A precise picking method for seismic first arrivals based on the residual long short-term memory network driven by time-frequency dual domain data

Abstract

First-arrival picking of seismic data is one of the key steps in seismic data processing. When seismic data have low signal-to-noise ratio (SNR) and weak first-arrival energy, accurately and efficiently picking first arrivals remain a critical challenge for most automatic picking methods. To address this issue, this paper proposes a Multi-perspective Residual Long Short-Term Memory (M-Res-LSTM) network. This network integrates the spatial feature extraction advantage of Residual Networks and the temporal dynamic modeling capability of LSTM networks, while introducing a coordinate attention mechanism. Through multi-perspective learning in both time and frequency domains, it effectively improves the reliability of automatic first-arrival picking. First, this paper elaborates on the core principle of the M-Res-LSTM network for automatic first-arrival picking: the amplitude, frequency, and phase features of seismic data are used as network inputs, and the accurately picked first arrivals manually serve as network outputs. After training the network using a supervised learning approach, the well-trained model is applied to perform automatic first-arrival picking. Second, an analysis of the network’s hyperparameters is conducted to determine the optimal parameter configuration. Finally, automatic first-arrival picking tests are carried out on seismic datasets with different characteristics, and the picking results are compared with those obtained by the energy ratio method, single-input Res-LSTM, and Swin-Transformer. The results demonstrate that the proposed M-Res-LSTM method maintains good stability and accuracy even in complex scenarios with low first-arrival energy and poor SNR.

Keywords

Automatic first-arrival picking / Time-frequency dual domain / Multi-perspective learning / Res-LSTM / Attention mechanism

Funding

This work was supported in part by Sichuan Science and Technology Program (2024NSFSC0808) and in part by the Talent Project of Chengdu Technological University (2024RC028).

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