3D karst cave recognition using TransUnet with dual attention mechanisms in seismic images

ABSTRACT As crucial storage spaces for underground hydrocarbon resources, karst caves are essential for the exploitation and development of carbonate-prone reservoirs. The precise characterization of karst caves enables efficient resource management and enhances the effectiveness of oil and gas exploration and development. Due to noise interference and discontinuities in geologic strata, traditional methods that rely on seismic properties to detect karst caves are highly subjective, prone to multiple solutions, and require extensive manual intervention, resulting in low development efficiency. With the growing demand for exploration, overcoming the limitations of traditional methods through more efficient and precise karst cave identification technologies has emerged as a critical focus in carbonate-prone reservoir research. To address these limitations in 3D karst cave identification, we introduce a variant architecture of TransUnet, which integrates transformer and dual attention mechanisms with a convolutional neural network while upgrading the convolutional module from two dimensions to three dimensions to describe better the location and boundary characteristics of the karst caves in field seismic data. Compared with traditional U-shaped architectures, the model offers a faster convergence rate (epochs <10) during training and fewer false predictions during validation with open-source synthetic data. Further applications of physical models and field seismic data provide more accurate details on the positions and boundaries of karst caves, demonstrating their significant potential to improve the accuracy and resolution of karst cave detection and effectively enhance the characterization of carbonate reservoirs.