Seismic Attributes Aided Horizon Interpretation Using an Ensemble Dense Inception Transformer Network

Horizon picking is of paramount importance in seismic interpretation because it has a significant impact on subsequent interpretation and inversion. Although manual and various automatic interpretation methods have been widely used for horizon picking, they still have several problems, such as being time-consuming and highly dependent on human experience. Recently, deep-learning (DL) methods have been implemented to solve these problems. However, traditional convolutional neural networks (CNNs) have a shortage of capturing global features, and vision transformers, recently proposed, aim to address this. To segment the seismic horizon accurately, we suggest a dense inception transformer (DIFormer) by combining the dense extreme inception network (DexiNet) and the inception transformer network. When implementing model training with the patch technique, the DIFormer can retrieve more information and interpret horizons smoothly. Furthermore, we utilize multiple attributes computed from seismic data to train basic DIFormer models and then adopt ensemble learning to obtain the fusion model, that is the ensemble DIFormer (EDIFormer). We implement qualitative and quantitative analyses to verify the effectiveness of the suggested model and compare it with the state-of-the-art (SOTA) SegFormer and basic DIFormer trained with a single attribute in trace- and patch-based modes.