Deep-learning seismology
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 …
properties of Earth's interior. The availability of large-scale seismic datasets and the …
Applications of deep neural networks in exploration seismology: A technical survey
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 …
controlled (active) source into the ground, and recorded by an array of seismic sensors …
Extracting horizon surfaces from 3D seismic data using deep learning
Extracting horizon surfaces from key reflections in a seismic image is an important step of
the interpretation process. Interpreting a reflection surface in a geologically complex area is …
the interpretation process. Interpreting a reflection surface in a geologically complex area is …
Spatial–temporal prediction of minerals dissolution and precipitation using deep learning techniques: An implication to Geological Carbon Sequestration
Abstract In Geological Carbon Sequestration (GCS), mineralization is a secure carbon
dioxide (CO 2) trap** mechanism to prevent possible leakage at a later stage of the GCS …
dioxide (CO 2) trap** mechanism to prevent possible leakage at a later stage of the GCS …
Systematic literature review on seismic diffraction imaging
The diffractive component of the wavefield is often treated as noise during traditional
preprocessing workflows. However, it possesses valuable information for imaging faults …
preprocessing workflows. However, it possesses valuable information for imaging faults …
Machine learning for seismic exploration: Where are we and how far are we from the holy grail?
Machine-learning (ML) applications in seismic exploration are growing faster than
applications in other industry fields, mainly due to the large amount of acquired data for the …
applications in other industry fields, mainly due to the large amount of acquired data for the …
Separation and imaging of seismic diffractions using a localized rank-reduction method with adaptively selected ranks
Seismic diffractions are weak seismic events hidden within the more dominant reflection
events in a seismic profile. Separating diffraction energy from the poststack seismic profiles …
events in a seismic profile. Separating diffraction energy from the poststack seismic profiles …
[HTML][HTML] Multi-domain diffraction identification: A supervised deep learning technique for seismic diffraction classification
The seismic wavefield arises from interactions of a source wavefield with subsurface
heterogeneities as the wavefield propagates through the earth. In a conventional seismic …
heterogeneities as the wavefield propagates through the earth. In a conventional seismic …
Reflection and diffraction separation in the dip-angle common-image gathers using convolutional neural network
In exploration seismology, reflections have been extensively used for imaging and inversion
to detect hydrocarbon and mine resources, which are generated from subsurface continuous …
to detect hydrocarbon and mine resources, which are generated from subsurface continuous …
Extraction of diffractions from seismic data using convolutional U-net and transfer learning
Diffraction images can be used for modeling reservoir heterogeneities at or below the
seismic wavelength scale. However, the extraction of diffractions is challenging because …
seismic wavelength scale. However, the extraction of diffractions is challenging because …