29 citations · 40 across the 40 of their papers we have counts for
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Diffusion priors enhanced velocity model building from time-lag images using a neural operator
Xiao Ma, Mohammad Hasyim Taufik, Tariq Alkhalifah
Velocity model building serves as a crucial component for achieving high precision subsurface imaging. However, conventional velocity model building methods are often computational…
Diffusion Model-Based Posterior Sampling in Full Waveform Inversion
Mohammad H. Taufik, Tariq Alkhalifah
Bayesian full waveform inversion (FWI) offers uncertainty-aware subsurface models; however, posterior sampling directly on observed seismic shot records is rarely practical at the…
High-resolution velocity model estimation with neural operator and the time-shift imaging condition
Xiao Ma, Shaowen Wang, Tariq Alkhalifah
Extracting subsurface velocity information from seismic data is mainly an undetermined problem that requires injecting a priori information to constrain the inversion process. Mach…
Velocity model building from seismic images using a Convolutional Neural Operator
Xiao Ma, Tariq Alkhalifah
The success of building a high-resolution velocity model using machine learning is hampered by generalization limitations that often limit the success of the approach on field data…
A Deep-Learning-Driven Optimization-Based Inverse Solver for Accelerating the Marchenko Method
Ning Wang, Tariq Alkhalifah
The Marchenko method is a powerful tool for reconstructing full-wavefield Green's functions using surface-recorded seismic data. These Green's functions can then be utilized to pro…
Inspired by machine learning optimization: can gradient-based optimizers solve cycle skipping in full waveform inversion given sufficient iterations?
Xinru Mu, Omar M. Saad, Shaowen Wang +1
Full waveform inversion (FWI) iteratively updates the velocity model by minimizing the difference between observed and simulated data. Due to the high computational cost and memory…
Efficient Upside-Down Rayleigh-Marchenko Imaging through Self-Supervised Focusing Function Estimation
Ning Wang, Matteo Ravasi, Tariq Alkhalifah
The Upside-Down Rayleigh-Marchenko (UD-RM) method has recently emerged as a powerful tool for retrieving subsurface wavefields and images free from artifacts caused by both interna…
An effective physics-informed neural operator framework for predicting wavefields
Xiao Ma, Tariq Alkhalifah
Solving the wave equation is fundamental for geophysical applications. However, numerical solutions of the Helmholtz equation face significant computational and memory challenges.…
Physics-informed conditional diffusion model for generalizable elastic wave-mode separation
Shijun Cheng, Xinru Mu, Tariq Alkhalifah
Traditional elastic wavefield separation methods, while accurate, often demand substantial computational resources, especially for large geological models or 3D scenarios. Purely d…
DiffPINN: Generative diffusion-initialized physics-informed neural networks for accelerating seismic wavefield representation
Shijun Cheng, Tariq Alkhalifah
Physics-informed neural networks (PINNs) offer a powerful framework for seismic wavefield modeling, yet they typically require time-consuming retraining when applied to different v…
SiameseLSRTM: Enhancing least-squares reverse time migration with a Siamese network
Xinru Mu, Omar M. Saad, Tariq Alkhalifah
Least-squares reverse time migration (LSRTM) is an inversion-based imaging method rooted in optimization theory, which iteratively updates the reflectivity model to minimize the di…
DiffusionInv: Prior-enhanced Bayesian Full Waveform Inversion using Diffusion models
Yuanyuan Li, Hao Zhang, Zhuoqi Yan +1
Full waveform inversion (FWI) is an advanced seismic inversion technique for quantitatively estimating subsurface properties. However, with FWI, it is hard to converge to a geologi…
Self-supervised surface-related multiple suppression with multidimensional convolution
Shijun Cheng, Ning Wang, Tariq Alkhalifah
Surface-related multiples pose significant challenges in seismic data processing, often obscuring primary reflections and reducing imaging quality. Traditional methods rely on comp…
Least-Squares-Embedded Optimization for Accelerated Convergence of PINNs in Acoustic Wavefield Simulations
Mohammad Mahdi Abedi, David Pardo, Tariq Alkhalifah
Physics-Informed Neural Networks (PINNs) have shown promise in solving partial differential equations (PDEs), including the frequency-domain Helmholtz equation. However, standard t…
Full waveform inversion with CNN-based velocity representation extension
Xinru Mu, Omar M. Saad, Tariq Alkhalifah
Full waveform inversion (FWI) updates the velocity model by minimizing the discrepancy between observed and simulated data. However, discretization errors in numerical modeling and…
SeparationPINN: Physics-Informed Neural Networks for Seismic P- and S-Wave Mode Separation
Xinru Mu, Shijun Cheng, Tariq Alkhalifah
Accurate separation of P- and S-waves is essential for multi-component seismic data processing, as it helps eliminate interference between wave modes during imaging or inversion, w…
Seismic wavefield solutions via physics-guided generative neural operator
Shijun Cheng, Mohammad H. Taufik, Tariq Alkhalifah
Current neural operators often struggle to generalize to complex, out-of-distribution conditions, limiting their ability in seismic wavefield representation. To address this, we pr…
Gabor-Enhanced Physics-Informed Neural Networks for Fast Simulations of Acoustic Wavefields
Mohammad Mahdi Abedi, David Pardo, Tariq Alkhalifah
Physics-Informed Neural Networks (PINNs) have gained attention for solving partial differential equations, including the scattered Helmholtz equation, due to their flexibility and…
A generative foundation model for an all-in-one seismic processing framework
Shijun Cheng, Randy Harsuko, Tariq Alkhalifah
Seismic data often face challenges in their utilization due to noise contamination, incomplete acquisition, and limited low-frequency information, which hinder accurate subsurface…
Multi-frequency wavefield solutions for variable velocity models using meta-learning enhanced low-rank physics-informed neural network
Shijun Cheng, Tariq Alkhalifah
Physics-informed neural networks (PINNs) face significant challenges in modeling multi-frequency wavefields in complex velocity models due to their slow convergence, difficulty in…