29 citations · 29 across the 6 of their papers we have counts for
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…
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…
Conditional Image Prior for Uncertainty Quantification in Full Waveform Inversion
Lingyun Yang, Omar M. Saad, Guochen Wu +1
Full Waveform Inversion (FWI) is a technique employed to attain a high resolution subsurface velocity model. However, FWI results are effected by the limited illumination of the mo…
Signal Enhancement in Distributed Acoustic Sensing Data Using a Guided Unsupervised Deep Learning Network
Omar M. Saad, Matteo Ravasi, Tariq Alkhalifah
Distributed Acoustic Sensing (DAS) is a promising technology introducing a new paradigm in the acquisition of high-resolution seismic data. However, DAS data often show weak signal…