A single graph mixture density network trained on random 2D velocity models and station layouts can produce posterior velocity maps for synthetic and real seismic datasets of different sizes and scales.
Two crustal low-velocity channels beneath se tibet revealed by joint inversion of rayleigh wave dispersion and receiver functions
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Solving All Seismic Tomographic Problems using Deep Learning
A single graph mixture density network trained on random 2D velocity models and station layouts can produce posterior velocity maps for synthetic and real seismic datasets of different sizes and scales.