UPNet: Uncertainty-based Picking Deep Learning Network for Robust First Break Picking

arXiv:2305.13799v2 Announce Type: replace
Abstract: In seismic exploration, first break (FB) picking is a crucial aspect in the determination of subsurface velocity models, significantly influencing the placement of wells. Many deep neural networks (DNNs)-based automatic picking methods have been proposed to accelerate this processing. Significantly, the segmentation-based DNN methods provide a segmentation map and then estimate FB from the map using a picking threshold. However, the uncertainty of the results picked by DNNs still needs to be analyzed. Thus, the …

abstract arxiv cs.cv deep learning dnn eess.iv exploration network networks neural networks placement processing robust segmentation seismic type uncertainty