MSSPN: Automatic first-arrival picking using a multistage segmentation picking network

• DOI: 10.1190/geo2023-0110.1
• Publication Date: 2024-02-02T12:04:36Z
• Authors: Hongtao Wang; Jiangshe Zhang; Xiaoli Wei; Chunxia Zhang; Li Long; Zhenbo Guo
• Keywords: FOS: Computer and information sciences; Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV); Computer Science - Computer Vision and Pattern Recognition; FOS: Electrical engineering, electronic engineering, information engineering; Electrical Engineering and Systems Science - Image and Video Processing; 01 natural sciences; 0105 earth and related environmental sciences

Picking the first arrival of prestack gathers is an indispensable step in seismic data processing. To enhance the efficiency of seismic data processing, some deep-learning-based methods for first-arrival picking have been developed. However, when applying currently trained models to data that significantly differ from the training set, the results are often suboptimal. We refer to this predictive scenario as cross-survey picking. Therefore, further improving model generalization for accurate cross-survey picking has become an urgent problem. To overcome the problem, we develop a multistage picking method called multistage segmentation picking network (MSSPN), which breaks down the complex picking task into four stages. In the first stage, we develop a coarse segmentation network to recognize a rough trend of first arrivals. Second, a robust trend estimation method is developed in the second stage to further obtain a tighter range of first arrivals. Third, a refined segmentation network is conducted in the third stage to pick high-precision first arrivals. Finally, we develop a velocity constraint-based postprocessing strategy to remove the outliers of network pickings. Extensive experiments indicate that MSSPN outperforms current state-of-the-art methods under the cross-survey test situation in terms of the metrics of accuracy and stability. Particularly, MSSPN achieves 94.64% and 89.74% accuracy under the cross-survey field cases of the median and low signal-to-noise ratio data, respectively.