  We proposed a robust segmentation and picking workflow to solve the first
arrival picking problem for seismic signal processing. Unlike traditional
classification algorithm, image segmentation method can utilize the location
information by outputting a prediction map which has the same size of the input
image. A parameter-free nearest point picking algorithm is proposed to further
improve the accuracy of the first arrival picking. The algorithm is test on
synthetic clean data, synthetic noisy data, synthetic picking-disconnected data
and field data. It performs well on all of them and the picking deviation
reaches as low as 4.8ms per receiver. The first arrival picking problem is
formulated as the contour detection problem. Similar to \cite{wu2019semi}, we
use U-net to perform the segmentation as it is proven to be state-of-the-art in
many image segmentation tasks. Particularly, a Lovasz loss instead of the
traditional cross-entropy loss is used to train the network for a better
segmentation performance. Lovasz loss is a surrogate loss for Jaccard index or
the so-called intersection-over-union (IoU) score, which is often one of the
most used metrics for segmentation tasks. In the picking part, we use a novel
nearest point picking (NPP) method to take the advantage of the coherence of
the first arrival picking among adjacent receivers. Our model is tested and
validated on both synthetic and field data with harmonic noises. The main
contributions of this paper are as follows: 1. Used Lovasz loss to directly
optimize the IoU for segmentation task. Improvement over the cross-entropy loss
with regard to the segmentation accuracy is verified by the test result. 2.
Proposed a nearest point picking post processing method to overcome any defects
left by the segmentation output. 3. Conducted noise analysis and verified the
model with both noisy synthetic and field datasets.
