9 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…
Representations for multidimensional down-down deconvolution of ocean-bottom seismic data: theory and practical implications
Kees Wapenaar, Matteo Ravasi, Claudio Bagaini
Multidimensional up-down deconvolution effectively eliminates surface-related multiples from ocean-bottom seismic data. Recently, several down-down deconvolution methods have been…
Uncertainty Quantification in HSI Reconstruction using Physics-Aware Diffusion Priors and Optics-Encoded Measurements
Juan Romero, Qiang Fu, Matteo Ravasi +1
Hyperspectral image reconstruction from a compressed measurement is a highly ill-posed inverse problem. Current data-driven methods suffer from hallucination due to the lack of spe…
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…
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…
Geophysical inverse problems with measurement-guided diffusion models
Matteo Ravasi
Solving inverse problems with the reverse process of a diffusion model represents an appealing avenue to produce highly realistic, yet diverse solutions from incomplete and possibl…