29 citations · 170 across the 34 of their papers we have counts for
34 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…
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…
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…
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…
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…
Annealed Stein Variational Gradient Descent for Improved Uncertainty Estimation in Full-Waveform Inversion
Miguel Corrales, Sean Berti, Bertrand Denel +3
In recent years, Full-Waveform Inversion (FWI) has been extensively used to derive high-resolution subsurface velocity models from seismic data. However, due to the nonlinearity an…
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…
Multidimensional deconvolution with shared bases
Daria Sushnikova, Matteo Ravasi, David Keyes
We address the estimation of seismic wavefields by means of Multidimensional Deconvolution (MDD) for various redatuming applications. While offering more accuracy than conventional…
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…
Upside down Rayleigh-Marchenko: a practical, yet exact redatuming scheme for seabed seismic acquisitions
Ning Wang, Matteo Ravasi
Ocean-bottom seismic plays a crucial role in resource exploration and monitoring. However, despite its undoubted potential, the use of coarse receiver geometries poses challenges t…
Plug-and-Play regularized 3D seismic inversion with 2D pre-trained denoisers
Nick Luiken, Juan Romero, Miguel Corrales +1
Post-stack seismic inversion is a widely used technique to retrieve high-resolution acoustic impedance models from migrated seismic data. Its modelling operator assumes that a migr…
IntraSeismic: a coordinate-based learning approach to seismic inversion
Juan Romero, Wolfgang Heidrich, Nick Luiken +1
Seismic imaging is the numerical process of creating a volumetric representation of the subsurface geological structures from elastic waves recorded at the surface of the Earth. As…
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…
Laterally constrained low-rank seismic data completion via cyclic-shear transform
David Vargas, Ivan Vasconcelos, Nick Luiken +1
A crucial step in seismic data processing consists in reconstructing the wavefields at spatial locations where faulty or absent sources and/or receivers result in missing data. Sev…
Explainable Artificial Intelligence driven mask design for self-supervised seismic denoising
Claire Birnie, Matteo Ravasi
The presence of coherent noise in seismic data leads to errors and uncertainties, and as such it is paramount to suppress noise as early and efficiently as possible. Self-supervise…
Seis2Rock: A Data-Driven Approach to Direct Petrophysical Inversion of Pre-Stack Seismic Data
Miguel Corrales, Hussein Hoteit, Matteo Ravasi
The inversion of petrophysical parameters from seismic data represents a fundamental step in the process of characterizing the subsurface. We propose a novel, data-driven approach…
Deep Learning-based extraction of surface wave dispersion curves from seismic shot gathers
Danilo Chamorro, Jiahua Zhao, Claire Birnie +3
Multi-channel Analysis of Surface Waves (MASW) is a seismic method employed to obtain useful information about shear-wave velocities in the near surface. A fundamental step in this…
Physics reliable frugal uncertainty analysis for full waveform inversion
Muhammad Izzatullah, Matteo Ravasi, Tariq Alkhalifah
Full waveform inversion (FWI) enables us to obtain high-resolution velocity models of the subsurface. However, estimating the associated uncertainties in the process is not trivial…
PINNslope: seismic data interpolation and local slope estimation with physics informed neural networks
Francesco Brandolin, Matteo Ravasi, Tariq Alkhalifah
Interpolation of aliased seismic data constitutes a key step in a seismic processing workflow to obtain high quality velocity models and seismic images. Building on the idea of des…
Why POCS works, and how to make it better
Matteo Ravasi, Nick Luiken
Projection Over Convex Sets (POCS) is one of the most widely used algorithms in geophysical data processing to interpolate seismic data. Whilst usually described as a modification…
Seeing through the CO2 plume: joint inversion-segmentation of the Sleipner 4D Seismic Dataset
Juan Romero, Nick Luiken, Matteo Ravasi
4D seismic inversion is the leading method to quantitatively monitor fluid flow dynamics in the subsurface, with applications ranging from enhanced oil recovery to subsurface CO2 s…
Posterior sampling with CNN-based, Plug-and-Play regularization with applications to Post-Stack Seismic Inversion
Muhammad Izzatullah, Tariq Alkhalifah, Juan Romero +3
Uncertainty quantification is crucial to inverse problems, as it could provide decision-makers with valuable information about the inversion results. For example, seismic inversion…