<ul><li>A new latent diffusion model based on representation learning for seismic data interpolation and reconstruction has been proposed.</li><li>Traditional seismic data reconstruction methods struggle to handle large-scale continuous missing traces.</li><li>The proposed latent diffusion transformer utilizes representation learning to address complex and irregular missing situations in seismic data.</li><li>Reconstruction experiments on field and synthetic datasets show that the method achieves higher accuracy and can handle various complex missing scenarios.</li></ul>

SeisRDT: Latent Diffusion Model Based On Representation Learning For Seismic Data Interpolation And Reconstruction

Discover more