MSSPN: Automatic First Arrival Picking using Multi-Stage Segmentation Picking Network

TL;DR

This paper introduces MSSPN, a multi-stage segmentation network for automatic first arrival picking in seismic data, effectively handling low SNR and cross-site generalization, outperforming existing methods.

Contribution

Proposes MSSPN, a novel multi-stage segmentation network that improves automatic FAT picking accuracy and generalization across different seismic gather datasets, especially in low SNR conditions.

Findings

Achieves over 90% accuracy in medium/high SNR datasets across worksites.

Fine-tuned model reaches 88% accuracy on low SNR data.

Outperforms benchmark methods significantly.

Abstract

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.