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We consider the OpenFWI collection of datasets, comprising multi-structural benchmark datasets for DL4SI grouped into: Vel, Fault, and Style Families. We compare Latent U-Net and Invertible X-Net on these datasets against several baseline methods for both forward and inverse problems.
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For quantitative comparisons, we used Mean Absolute Error (MAE), Mean Square Error (MSE), and Structured Similarity (SSIM) as evaluation metrics since neither metric
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alone is fully comprehensive. MAE captures pixel-level accuracy while SSIM highlights structural similarity.
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<li><p><b>Quantitative Comparison</b></p>
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<p><b> Quantitative Comparison: </b> We used Mean Absolute Error (MAE), Mean Square Error (MSE), and Structured Similarity (SSIM) as evaluation metrics since neither metric
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alone is fully comprehensive. MAE captures pixel-level accuracy while SSIM highlights structural similarity. </p>
Figure 4: Comparison of Latent U-Nets (Small and Large), Invertible X-Net, Invertible X-Net (Cycle) with different baseline methods across different OpenFWI datasets.
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</figcaption>
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</figcaption>
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<p><b> Qualitative Comparison: </b> We show model prediction on three datasets CVB, CFB, and STA, choosing one from each family of OpenFWI. </p>
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