MSSPN: Automatic First Arrival Picking using Multi-Stage Segmentation Picking Network
arxiv(2022)
摘要
Picking the first arrival times of prestack gathers is called First Arrival
Time (FAT) picking, which is an indispensable step in seismic data processing,
and is mainly solved manually in the past. With the current increasing density
of seismic data collection, the efficiency of manual picking has been unable to
meet the actual needs. Therefore, automatic picking methods have been greatly
developed in recent decades, especially those based on deep learning. However,
few of the current supervised deep learning-based method can avoid the
dependence on labeled samples. Besides, since the gather data is a set of
signals which are greatly different from the natural images, it is difficult
for the current method to solve the FAT picking problem in case of a low Signal
to Noise Ratio (SNR). In this paper, for hard rock seismic gather data, we
propose a Multi-Stage Segmentation Pickup Network (MSSPN), which solves the
generalization problem across worksites and the picking problem in the case of
low SNR. In MSSPN, there are four sub-models to simulate the manually picking
processing, which is assumed to four stages from coarse to fine. Experiments on
seven field datasets with different qualities show that our MSSPN outperforms
benchmarks by a large margin.Particularly, our method can achieve more than
90% accurate picking across worksites in the case of medium and high SNRs, and
even fine-tuned model can achieve 88% accurate picking of the dataset with low
SNR.
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