Double-scale supervised inversion with a data-driven forward model for low-frequency impedance recovery
S Yuan, X Jiao, Y Luo, W Sang, S Wang - Geophysics, 2022 - library.seg.org
Low-frequency information is important in reducing the nonuniqueness of absolute
impedance inversion and for quantitative seismic interpretation. In traditional model-driven …
impedance inversion and for quantitative seismic interpretation. In traditional model-driven …
Machine learning for seismic exploration: Where are we and how far are we from the holy grail?
Machine-learning (ML) applications in seismic exploration are growing faster than
applications in other industry fields, mainly due to the large amount of acquired data for the …
applications in other industry fields, mainly due to the large amount of acquired data for the …
Deep physics-aware stochastic seismic inversion
PY Bürkle, L Azevedo, M Vellasco - Geophysics, 2023 - library.seg.org
Seismic inversion allows the prediction of subsurface properties from seismic reflection data
and is a key step in reservoir modeling and characterization. With the generalization of …
and is a key step in reservoir modeling and characterization. With the generalization of …
Porosity prediction based on a structural modeling deep-learning method
B Tao, X Liu, H Zhou, J Liu, F Lyu, Y Lin - Geophysics, 2024 - pubs.geoscienceworld.org
The seismic inversion method is a pivotal approach for acquiring porosity parameters. The
performance of inversion depends on several factors, such as algorithm stability, the …
performance of inversion depends on several factors, such as algorithm stability, the …
A physics‐guided neural network‐based approach to velocity model calibration for microseismic data
The physics‐guided neural network framework combines the effectiveness of data‐driven
and physics‐based models, and it is, therefore, becoming increasingly popular in …
and physics‐based models, and it is, therefore, becoming increasingly popular in …
Physics-driven cycle network for seismic impedance inversion using conditional generative adversarial networks
Despite the extensive application of artificial neural networks in seismic inversion, their
effectiveness is often hampered by the limited availability of labeled data. To address this …
effectiveness is often hampered by the limited availability of labeled data. To address this …
Extremely weak supervision inversion of multiphysical properties
Multi-physical inversion plays a critical role in geophysics. It has been widely used to infer
various physical properties (such as velocity and conductivity). Among those inversion …
various physical properties (such as velocity and conductivity). Among those inversion …