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Numerical experiments demonstrate that the diffusion-regularized ASVGD-FWI produces more geologically consistent mean models, suppresses nonphysical artifacts, and yields uncertainty estimates that are more concentrated around structurally complex and poorly constrained regions.
The growth in seismic acquisition techniques has led to large-scale surveys, making traditional manual dispersion analysis logistically and computationally prohibitive. While deep learning techniques can offer an automated alternative, standard convolutional neural networks (CNNs) often fail to account for the non-uniq...
A deep‐learning‐based matching‐filtering framework is proposed, in which a lightweight neural network is trained to learn adaptive, data‐driven matching filters that map synthetic elastic wavefields towards the observed data.
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