Deep learning for extracting dispersion curves
High-frequency surface-wave methods have been widely used for surveying near-surface
shear-wave velocities. A key step in high-frequency surface-wave methods is to acquire …
shear-wave velocities. A key step in high-frequency surface-wave methods is to acquire …
Deep learning, machine learning and internet of things in geophysical engineering applications: An overview
Abstract The earthquakes in Eastern Mediterranean are mostly tectonic. The earthquakes
that are 60 km deep in the ground are called Shallow earthquakes. The earthquakes in the …
that are 60 km deep in the ground are called Shallow earthquakes. The earthquakes in the …
Convolutional neural network for seismic impedance inversion
We have addressed the geophysical problem of obtaining an elastic model of the
subsurface from recorded normal-incidence seismic data using convolutional neural …
subsurface from recorded normal-incidence seismic data using convolutional neural …
Deep denoising autoencoder for seismic random noise attenuation
Attenuation of seismic random noise is considered an important processing step to enhance
the signal-to-noise ratio of seismic data. A new approach is proposed to attenuate random …
the signal-to-noise ratio of seismic data. A new approach is proposed to attenuate random …
Deep learning electromagnetic inversion with convolutional neural networks
V Puzyrev - Geophysical Journal International, 2019 - academic.oup.com
Geophysical inversion attempts to estimate the distribution of physical properties in the
Earth's interior from observations collected at or above the surface. Inverse problems are …
Earth's interior from observations collected at or above the surface. Inverse problems are …
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 …
Seismic velocity inversion transformer
Velocity model inversion is one of the most challenging tasks in seismic exploration, and an
accurate velocity model is essential for high-resolution seismic imaging. Recently, velocity …
accurate velocity model is essential for high-resolution seismic imaging. Recently, velocity …
Unsupervised clustering of seismic signals using deep convolutional autoencoders
In this letter, we use deep neural networks for unsupervised clustering of seismic data. We
perform the clustering in a feature space that is simultaneously optimized with the clustering …
perform the clustering in a feature space that is simultaneously optimized with the clustering …
Deep learning reservoir porosity prediction based on multilayer long short-term memory network
The cost of obtaining a complete porosity value using traditional coring methods is relatively
high, and as the drilling depth increases, the difficulty of obtaining the porosity value also …
high, and as the drilling depth increases, the difficulty of obtaining the porosity value also …
Deep-learning seismic full-waveform inversion for realistic structural models
Velocity model inversion is one of the most important tasks in seismic exploration. Full-
waveform inversion (FWI) can obtain the highest resolution in traditional velocity inversion …
waveform inversion (FWI) can obtain the highest resolution in traditional velocity inversion …