Structure-Guided Deep Learning for Depth-Domain Seismic Impedance Inversion (opens in new tab)

Deep learning (DL) has demonstrated significant potential for direct seismic impedance inversion in the depth domain. However, conventional DL frameworks often adopt a trace-by-trace strategy, which fails to account for the spatial coherence of subsurface structures, leading to lateral discontinuities and a lack of geological guidance. To address these limitations, we propose a dip-constrained multitrace deep neural network (DMDNN) for depth-domain seismic inversion. This approach integrates ...