Successful leveraging of image processing and machine learning in seismic structural interpretation: A review
As a process that identifies geologic structures of interest such as faults, salt domes, or
elements of petroleum systems in general, seismic structural interpretation depends heavily …
elements of petroleum systems in general, seismic structural interpretation depends heavily …
[HTML][HTML] Current state and future directions for deep learning based automatic seismic fault interpretation: A systematic review
Automated seismic fault interpretation has been an active area of research. Since 2018,
Deep learning (DL) based seismic fault interpretation methods have emerged and shown …
Deep learning (DL) based seismic fault interpretation methods have emerged and shown …
Seismic fault detection in real data using transfer learning from a convolutional neural network pre-trained with synthetic seismic data
The challenging task of automatic seismic fault detection recently gained in quality with the
emergence of deep learning techniques. Those methods successfully take advantage of a …
emergence of deep learning techniques. Those methods successfully take advantage of a …
Seismic facies classification using supervised convolutional neural networks and semisupervised generative adversarial networks
Mapping of seismic and lithologic facies from 3D reflection seismic data plays a key role in
depositional environment analysis and reservoir characterization during hydrocarbon …
depositional environment analysis and reservoir characterization during hydrocarbon …
Regularized elastic full-waveform inversion using deep learning
Z Zhang, T Alkhalifah - Advances in subsurface data analytics, 2022 - Elsevier
Elastic full-waveform inversion, which aims to match the waveforms of prestack seismic data,
potentially provides more accurate high-resolution reservoir characterization from seismic …
potentially provides more accurate high-resolution reservoir characterization from seismic …
[HTML][HTML] Deep convolutional neural network for automatic fault recognition from 3D seismic datasets
With the explosive growth in seismic data acquisition and the successful application of deep
convolutional neural networks (DCNN) to various image processing tasks within …
convolutional neural networks (DCNN) to various image processing tasks within …
Seismic fault detection using convolutional neural networks with focal loss
Fault detection is a fundamental and important research topic in automatic seismic
interpretation since the geometry of faults usually reveals the accumulation and migration of …
interpretation since the geometry of faults usually reveals the accumulation and migration of …
MTL-FaultNet: Seismic data reconstruction assisted multi-task deep learning 3D fault interpretation
W Wu, Y Yang, B Wu, D Ma, Z Tang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Seismic fault interpretation is of extraordinary significant for hydrocarbon reservoir
characterization and drilling hazard mitigation. In recent years, deep learning-based seismic …
characterization and drilling hazard mitigation. In recent years, deep learning-based seismic …
Seismic stratigraphy interpretation by deep convolutional neural networks: A semisupervised workflow
H Di, Z Li, H Maniar, A Abubakar - Geophysics, 2020 - library.seg.org
Depicting geologic sequences from 3D seismic surveying is of significant value to
subsurface reservoir exploration, but it is usually time-and labor-intensive for manual …
subsurface reservoir exploration, but it is usually time-and labor-intensive for manual …
Seismic fault detection using convolutional neural networks trained on synthetic poststacked amplitude maps
A Pochet, PHB Diniz, H Lopes… - IEEE Geoscience and …, 2018 - ieeexplore.ieee.org
Fault detection is a crucial step in reservoir characterization. Despite the many tools
developed in the past decades, automation of this task remains a challenge. We investigate …
developed in the past decades, automation of this task remains a challenge. We investigate …