29 citations · 40 across the 40 of their papers we have counts for
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Depth-progressive 3D seismic velocity model building via 2D generative diffusion models
Shijun Cheng, Randy Harsuko, Mohammad H. Taufik +1
Three-dimensional (3D) velocity model building (VMB) remains computationally challenging with conventional approaches and native 3D neural networks, largely due to the prohibitive…
Coarse-to-fine multi-resolution hash encoding for implicit full waveform inversion
Linrong Wang, Shaowen Wang, Fan Min +1
Implicit full waveform inversion (IFWI) reparameterizes the velocity model with a neural network, but it suffers from spectral bias and requires many iterations to converge. Multi-…
Shallow-to-deep velocity model building via diffusion models-Part II: Realistic scenarios
Shijun Cheng, Randy Harsuko, Tariq Alkhalifah
Full-waveform inversion (FWI) requires accurate initial velocity models to avoid cycle-skipping, but constructing such models remains challenging in practice. Building on the depth…
Shallow-to-deep velocity model building via diffusion models-Part I: Method and Proof of concept
Shijun Cheng, Randy Harsuko, Tariq Alkhalifah
Seismic velocity model building (VMB) is fundamental for understanding subsurface structures. Traditional methods demand high-quality starting models and, also, remain limited in r…
Post-FWI Injection of Learned Priors Using a Flow Matching Model
Hao Zhang, Tariq Alkhalifah
Full Waveform Inversion (FWI) is a powerful tool for subsurface velocity reconstruction but remains highly ill-posed, sensitive to acquisition limitations, often requiring some for…
Estimation of Elastic Parameters with Guidance-based Diffusion model
Anjali Dixit, Francesco Brandolin, Tariq Alkhalifah
Elastic parameters are fundamental rock properties for reservoir characterization, but their reliable estimation from angle-stack seismic data remains challenging due to strong non…
Joint Velocity Slope Diffusion Prior for Structurally Constrained Velocity Model Building
Francesco Brandolin, Tariq Alkhalifah
High-resolution velocity models are crucial for reservoir characterization and subsurface delineation. However, the band limited nature of our surface recorded data limits resoluti…
Generative wave propagator
Shijun Cheng, Tariq Alkhalifah
Seismic wavefield simulation is fundamental to seismology, but conventional finite-difference (FD) methods remain limited by numerical dispersion and stability constraints, which o…
Mitigating cycle skipping in full waveform inversion using max-pooling-based approximate envelope and shot patching
Xinru Mu, Omar M. Saad, Shaowen Wang +1
Full waveform inversion (FWI) can produce accurate subsurface velocity models. However, the lack of sufficiently low-frequency content in field data often causes cycle skipping and…
Velocity Model Building and Editing with Guided Denoising Diffusion Implicit Models
Francesco Brandolin, Tariq Alkhalifah
Velocity-model building is a fundamental component of seismic imaging, yet it remains a challenging inverse problem due to limited data coverage, nonlinearity, and the need to inte…
Propagating the prior from far to near offset: A self-supervised diffusion framework for progressively recovering near-offsets of towed-streamer data
Shijun Cheng, Tariq Alkhalifah
In marine towed-streamer seismic acquisition, the nearest hydrophone is often two hundred meter away from the source resulting in missing near-offset traces, which degrades critica…
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…
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…