Machine learning for subsurface geological feature identification from seismic data: Methods, datasets, challenges, and opportunities
Identification of geological features from seismic data such as faults, salt bodies, and
channels, is essential for studies of the shallow Earth, natural disaster forecasting and …
channels, is essential for studies of the shallow Earth, natural disaster forecasting and …
Seismic volumetric dip estimation via multichannel deep learning model
Although there are plenty of approaches proposed for addressing seismic volumetric dip
estimation, it still suffers from several limitations, for example, the expensive computation …
estimation, it still suffers from several limitations, for example, the expensive computation …
A deep learning-based seismic horizon tracking method with uncertainty encoding and vertical constraint
Z Liao, P Zhu, H Zhang, Z Li, M Ali - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Deep learning-based seismic horizon tracking methods have been extensively researched
in the past few years. However, the predicted results of previous methods are currently …
in the past few years. However, the predicted results of previous methods are currently …
Automatic tracking for seismic horizons using convolution feature analysis and optimization algorithm
Seismic horizon tracking is a fundamental aspect of seismic data interpretation. However,
seismic horizons are typically obtained using manual tracking or a combination of manual …
seismic horizons are typically obtained using manual tracking or a combination of manual …
Seismic attributes aided horizon interpretation using an ensemble dense inception transformer network
Horizon picking is of paramount importance in seismic interpretation because it has a
significant impact on subsequent interpretation and inversion. Although manual and various …
significant impact on subsequent interpretation and inversion. Although manual and various …
Seismic stratigraphic interpretation based on deep active learning
Seismic stratigraphic interpretation plays an important role in geophysics and geosciences.
Recently, deep learning has been explored for seismic stratigraphic interpretation. However …
Recently, deep learning has been explored for seismic stratigraphic interpretation. However …
Quantitative appraisal of tectonically-influenced hydrocarbon-bearing Late-Cretaceous fluvial depositional system, Southwest Pakistan using spectral waveform …
MT Naseer - Journal of Asian Earth Sciences, 2025 - Elsevier
Quantitative seismic reservoir characterization is among the finest advancements in seismic
technologies for sub-surface exploration of fluvial depositional systems (FDSS). These …
technologies for sub-surface exploration of fluvial depositional systems (FDSS). These …
SHBGAN: Hybrid Bilateral Attention GAN for Seismic Image Super-Resolution Reconstruction
T Zhong, F Yang, X Dong, S Dong… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The super-resolution reconstruction for seismic images obtained by multi-step processing of
field data is essential due to the noise contamination, sparse geometry and low dominant …
field data is essential due to the noise contamination, sparse geometry and low dominant …
Multiple attribute regression network for 3-D seismic horizon tracking
A key challenge of 3-D seismic horizon tracking lies in effectively utilizing the appropriate
seismic attributes to enhance tracking precision. Numerous existing horizon tracking …
seismic attributes to enhance tracking precision. Numerous existing horizon tracking …
Automatic horizon picking using multiple seismic attributes and Markov decision process
C Wu, B Feng, X Song, H Wang, R Xu, S Sheng - Remote Sensing, 2023 - mdpi.com
Picking the reflection horizon is an important step in velocity inversion and seismic
interpretation. Manual picking is time-consuming and no longer suitable for current large …
interpretation. Manual picking is time-consuming and no longer suitable for current large …