We consider the problem of seismic reflectivity inversion, which pertains to the high-
resolution recovery of interface locations and reflection coefficients from seismic …

Full waveform inversion (FWI) aims at reconstructing unknown physical coefficients in wave
equations using the wave field data generated from multiple incoming sources. In this work …

We solve the analysis sparse coding problem considering a combination of convex and non-
convex sparsity promoting penalties. The multi-penalty formulation results in an iterative …

The choice of the sensing matrix is crucial in compressed sensing. Random Gaussian
sensing matrices satisfy the restricted isometry property, which is crucial for solving the …

We solve the problem of sparse signal deconvolution in the context of seismic reflectivity
inversion, which pertains to high-resolution recovery of the subsurface reflection coefficients …

The choice of the sensing matrix is crucial in compressed sensing (CS). Gaussian sensing
matrices possess the desirable restricted isometry property (RIP), which is crucial for …

Sparse coding methods are iterative and typically rely on proximal gradient methods. While
the commonly used sparsity promoting penalty is the ℓ 1 norm, alternatives such as the …

This thesis presents a systematic computational investigation of loss functions in solving
inverse problems of partial differential equations. The primary efforts are spent on …

Recent application of machine learning models have shown great potential in reflectivity
inversion of seismic data. However, existing methods usually focus on the framework of …