Acoustic Full Waveform Inversion using Hamiltonian Monte Carlo
Inverse Problems, Full Waveform Inversion, Monte Carlo Methods, Hamiltonian Monte Carlo.
Full Waveform Inversion (FWI) is a high-resolution technique used to estimate an ensemble of subsurface models
that describe the physical structure of Earth's interior considering the entire seismic wavefield. Hamiltonian Monte Carlo
(HMC) method has acquired great attention in solving seismic inversion problems due to its capacity of sampling on high-
dimensional model spaces by avoiding the random walk behavior. In this work, we verify the feasibility of using the HMC
method on the FWI problem under the acoustic wave approximation. We consider as laboratory two synthetic velocity models
under different data variance scenarios. The results suggest that the inversion process is strongly dependent on data variance
and on the choice of mass matrix, implying that more efforts are necessary to real data applications.