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 …
Low-Rank Approximation Reconstruction of Five-Dimensional Seismic Data
Low-rank approximation has emerged as a promising technique for recovering five-
dimensional (5D) seismic data, yet the quest for higher accuracy and stronger rank …
dimensional (5D) seismic data, yet the quest for higher accuracy and stronger rank …
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 …
Unsupervised deep-learning framework for 5D seismic denoising and interpolation
We develop an unsupervised framework to reconstruct missing data from noisy and
incomplete five-dimensional (5D) seismic data. Our method comprises two main …
incomplete five-dimensional (5D) seismic data. Our method comprises two main …
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 …
FUDLInter: Frequency–Space-Dependent Unsupervised Deep Learning Framework for 3D and 5D Seismic Data Interpolation
Deep learning (DL) has emerged as a focal point in addressing various challenges within
the field of exploration seismology, prominently featuring applications in seismic data …
the field of exploration seismology, prominently featuring applications in seismic data …
Simultaneous seismic data de‐aliasing and denoising with a fast adaptive method based on hybrid wavelet transform
P Zhang, X Han, C Chen, X Liu - Geophysical Prospecting, 2024 - Wiley Online Library
Missing data and random noise are prevalent issues encountered during the processing of
acquired seismic data. Interpolation and denoising represent economical solutions to …
acquired seismic data. Interpolation and denoising represent economical solutions to …
Unsupervised frequency space domain deep learning framework for reconstructing 5D seismic data
Five-dimensional (5D) seismic data reconstruction is a crucial step to improve seismic
imaging. We introduce a deep complex-valued neural network for constructing an …
imaging. We introduce a deep complex-valued neural network for constructing an …
Improve automatic migrated gather processing with feature engineering and 4D convolutional neural networks
W Pan, H Rynja, R Dandu, Z Liu, S Ye… - … Exposition and Annual …, 2024 - onepetro.org
Post-processing of migrated common image gathers is an essential step before residual
moveout picking for pore pressure analysis and velocity modeling. Conventional processing …
moveout picking for pore pressure analysis and velocity modeling. Conventional processing …
Unsupervised deep learning for seismic data reconstruction
Seismic data suffer from under-sampling along spatial dimensions due to physical and
economic limitations during acquisition, which can adversely impact subsequent imaging …
economic limitations during acquisition, which can adversely impact subsequent imaging …