AccScience Publishing / JSE / Online First / DOI: 10.36922/JSE025330058
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A precise picking method for seismic first arrivals based on the residual long short-term memory network driven by time-frequency dual domain data

Ziyu Qin1 ,  Xianju Zheng1* ,  Wenhua Wang2
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1 Department of Software Engineering, School of Computer Engineering, Chengdu Technological University, Chengdu, Sichuan, China
2 Department of Intelligence Science and Technology, School of Computer Science, Chengdu Normal University, Chengdu, Sichuan, China
JSE, 025330058 https://doi.org/10.36922/JSE025330058
Submitted: 16 August 2025 | Revised: 8 September 2025 | Accepted: 29 October 2025 | Published: 18 November 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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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 the Natural Science Foundation of Sichuan Province (2024NSFSC0808) and in part by the Talent Project of Chengdu Technological University (2024RC028).
Conflict of interest
The authors declare that there are no (potential) conflicts of interest with any institutes, organizations, or agencies that might influence the integrity of the results or the objective interpretation of this submitted work. All research activities and manuscript preparation have been conducted in an objective and impartial manner, free from any factors that could compromise the validity and fairness of the study.
References
  1. Yuan S, Liu J, Wang S, Wang T, Shi P. Seismic waveform classification and first-break picking using convolution neural networks. IEEE Geosci Remote Sens Lett. 2018;15(2):272-276. doi: 10.1109/LGRS.2017.2785834

 

  1. Peraldi R, Clement A. Digital processing of refraction data study of first arrivals. Geophys Prospect. 1972;20(3):529-548. doi: 10.1111/j.1365-2478.1972.tb00653.x

 

  1. Molyneux JB, Schmitt DR. First-break timing: Arrival onset times by direct correlation. Geophysics. 1999;64(5): 1492-1501. doi: 10.1190/1.1444653

 

  1. Gelchinsky B, Shtivelman V. Automatic picking of first arrivals and parameterization of travel time curves. Geophys Prospect. 1983;31:915-928. doi: 10.1111/j.1365-2478.1983.tb01097.x

 

  1. Coppens F. First arrival picking on common-offset trace collections for automatic estimation of static corrections. Geophys Prospect. 1985;33(8):1212-1231. doi: 10.1111/j.1365-2478.1985.tb01360.x

 

  1. Qin Z, Pan S, Hu L, Cui Q, Gou Q. First-arrival automatic picking based on improved energy ratio method and outlier detection theory. Acta Geophysica. 2021;69(5):1667-1677. doi: 10.1007/s11600-021-00639-w

 

  1. Allen RV. Automatic earthquake recognition and timing from single traces. Bull Seismol Soc Am. 1978;68(5): 1521-1532. doi: 10.1785/bssa0680051521

 

  1. Wong J, Han L, Bancroft J, Stewart RR. Automatic Time-Picking of First-Arrivals on Noisy Microseismic Data. Alberta: CSEG Microseismic Workshop; 2009. p. 1-6.

 

  1. Akram J, Eaton DW. A review and appraisal of arrival-time picking methods for downhole microseismic data. Geophysics. 2016;81(2):KS71-KS91. doi: 10.1190/geo2014-0500.1

 

  1. Pan S, Qin Z, Lan H, Badal J. Automatic first-arrival picking method based on an image connectivity algorithm and multiple time windows. Comput Geosci. 2019;123:95-102. doi: 10.1016/j.cageo.2018.12.001

 

  1. Leonard M. Comparison of manual and automatic onset time picking. Bull Seismol Soc Am. 2000;90(6):1384-1390. doi: 10.1785/0120000026

 

  1. Zhang H, Thurber C, Rowe C. Automatic p-wave arrival detection and picking with multiscale wavelet analysis for single component recordings. Bull Seismol Soc Am. 2003;93(5):1904-1912. doi: 10.1785/0120020241

 

  1. Jin H, Li L, Cheng S, Li X. Time-arrival pickup method of tunnel water inrush microseismic signals based on kurtosis value and AIC method. Tunnell Undergr Space Technol. 2024;154:106135. doi: 10.1016/j.tust.2024.106135

 

  1. Boschetti F, Dentith MD, List RD. A fractal-based algorithm for detecting first arrivals on seismic traces. Geophysics. 1996;61(4):1095-1102. doi: 10.1190/1.1444030

 

  1. Chi-Duráur-Dur/1.14440Dur- M, Silva J. Automatic detection of p- and s-wave arrival times: New strategies based on the modified fractal method and basic matching pursuit. J Seismol. 2017;21(5):1171-1184. doi: 10.1007/s10950-017-9658-0

 

  1. Jiao LX, Moon WM. Detection of seismic refraction signals using a variance fractal dimension technique. Geophysics. 2000;65(1):286-292. doi: 10.1190/1.1444719

 

  1. Esmaeili S. A new automatic first break picking method based on the STA/LTA fractal dimension algorithm. J Seismic Explor. 2022;31(3):253-265.

 

  1. Lan Z, Gao P, Wang P, Wang Y, Liang J, Hu G. Automatic first arrival time identification using fuzzy C-Means and AIC. IEEE Trans Geosci Remote Sens. 2022;60:5907613. doi: 10.1109/TGRS.2021.3121032

 

  1. Zhang Y, Lan Z, Xue Y, Wang J, Zhu K, He J. Automatic first arrival picking based on self-similarity and multicenter fuzzy clustering. IEEE Trans Geosci Remote Sens. 2025;63:5906013. doi: 10.1109/TGRS.2025.3538757

 

  1. Mousa WA, Al-Shuhail AA. Enhancement of first arrivals using the ivaltransform on energy-ratio seismic shot records. Geophysics. 2012;77(3):V101-V111. doi: 10.1190/geo2010-0331.1

 

  1. Gaci S. The use of wavelet-based denoising techniques to enhance the first-arrival picking on seismic traces. IEEE Trans Geosci Remote Sens. 2014;52(8):4558-4563. doi: 10.1109/tgrs.2013.2282422

 

  1. Cheng Y, Li Y, Zhang C. First arrival time picking for microseismic data based on shearlet transform. J Geophys Eng. 2017;14(2):262-271. doi: 10.1088/1742-2140/aa5777

 

  1. Sheng GQ, Tang XG, Xie K, Xiong J. Hydraulic fracturing microseismic first arrival picking method based on non-subsampled shearlet transform and higher-order-statistics. J Seismic Explor. 2019;28(6):593-618.

 

  1. Dong XT, Jiang H, Li Y, Yang B. Arrival time picking of micro-seismic data by using SPE algorithm. J Seismic Explor. 2019;28(5):475-494.

 

  1. Qin Z, Pan S, Chen J, Cui Q, He J. Method of automatically detecting the abnormal first arrivals using delay time. IEEE Trans Geosci Remote Sens. 2022;60:4504908. doi: 10.1109/tgrs.2021.3118921

 

  1. Murat ME, Rudman AJ. Automated first arrival picking: A neural network approach. Geophys Prospect. 1992;40: 587-604. doi: 10.1111/j.1365-2478.1992.tb00543.x

 

  1. McCormack MD, Zaucha DE, Dushek DW. First-break refraction event picking and seismic data trace editing using neural networks. Geophysics. 1993;58(1):67-78. doi: 10.1190/1.1443352

 

  1. Maity D, Aminzadeh F, Karrenbach M. Novel hybrid artificial neural network based autopicking workflow for passive seismic data. Geophys Prospect. 2014;62:834-847. doi: 10.1111/1365-2478.12125.

 

  1. Maity D, Salehi I. Neuro-evolutionary event detection technique for downhole microseismic surveys. Comput Geosci. 2016;86:23-33. doi: 10.1016/j.cageo.2015.09.024

 

  1. Mousavi SM, Horton SP, Langston CA, Samei B. Seismic features and automatic discrimination of deep and shallow induced-microearthquakes using neural network and logistic regression. Geophys J Int. 2016;207:29-46. doi: 10.1093/gji/ggw258

 

  1. Wu Y, Pan S, Lan H, Badal J, Wei Z, Chen Y. Automatic seismic first-break picking based on multi-view feature fusion network. Geophys Prospect. 2024;72(9):3547-3559. doi: 10.1111/1365-2478.13592

 

  1. Tsai KC, Hu W, Wu X, Chen J, Han Z. Automatic first arrival picking via deep learning with human interactive learning. IEEE Trans Geosci Remote Sens. 2020;58(2):1380-1391. doi: 10.1109/tgrs.2019.2946118

 

  1. Wang J, Xiao Z, Liu C, Zhao D, Yao Z. Deep learning for picking seismic arrival times. J Geophys Res Solid Earth. 2019;124(7):6612-6624. doi: 10.1029/2019jb017536

 

  1. Zhu W, Beroza GC. Phasenet: A deep-neural-network-based seismic arrival time picking method. Geophys J Int. 2019;216(3):1831-1841. doi: 10.1093/gji/ggy423

 

  1. Wu H, Zhang B, Li F, Liu N. Semiautomatic first-arrival picking of microseismic events by using the pixel-wise convolutional image segmentation method. Geophysics. 2019;84(3):V143-V155. doi: 10.1190/geo2018-0389.1

 

  1. Mardan A, Blouin M, Fabien-Ouellet G, Giroux B, Vergniault C, Gendreau J. A fine-tuning workflow for automatic first-break picking with deep learning. Near Surface Geophys. 2024;22:539-552. doi: 10.1002/nsg.12316

 

  1. Zhang Z, Yang J. Seismic first break picking based on multi-task learning. Front Earth Sci. 2025;13:1601134. doi: 10.3389/feart.2025.160113433

 

  1. Li H, Sun Y, Li J, Li H, Dong H. A meta-learning based approach for automatic first-arrival picking. IEEE Trans Geosci Remote Sens. 2024;62:5922615. doi: 10.1109/tgrs.2024.3436817

 

  1. Yuan P, Hu W, Wu X, Nguyen H. Adaptive first arrival picking model with meta-learning. In: SEG International Exposition and 90th Annual Meeting. United States: IEEE; 2020. p. 1486-1490. doi: 10.1190/segam2020-3420195.1

 

  1. Jiang P, Deng F, Wang X, Shuai P, Luo W, Tang Y. Seismic first break picking through swin transformer feature extraction. IEEE Geosci Remote Sens Lett. 2023;20:7501505. doi: 10.1109/LGRS.2023.3248233

 

  1. Chen K, Li M, Li X, et al. Enhancing microseismic event detection with TransUNet: A deep learning approach for simultaneous pickings of P-wave and S-wave first arrivals. Artif Intell Geosci. 2025;6:100129. doi: 10.1016/j.aiig.2025.100129

 

  1. Jiang P, Deng F, Wang X, Luo W, Ye C. 3-D seismic first break picking based on two-channel mask strategy. IEEE Trans Geosci Remote Sens. 2024;62:5918115. doi: 10.1109/TGRS.2024.3412673

 

  1. Liao X, Cao J, Hu J, You J, Jiang X, Liu Z. First arrival time identification using transfer learning with continuous wavelet transform feature images. IEEE Geosci Remote Sens Lett. 2020;17(11):2002-2006. doi: 10.1109/lgrs.2019.2955950

 

  1. Zhou Y, Yue H, Kong Q, Zhou S. Hybrid event detection and phase-picking algorithm using convolutional and recurrent neural networks. Seismol Res Lett. 2019;90(3):1079-1087. doi: 10.1785/0220180319

 

  1. Wang H, Zhang J, Wei X, Zhang C, Long L, Guo Z. MSSPN: Automatic first-arrival picking using a multistage segmentation picking network. Geophysics. 2024;89(3): U53-U70. doi: 10.1190/geo2023-0110.1

 

  1. He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016. p. 770-778. doi: 10.48550/ARXIV.1512.03385

 

  1. Hou Q, Zhou D, Feng J. Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. United States: IEEE; 2021. doi: 10.48550/arXiv.2103.02907

 

  1. Taner MT, Koehler F, Sheriff RE. Complex seismic trace analysis. Geophysics. 1979;44(6):1041-1063. doi: 10.1190/1.1440994

 

  1. Robertson JD, Nogami HH. Complex seismic trace analysis of thin beds. Geophysics. 1984;49(4):344-352. doi: 10.1190/1.1441670

 

  1. Kingma D, Ba J. Adam: A Method for Stochastic Optimization. In: Conference Paper at the 3rd International Conference for Learning Representations, San Diego, CA, USA; 2015. doi: 10.48550/ARXIV.1412.6980

 

  1. Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell. 2017;39(4):640-651. doi: 10.1109/tpami.2016.2572683

 

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Journal of Seismic Exploration, Print ISSN: 0963-0651, Published by AccScience Publishing
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