Statistics-guided dictionary learning for automatic coherent noise suppression
Coherent seismic noise is usually difficult to attenuate due to the similar morphological
patterns between noise and useful signals. To attenuate coherent noise, special …
patterns between noise and useful signals. To attenuate coherent noise, special …
Nonstationary seismic reflectivity inversion based on prior-engaged semisupervised deep learning method
Reflectivity inversion methods based on a stationary convolution model are essential for
seismic data processing. They compress the seismic wavelet, and by broadening the …
seismic data processing. They compress the seismic wavelet, and by broadening the …
A deep-learning-based generalized convolutional model for seismic data and its application in seismic deconvolution
The convolutional model, which describes the relation among poststack seismic data,
wavelet, and reflectivity, is the foundation of seismic deconvolution (SD). However, this …
wavelet, and reflectivity, is the foundation of seismic deconvolution (SD). However, this …
High‐resolution imaging of complex shallow fault zones along the July 2019 Ridgecrest ruptures
We perform ambient noise tomography using data recorded on 342 seismographs within a
50× 50 km area inside which the July 2019 M7. 1 and M6. 4 Ridgecrest earthquakes …
50× 50 km area inside which the July 2019 M7. 1 and M6. 4 Ridgecrest earthquakes …
Absorption attenuation compensation using an end-to-end deep neural network
C Zhou, S Wang, Z Wang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Absorption attenuation compensation is an important part of seismic data processing. It
enhances the resolution of nonstationary seismic data by compensating the amplitude …
enhances the resolution of nonstationary seismic data by compensating the amplitude …
Multitrace semiblind nonstationary deconvolution
We proposed a multitrace semiblind nonstationary deconvolution method. The proposed
method estimates reflectivity and source wavelet simultaneously for pursuing high-resolution …
method estimates reflectivity and source wavelet simultaneously for pursuing high-resolution …
A Multi-Task Learning Framework of Stable Q-Compensated Reverse Time Migration Based on Fractional Viscoacoustic Wave Equation
Z Xue, Y Ma, S Wang, H Hu, Q Li - Fractal and Fractional, 2023 - mdpi.com
Q-compensated reverse time migration (Q-RTM) is a crucial technique in seismic imaging.
However, stability is a prominent concern due to the exponential increase in high-frequency …
However, stability is a prominent concern due to the exponential increase in high-frequency …
Multichannel block sparse Bayesian learning reflectivity inversion with lp-norm criterion-based Q estimation
M Ma, S Wang, S Yuan, J Gao, S Li - Journal of Applied Geophysics, 2018 - Elsevier
The generation of attenuated seismic reflection data can be described via a nonstationary
convolution model with quality factor Q. In according with the linear matrix-matrix …
convolution model with quality factor Q. In according with the linear matrix-matrix …
Spatially constrained attenuation compensation in the mixed domain
X Ma, G Li, S He, H Li, Z Wang - Geophysical Prospecting, 2020 - earthdoc.org
Seismic attenuation compensation is a spectrum‐broadening technique for enhancing the
resolution of non‐stationary seismic data. The single‐trace attenuation compensation …
resolution of non‐stationary seismic data. The single‐trace attenuation compensation …
Q-factor estimation using bisection algorithm with power spectrum
D Yang, J Liu, J Li, D Liu - Geophysics, 2020 - pubs.geoscienceworld.org
The centroid frequency shift (CFS) method is a widely used Q estimation approach.
However, the CFS approach assumes that the amplitude spectrum of a source wavelet has a …
However, the CFS approach assumes that the amplitude spectrum of a source wavelet has a …