Seismic waves from earthquakes and other sources are used to infer the structure and
properties of Earth's interior. The availability of large-scale seismic datasets and the …

Recently deep learning (DL), as a new data‐driven technique compared to conventional
approaches, has attracted increasing attention in geophysical community, resulting in many …

Seismic inversion is a fundamental tool in geophysical analysis, providing a window into
Earth. In particular, it enables the reconstruction of large-scale subsurface Earth models for …

One of the key objectives in geophysics is to characterize the subsurface through the
process of analyzing and interpreting geophysical field data that are typically acquired at the …

An inversion algorithm is commonly used to estimate the elastic properties, such as P-wave
velocity (VP), S-wave velocity (VS), and density (ρ) of the earth's subsurface. Generally, the …

Pressure prediction has long been one of subject of research focuses in petroleum geology
and exploration, but is traditionally limited to moderately overpressured formations due to …

The determination of subsurface elastic property models is crucial in quantitative seismic
data processing and interpretation. This problem is commonly solved by deterministic …

The physics that describes the seismic response of an interval of saturated porous rocks with
known petrophysical properties is relatively well understood and includes rock physics …

Exploration seismology uses reflected and refracted seismic waves, emitted from a
controlled (active) source into the ground, and recorded by an array of seismic sensors …

In this letter, we use a fully convolutional residual network (FCRN) for seismic impedance
inversion. After training with appropriate data, the FCRN can effectively predict impedance …