Intelligent AVA inversion using a convolution neural network trained with pseudo-well datasets
The amplitude-variation-with-angle (AVA) inversion for seismic data has been widely used
for hydrocarbon detection in exploration seismology. Traditional AVA inversion quantitatively …
for hydrocarbon detection in exploration seismology. Traditional AVA inversion quantitatively …
Interpolation and denoising of seismic data using convolutional neural networks
Seismic data processing algorithms greatly benefit from regularly sampled and reliable data.
Therefore, interpolation and denoising play a fundamental role as one of the starting steps of …
Therefore, interpolation and denoising play a fundamental role as one of the starting steps of …
A comparison of deep machine learning and Monte Carlo methods for facies classification from seismic data
Among the large variety of mathematical and computational methods for estimating reservoir
properties such as facies and petrophysical variables from geophysical data, deep machine …
properties such as facies and petrophysical variables from geophysical data, deep machine …
Unsupervised seismic random noise attenuation based on deep convolutional neural network
Random noise attenuation is one of the most essential steps in seismic signal processing.
We propose a novel approach to attenuate seismic random noise based on deep …
We propose a novel approach to attenuate seismic random noise based on deep …
Trace-wise coherent noise suppression via a self-supervised blind-trace deep-learning scheme
Seismic data denoising via supervised deep learning is effective and popular but requires
noise-free labels, which are rarely available. Blind-spot networks circumvent this …
noise-free labels, which are rarely available. Blind-spot networks circumvent this …
Applying machine learning to 3D seismic image denoising and enhancement
E Wang, J Nealon - Interpretation, 2019 - library.seg.org
We have trained a supervised deep 3D convolutional neural network (CNN) on marine
seismic images for poststack structural seismic image enhancement and noise attenuation …
seismic images for poststack structural seismic image enhancement and noise attenuation …
Self-Attention Generative Adversarial Network Interpolating and Denoising Seismic Signals Simultaneously
M Ding, Y Zhou, Y Chi - Remote Sensing, 2024 - mdpi.com
In light of the challenging conditions of exploration environments coupled with escalating
exploration expenses, seismic data acquisition frequently entails the capturing of signals …
exploration expenses, seismic data acquisition frequently entails the capturing of signals …
Seismic noise attenuation by signal reconstruction: An unsupervised machine learning approach
Y Gao, P Zhao, G Li, H Li - Geophysical Prospecting, 2021 - earthdoc.org
Random noise attenuation is an essential step in seismic data processing for improving
seismic data quality and signal‐to‐noise ratio. We adopt an unsupervised machine learning …
seismic data quality and signal‐to‐noise ratio. We adopt an unsupervised machine learning …
Coherent noise suppression via a self-supervised blind-trace deep learning scheme
Coherent noise regularly plagues seismic recordings, causing artefacts and uncertainties in
products derived from down-the-line processing and imaging tasks. The outstanding …
products derived from down-the-line processing and imaging tasks. The outstanding …
BSnet: An unsupervised blind spot network for seismic data random noise attenuation
W Fang, L Fu, H Li, S Liu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Existing deep learning-based seismic data denoising methods mainly involve supervised
learning, in which a denoising network is trained using a large amount of noisy input/clean …
learning, in which a denoising network is trained using a large amount of noisy input/clean …