4 citations · 4 across the 5 of their papers we have counts for
5 papers
Foundation Model-Assisted Full Waveform Inversion
Mustafa Alfarhan, Matteo Ravasi, Fuqiang Chen +2
Full waveform inversion (FWI) can recover high-resolution subsurface velocity models. Conventional waveform-difference objectives, however, are vulnerable to cycle skipping when th…
A Deep Learning-based time shift objective function for Full Waveform Inversion
Mustafa Alfarhan, Fuqiang Chen, George Turkiyyah +3
Full Waveform Inversion (FWI) is a powerful technique for estimating high-resolution subsurface velocity models by minimizing the discrepancy between modeled and observed seismic d…
Reciprocity-aware adaptive tile low-rank factorization for large-scale 3D multidimensional deconvolution
Fuqiang Chen, Matteo Ravasi, David Keyes
Low-rank regularization is an effective technique for addressing ill-posed inverse problems when the unknown variable exhibits low-rank characteristics. However, global low-rank as…
Robust Full Waveform Inversion with deep Hessian deblurring
Mustafa Alfarhan, Matteo Ravasi, Fuqiang Chen +1
Full Waveform Inversion (FWI) is a technique widely used in geophysics to obtain high-resolution subsurface velocity models from waveform seismic data. Due to its large computation…
A reciprocity-aware, low-rank regularization for multidimensional deconvolution
Fuqiang Chen, Matteo Ravasi, David Keyes
This paper presents a novel factorization-based, low-rank regularization method for solving multidimensional deconvolution problems in the frequency domain. In this approach, each…