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projects/GFI-framework/index.html

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@@ -100,8 +100,7 @@ <h2 class="subtitle is-4 has-text-weight-bold">ICLR 2025</h2>
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<!-- Abstract -->
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<h2>
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Abstract</h2>
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<h2 class="title is-3"> Abstract </h2>
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<p>
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In subsurface imaging, learning the mapping from velocity maps to seismic waveforms (forward problem) and waveforms to velocity (inverse problem) is important for several applications. While traditional techniques for solving forward and inverse problems are computationally prohibitive, there is a growing interest in leveraging recent advances in deep learning to learn the mapping between velocity maps and seismic waveform images directly from data.
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Despite the variety of architectures explored in previous works, several open questions remain unanswered such as the effect of latent space sizes, the importance of manifold learning, the complexity of translation models, and the value of jointly solving forward and inverse problems.
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<!-- Architecture Diagram -->
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<h2 class="title is-3">Method Overview</h2>
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<img src="./static/images/architecture.png" alt="Model Architecture" width="90%">
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<h2 class="title is-3">Quantitative Results</h2>
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<p class="has-text-centered">Table comparing PSNR / MSE / FID between baselines and our method.</p>
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<img src="./static/images/results-table.png" alt="Results Table" width="90%">
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<h2 class="title is-3">Experiments</h2>
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</div>
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