The interpolation of sparse geophysical data
Geophysical data interpolation has attracted much attention in the past decades. While a
variety of methods are well established for either regularly sampled or irregularly sampled …
variety of methods are well established for either regularly sampled or irregularly sampled …
[КНИГА][B] Seismic inversion: Theory and applications
Y Wang - 2016 - books.google.com
Seismic inversion aims to reconstruct a quantitative model of the Earth subsurface, by
solving an inverse problem based on seismic measurements. There are at least three …
solving an inverse problem based on seismic measurements. There are at least three …
Meta-Processing: A robust framework for multi-tasks seismic processing
Abstract Machine learning-based seismic processing models are typically trained separately
to perform seismic processing tasks (SPTs) and, as a result, require plenty of high-quality …
to perform seismic processing tasks (SPTs) and, as a result, require plenty of high-quality …
Self-supervised deep learning to reconstruct seismic data with consecutively missing traces
H Huang, T Wang, J Cheng, Y ** between regularly …
Structure-oriented singular value decomposition for random noise attenuation of seismic data
Singular value decomposition (SVD) can be used both globally and locally to remove
random noise in order to improve the signal-to-noise ratio (SNR) of seismic data. However, it …
random noise in order to improve the signal-to-noise ratio (SNR) of seismic data. However, it …