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- W4285221776 abstract "Summary Pre-stack three-parameter inversion commonly refers to estimating the three elastic parameters of longitudinal velocity VP, transverse velocity VS and density ρ from observed seismic angle gather. Conventional deep learning-based methods have significant lateral discontinuities and neglect the correlation between the three elasticity parameters. In this abstract, we investigate a deep neural network under the low-frequency information constraint for three-parameter simultaneous inversion, which realizes three related tasks based on the idea of multitask learning. Taking the typical model Marmousi2 as an example, we demonstrate that the network architecture, with the constraint of low-frequency information, predicts three elastic parameters with excellent lateral stability and high accuracy in the time direction." @default.
- W4285221776 created "2022-07-14" @default.
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- W4285221776 date "2022-01-01" @default.
- W4285221776 modified "2023-09-28" @default.
- W4285221776 title "Multitask Learning for Pre-Stack Three-Parameter Inversion" @default.
- W4285221776 doi "https://doi.org/10.3997/2214-4609.202210237" @default.
- W4285221776 hasPublicationYear "2022" @default.
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