (2024)
GaborPINN: Efficient physics informed neural networks using multiplicative filtered networks
por: Huang, Xinquan, et al.
Publicado: (2023)
por: Huang, Xinquan, et al.
Publicado: (2023)
Multi-frequency wavefield solutions for variable velocity models using meta-learning enhanced low-rank physics-informed neural network
por: Cheng, Shijun, et al.
Publicado: (2025)
por: Cheng, Shijun, et al.
Publicado: (2025)
A probabilistic full waveform inversion of surface waves
por: Sean Berti, et al.
Publicado: (2024)
por: Sean Berti, et al.
Publicado: (2024)
Controllable seismic velocity synthesis using generative diffusion models
por: Wang, Fu, et al.
Publicado: (2024)
por: Wang, Fu, et al.
Publicado: (2024)
Efficient Upside-Down Rayleigh-Marchenko Imaging through Self-Supervised Focusing Function Estimation
por: Wang, Ning, et al.
Publicado: (2025)
por: Wang, Ning, et al.
Publicado: (2025)
Self-supervised surface-related multiple suppression with multidimensional convolution
por: Cheng, Shijun, et al.
Publicado: (2025)
por: Cheng, Shijun, et al.
Publicado: (2025)
Diffusion-based subsurface CO$_2$ multiphysics monitoring and forecasting
por: Huang, Xinquan, et al.
Publicado: (2024)
por: Huang, Xinquan, et al.
Publicado: (2024)
Seismic full waveform inversion for fracture parameters in anisotropic media
por: Ujjwal Shekhar, et al.
Publicado: (2025)
por: Ujjwal Shekhar, et al.
Publicado: (2025)
Scalable Bayesian full waveform inversion via dual augmented Lagrangian SVGD
por: Aghazade, Kamal, et al.
Publicado: (2026)
por: Aghazade, Kamal, et al.
Publicado: (2026)
Robust acoustic and elastic full waveform inversion by adaptive Tikhonov-TV regularization
por: Aghazade, Kamal, et al.
Publicado: (2025)
por: Aghazade, Kamal, et al.
Publicado: (2025)
Microseismic source imaging using physics-informed neural networks with hard constraints
por: Huang, Xinquan, et al.
Publicado: (2023)
por: Huang, Xinquan, et al.
Publicado: (2023)
Propagating the prior from far to near offset: A self-supervised diffusion framework for progressively recovering near-offsets of towed-streamer data
por: Cheng, Shijun, et al.
Publicado: (2026)
por: Cheng, Shijun, et al.
Publicado: (2026)
An effective physics-informed neural operator framework for predicting wavefields
por: Ma, Xiao, et al.
Publicado: (2025)
por: Ma, Xiao, et al.
Publicado: (2025)
Improved elastic full‐waveform inversion of ocean bottom node data
por: Bo Wu, et al.
Publicado: (2024)
por: Bo Wu, et al.
Publicado: (2024)
DiffusionInv: Prior-enhanced Bayesian Full Waveform Inversion using Diffusion models
por: Li, Yuanyuan, et al.
Publicado: (2025)
por: Li, Yuanyuan, et al.
Publicado: (2025)
Bayesian full waveform inversion with sequential surrogate model refinement
por: Meles, Giovanni Angelo, et al.
Publicado: (2025)
por: Meles, Giovanni Angelo, et al.
Publicado: (2025)
A level-set structural approach for multi-physics joint inversion using full-waveform and gravity data
por: Deng, Xingyu, et al.
Publicado: (2025)
por: Deng, Xingyu, et al.
Publicado: (2025)
Ejemplares similares
-
Inspired by machine learning optimization: can gradient-based optimizers solve cycle skipping in full waveform inversion given sufficient iterations?
por: Mu, Xinru, et al.
Publicado: (2025) -
Full waveform inversion with CNN-based velocity representation extension
por: Mu, Xinru, et al.
Publicado: (2025) -
SiameseLSRTM: Enhancing least-squares reverse time migration with a Siamese network
por: Mu, Xinru, et al.
Publicado: (2025) -
Geological and Well prior assisted full waveform inversion using conditional diffusion models
por: Wang, Fu, et al.
Publicado: (2024) -
Addressing cycle‐skipping in full‐waveform inversion using acoustic wave energy
por: Zhonglei Li, et al.
Publicado: (2024)