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

TL;DR

UPNet introduces uncertainty quantification into seismic first break picking, enhancing robustness and accuracy in noisy field data, and providing meaningful confidence measures for better decision-making.

Contribution

The paper presents UPNet, a novel deep learning model that estimates uncertainty in first break picking, improving robustness over existing deterministic methods.

Findings

UPNet achieves state-of-the-art accuracy in field surveys.

UPNet effectively filters low-confidence picks, enhancing robustness.

Measurement uncertainty from UPNet aids human decision-making.

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 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.