Seismic facies classification remains a critical yet challenging task in subsurface characterization due to the complexity of depositional systems and limitations of manual interpretation. This study presents a supervised deep learning workflow for seismic facies prediction using a dip-steered median filtered (DSMF) seismic volume. Ten facies label sets, representing minima and maxima responses, were derived from unsupervised vector quantization (UVQ) clustering and subsequently refined using Thalweg tracking, with 1,500 samples per class. These labels, together with the DSMF volume, were used to extract 3D cubelets (41×41×3) for training convolutional neural networks (CNNs). Three archite... More >