UPNet: Uncertainty-based Picking Deep Learning Network for Robust First Break Picking
This addresses the need for more reliable automatic picking methods in seismic exploration, particularly for low signal-to-noise ratio datasets, though it is incremental as it builds on existing segmentation-based DNN approaches.
The paper tackles the problem of robust first break picking in seismic exploration by introducing uncertainty quantification into deep learning networks, resulting in higher accuracy and robustness than deterministic models, achieving state-of-the-art performance in field surveys.
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 automatic picking methods applied in field datasets can not ensure robustness, especially in the case of a low signal-to-noise ratio (SNR). In this paper, we introduce uncertainty quantification into the FB picking task and propose a novel uncertainty-based picking deep learning network called UPNet. UPNet not only estimates the uncertainty of network output but also can filter the pickings with low confidence. Many experiments evaluate that UPNet exhibits higher accuracy and robustness than the deterministic DNN-based model, achieving State-of-the-Art (SOTA) performance in field surveys. In addition, we verify that the measurement uncertainty is meaningful, which can provide a reference for human decision-making.