Seismic horizon tracking based on TransUnet model

Abstract

Seismic horizons are of paramount importance for building structure models and stratigraphic interpretations. However, currently, horizon tracking is generally obtained through manual tracking or a combination of traditional automatic tracking and manual tracking, which is a highly time-consuming and error-prone process. Despite the many methods proposed for automated horizon tracking, tracking horizons with complex features still face numerous challenges.This paper proposes a method for horizon tracking using the TransUnet model, which can be applied to track both individual horizons and multiple horizons simultaneously. This method is implemented through deep learning semantic segmentation. Training is conducted using a deep learning encoder-decoder structure to establish the mapping relationship between seismic data and horizon data. The TransUnet model builds upon the Unet model by incorporating Transformer modules, enabling the model to possess both local and global attention capabilities. The ultimate results of horizon tracking reveal that the TransUnet model is more proficient at tracking horizons in areas with complex seismic reflections compared to the Unet model. In single and multiple horizon tracking, the root mean square error (RMSE) and coefficient of determination (R2 ) calculated from horizons predicted by the TransUNet model and manually tracked horizons are significantly lower than those of the U-Net model, this research demonstrates that the proposed method is more efficient and accurate in tracking the horizons of three-dimensional seismic volumes compared to traditional approaches.