7 papers
Depth-Aware Implicit Neural Representation Priors for 3D Gravity Inversion
León Suarez-Rodriguez, Paul Goyes-Peñafiel, Javier Torres-Quintero +1
Gravimetry images subsurface density contrasts associated with geological structures, geothermal systems, and intrusive bodies. Recovering a three-dimensional density model from gr…
DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems
Romario Gualdrón-Hurtado, Roman Jacome, Leon Suarez +1
Solving imaging inverse problems has usually been addressed by designing proper prior models of the underlying signal. However, minimizing the data fidelity term poses significant…
GSNR: Graph Smooth Null-Space Representation for Inverse Problems
Romario Gualdrón-Hurtado, Roman Jacome, Rafael S. Suarez +1
Inverse problems in imaging are ill-posed, leading to infinitely many solutions consistent with the measurements due to the non-trivial null-space of the sensing matrix. Common ima…
End-to-end optimization of sparse ultrasound linear probes
Sergio Urrea, Adrian Basarab, Hervé Liebgott +1
Ultrasound imaging faces a trade-off between image quality and hardware complexity caused by dense transducers. Sparse arrays are one popular solution to mitigate this challenge. T…
Poststack Seismic Data Preconditioning via Dynamic Guided Learning
Javier Torres-Quintero, Paul Goyes-Peñafiel, Ana Mantilla-Dulcey +3
Seismic data preconditioning is essential for subsurface interpretation. It enhances signal quality while attenuating noise, improving the accuracy of geophysical tasks that would…
Physically Guided Deep Unsupervised Inversion for 1D Magnetotelluric Models
Paul Goyes-Peñafiel, Umair bin Waheed, Henry Arguello
The global demand for unconventional energy sources such as geothermal energy and white hydrogen requires new exploration techniques for precise subsurface structure characterizati…