6 papers
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…
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…
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…
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…
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…