Data-driven deep-learning full-waveform inversion (DD-DLFWI) can efficiently reconstruct a
velocity image of the subsurface from prestack seismic recordings, once the deep-learning …

Five alternative algorithms were evaluated to circumvent the excessive storage requirement
imposed by saving source wavefield snapshots used for the crosscorrelation image …

Divergence and curl operators used for the decomposition of P-and S-wave modes in elastic
reverse time migration (RTM) change the amplitudes, units, and phases of extrapolated …

We introduce a technique to compute exact anelastic sensitivity kernels in the time domain
using parsimonious disk storage. The method is based on a reordering of the time loop of …

Time-domain elastic least-squares reverse time migration (LSRTM) is formulated as a
linearized elastic full-waveform inversion problem. The elastic Born approximation and …

Wavefield energy can be measured by the so-called energy norm. We have extended the
concept of “norm” to obtain the energy inner product between two related wavefields …

Elastic reverse time migration (RTM) has the ability to retrieve accurately migrated images of
complex subsurface structures by imaging the multicomponent seismic data. However, the …

We introduce a robust method to produce long wavelength updates in gradient-based Full
Waveform Inversion (FWI). The solution introduces dynamic weights in the velocity sensitivity …

In recent years, the application of time-domain adjoint methods to improve large, complex
underground tomographic models at the regional scale has led to new challenges for the …

Least-squares migration (LSM) is commonly regarded as an amplitude-preserving or true
amplitude migration algorithm that, compared with conventional migration, can provide …