E. Ozturk, M. Prabhushankar, and G. AlRegib
In addressing the inherent uncertainties in seismic data interpretation, our study introduces an approach for seismic facies segmentation by applying multi-view test-time augmentation (TTA) within a deep learning framework. Departing from traditional our method integrates dynamic uncertainty thresholding with an encoder-decoder convolutional neural network (CNN) to refine predictions across multiple seismic views, inline, crossline, and depth, thereby substantially enhancing segmentation accuracy. This research demonstrates the superiority of multi-view analysis over conventional methods, offering a robust solution to the complexities of facies segmentation.
