Deep-learning (DL) methods have shown promising performance in predicting acoustic
impedance from seismic data that is typically considered as an ill-posed problem for …

The analysis of acoustic impedance (AI) allows for the mapping of seismic reflection data to
lithology, and hence it plays an important role in the interpretation of poststack seismic data …

The impedance inversion technique plays a crucial role in seismic reservoir properties
prediction. However, most existing impedance inversion methods often suffer from spatial …

Prestack acoustic full-waveform inversion (FWI) can provide long-wavelength components of
the P-wave velocity by using low frequencies and long-offset direct/diving/refracted waves …

We present an efficient deconvolution method to retrieve sparse reflectivity series from
seismic data in the presence of additive Gaussian and non-Gaussian noise. The problem is …

Seismic acoustic impedance inversion (SAII) aims at recovering the subsurface impedance
to achieve lithology interpretation. However, its ill-posedness and nonlinearity pose a great …

Deconvolution is an essential step in seismic data processing. Sparse-spike deconvolution
often is used to enhance the resolution of the seismic image by adding a model-driven …

Velocity model building and impedance inversion generally suffer from a lack of
intermediate wavenumber content in seismic data. Intermediate wavenumbers may be …

ABSTRACT A spectral sparse Bayesian learning reflectivity inversion method, combining
spectral reflectivity inversion with sparse Bayesian learning, is presented in this paper. The …

One of reverse time migration's main limitations is that an unscaled adjoint operator is prone
to produce images with low resolution, inaccurate amplitudes, and even artifacts. Least …