Channel Characterization Based on 3-D TransUnet-CBAM With Multiloss Function

Abstract

The channel system is intimately linked to the formation of oil and gas reservoirs. In petroliferous basins, channel deposits frequently serve as both storage spaces and fluid conduits. Consequently, the accurate identification of channels in 3-D seismic data is, therefore, critical for reservoir prediction. Traditional seismic attribute-based methods can outline channel boundaries, but noise and stratigraphic complexity introduce discontinuities that reduce accuracy and require extensive manual correction. Deep learning-based methods outperform conventional methods in terms of efficiency and precision. However, the similar seismic signatures of channels and continuous karst caves in seismic profiles can still mislead the existing models. To address this challenge, we proposed an improved variant of the 3-D TransUnet model for 3-D seismic data recognition. The model incorporates channel and spatial attention mechanisms into the skip connections of the TransUnet architecture, effectively enhancing its feature representation capability and recognition accuracy. In addition, a multiloss function is introduced to improve the delineation and continuity of the channel while increasing the model’s robustness against nonchannel interference features. Experiments on synthetic and field seismic data confirm superior boundary delineation, continuity, and noise resistance compared with baseline methods.