Auto-Picking of First-Break Refractions in Arctic Ocean Seismic Data Using Stacked BiLSTM Networks

 In seismic exploration data from the Arctic Ocean, refractions are recorded earlier than direct waves due to the shallow depths and subsea permafrost with high velocity. These refraction signals could be utilized for estimating the velocity, thickness, and depth of the subsea permafrost. However, it is very challenging work to pick the accurate first arrivals of seismic data in the Arctic Ocean because of many factors such as ambient noise and etc. Therefore, identifying first-break refractions is crucial and can be performed by manual or automated picking methods. Various semi-automatic techniques have been developed to identify first-break refractions, but these methods are often sensitive to pulse variations and require parameter tuning. Recently, deep learning-based methods have also been explored, but their reliance on training data often results in inconsistent performance, making it essential to generate training data optimized for the target environment. 

This study presents a recurrent neural network-based algorithm optimized for Arctic Ocean environments to automatically identify first-break refractions. To effectively classify first-break refractions, a stacked bidirectional long short-term memory (BiLSTM) network was constructed to iteratively learn bidirectional long-term dependencies by utilizing the temporal patterns of time-series data. Additionally, the training data were generated by creating velocity models that reflect the subsurface properties of subsea permafrost, enabling the generation of first-break refraction label data. The proposed network demonstrated superior performance in identifying first-break refractions from noisy data, achieving over 95% accuracy in numerical experiments and field tests. Field data applications demonstrated that the proposed network achieves high accuracy in classifying first-break refractions, validating its robustness and adaptability.

 

 

Acknowledgments

This research was supported by Korea Institute of Marine Science & Technology Promotion (KIMST) funded by the Ministry of Oceans and Fisheries, Korea (RS-2023-00259633).