5D Seismic data interpolation by continuous representation
How to represent a seismic wavefield? Traditionally, while seismic wavefields are
conceptualized continuously, acquisition geometries capture seismic data discretely using 2 …
conceptualized continuously, acquisition geometries capture seismic data discretely using 2 …
Generative interpolation via a diffusion probabilistic model
Seismic data interpolation is essential in a seismic data processing workflow, recovering
data from sparse sampling. Traditional and deep-learning-based methods have been widely …
data from sparse sampling. Traditional and deep-learning-based methods have been widely …
NeRSI: Neural implicit representations for 5D seismic data interpolation
Due to challenging field operations and resource constraints, seismic data acquisition often
requires coping with missing traces. Interpolation algorithms are crucial for reconstructing …
requires coping with missing traces. Interpolation algorithms are crucial for reconstructing …
Sparse prior-net: A sparse prior-based deep network for seismic data interpolation
M Wu, L Fu, W Fang, J Cao - Geophysics, 2024 - library.seg.org
Seismic data interpolation plays a crucial role in obtaining dense and regularly sampled
data, contributing to improving the quality of seismic data in seismic exploration. Sparsity …
data, contributing to improving the quality of seismic data in seismic exploration. Sparsity …
3D9C seismic data reconstruction with multi-scale convolution neural network
H Tang, S Cheng, H Song, W Mao - Journal of Applied Geophysics, 2023 - Elsevier
The nine components (9C) seismic data acquired with three-component (3C) sources and
3C receivers is beneficial to the inversion of lithologic reservoirs with high resolution …
3C receivers is beneficial to the inversion of lithologic reservoirs with high resolution …
Self-supervised transfer learning POCS-Net for Seismic Data Interpolation
Deep learning has been widely applied to seismic data interpolation. However, most
existing methods are based on supervised learning, suffering from limitations such as low …
existing methods are based on supervised learning, suffering from limitations such as low …
Stochastic Solutions for Simultaneous Seismic Data Denoising and Reconstruction via Score-Based Generative Models
Usually, inverse problems are ill-posed. The solution to the inverse problem is
indeterminate, meaning that for given observational data, there may be multiple possible …
indeterminate, meaning that for given observational data, there may be multiple possible …
Trace-based interpolation using machine learning for irregularly missing seismic data
Recently, machine learning (ML) techniques have been actively applied for seismic trace
interpolation. However, because most research is based on training-inference strategies that …
interpolation. However, because most research is based on training-inference strategies that …
Seismic data interpolation with a recurrent inference mechanism
Seismic data interpolation is a significant procedure in seismic data processing as the highly
complete data are extremely useful for the subsequent imaging and interpretation workflows …
complete data are extremely useful for the subsequent imaging and interpretation workflows …
Deep learning-based off-the-grid seismic data reconstruction and regularization: Preliminary research
T Mo, J Song, Z Li, B Wang - Geophysics, 2025 - library.seg.org
During field seismic data acquisition, sources and receivers are commonly positioned off the
Cartesian grids (off-the-grid). Irregularities in the observed seismic data can negatively affect …
Cartesian grids (off-the-grid). Irregularities in the observed seismic data can negatively affect …