Quality control in deep learning and confidence quantification: Seismic velocity regression through classification
J Simon, G Fabien-Ouellet, E Gloaguen - Computers & Geosciences, 2024 - Elsevier
Deep learning methods are increasingly used in seismic, but the black-box nature of neural
networks hinders the confidence users may have in their outputs. Moreover, conventional …
networks hinders the confidence users may have in their outputs. Moreover, conventional …
First-Arrival Picking for Out-of-Distribution Noisy Data: A Cost-Effective Transfer Learning Method with Tens of Samples
Data-driven methods for picking the first-arrival of seismic waves can encounter challenges
with generalization when they are faced with out-of-distribution data that falls outside their …
with generalization when they are faced with out-of-distribution data that falls outside their …
Automatic velocity analysis using interpretable multimode neural networks
H Zhang, S Yuan, H Zeng, H Yuan… - Geophysical Journal …, 2023 - academic.oup.com
Seismic velocity analysis is the basis for seismic imaging and understanding complex
subsurface geological structures. Although the performance of automatic velocity analysis …
subsurface geological structures. Although the performance of automatic velocity analysis …
Transfer learning-based hybrid deep learning method for gas-bearing distribution prediction with insufficient training samples and uncertainty analysis
J Yang, N Lin, K Zhang, C Fu, C Zhang - Energy, 2024 - Elsevier
The availability of sample data, required for seismicity-based reservoir prediction using deep
learning (DL), is often limited by well availability. To alleviate this problem, this study …
learning (DL), is often limited by well availability. To alleviate this problem, this study …
A fine‐tuning workflow for automatic first‐break picking with deep learning
First‐break picking is an essential step in seismic data processing. For reliable results, first
arrivals should be picked by an expert. This is a time‐consuming procedure and subjective …
arrivals should be picked by an expert. This is a time‐consuming procedure and subjective …
Vector Decomposition of Elastic Seismic Wavefields Using Self-Attention Deep Convolutional Generative Adversarial Networks
Vector decomposition of P-and S-wave modes from elastic seismic wavefields is a key step
in elastic reverse-time migration (ERTM) to effectively improve the multi-wave imaging …
in elastic reverse-time migration (ERTM) to effectively improve the multi-wave imaging …
[HTML][HTML] Deep-learning velocity model building by jointly using seismic first arrivals and early-arrival waveforms
Seismic first arrivals and early-arrival waveform data contain rich velocity structure
information and are widely used in velocity model building. Such methods based on first …
information and are widely used in velocity model building. Such methods based on first …
InverMulT-STP: Closed-loop Transformer Seismic AVA Inversion with Synthetic Data Style Transfer Pretraining
Prestack seismic data amplitude variation with angle (AVA) inversion is critical in identifying
oil and gas reservoirs. Recently, deep learning (DL) has gained significant popularity in AVA …
oil and gas reservoirs. Recently, deep learning (DL) has gained significant popularity in AVA …
Transfer learning model for estimating site amplification factors from limited microtremor H/V spectral ratios
Site amplification factors (SAFs) of seismic ground motions are essential in evaluating and
estimating seismic hazards. In our previous study, the authors proposed a simple and cost …
estimating seismic hazards. In our previous study, the authors proposed a simple and cost …
Deep-learning viscoelastic seismic inversion for map** subsea permafrost
Marine seismic surveys can be used to map ice-bearing subsea permafrost on a large scale.
However, current seismic processing technologies have limited capacity to image …
However, current seismic processing technologies have limited capacity to image …