Skip to main content
SpringerLink
Log in

Travel time picking of ambient noise cross-correlation using a deep neural network combining convolutional neural networks and Transformer

  • Research Article - Applied Geophysics
  • Published:
Acta Geophysica Aims and scope Submit manuscript

Abstract

The travel time of ambient noise cross-correlation is widely used in geophysics, but traditional methods for picking the travel time of correlation are either difficult to be applied to data with low signal-to-noise ratio (SNR), or make some assumptions which fail to be achieved in many realistic situations, or require a lot of complex calculations. Here, we present a neural network based on convolutional neural networks (CNN) and Transformer for the travel time picking of ambient noise cross-correlation. CNNs expand the dimension of the vector of each time step for the input of Transformer. Transformer focuses the model’s attention on the key parts of the sequence. Model derives the travel time according to the attention. 102,000 cross-correlations are used to train the network. Compared with traditional methods, the approach is easy to use and has a better performance, especially for the low SNR data. Then, we test our model on another ambient noise cross-correlation dataset, which contains cross-correlations from different regions and at different scales. The model has good performance on the test dataset. It can be seen from the experiment that the travel time of the cross-correlation function of ambient noise with an average SNR as low as 9.3 can be picked. 97.2% of the picked travel times are accurate, and the positive and negative travel time of most cross-correlations are identical (90.2%). Our method can be applied to seismic instrument performance verification, seismic velocity imaging, source location and other applications for its good ability to pick travel time accurately.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Bensen GD, Ritzwoller MH, Barmin MP, Levshin AL, Lin F, Moschetti MP, Shapiro NM, Yang Y (2007) Processing seismic ambient noise data to obtain reliable broad-band surface wave dispersion measurements. Geophys J Int 169(3):1239–1260. https://doi.org/10.1111/j.1365-246X.2007.03374.x

    Article  ADS  Google Scholar 

  • Brenguier F, Shapiro NM, Campillo M, Ferrazzini V, Duputel Z, Coutant O, Nercessian A (2008) Towards forecasting volcanic eruptions using seismic noise. Nat Geosci 1(2):126–130

    Article  ADS  CAS  Google Scholar 

  • Chai C, Maceira M, Santos-Villalobos HJ, Venkatakrishnan SV et al (2020) Using a deep neural network and transfer learning to bridge scales for seismic phase picking. Geophys Res Lett 47:e2020GL088651

    Article  ADS  Google Scholar 

  • Chen KX, Gung Y, Kuo BY, Huang TY (2018) Crustal magmatism and deformation fabrics in northeast Japan revealed by ambient noise tomography. J Geophysi Res Solid Earth 123(10):8891–8906

    Article  ADS  Google Scholar 

  • Djebbi R, Alkhalifah T (2014) Traveltime sensitivity kernels for wave equation tomography using the unwrapped phase. Geophys J Int 197(2):975–986

    Article  ADS  Google Scholar 

  • Gouédard P, Seher T, McGuire JJ, Collins JA, van der Hilst RD (2014) Correction of ocean-bottom seismometer instrumental clock errors using ambient seismic noise. Bull Seismol Soc Am 104(3):1276–1288

    Article  Google Scholar 

  • Grêt A, Snieder R, Scales J (2006) Tim-lapse monitoring of rock properties with coda wave interferometry. J Geophys Res Solid Earth. https://doi.org/10.1029/2004JB003354

    Article  Google Scholar 

  • He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 770–778

  • Huang L, Li J, Hao H, Li X (2018) Micro-seismic event detection and location in underground mines by using convolutional neural networks (CNN) and deep learning. Tunn Undergr Space Technol 81:265–276

    Article  Google Scholar 

  • Hunter JD (2007) Matplotlib: a 2D graphics environment. Comput Sci Eng 9(3):90–95

    Article  Google Scholar 

  • Lin FC, Moschetti MP, Ritzwoller MH (2008) Surface wave tomography of the western United States from ambient seismic noise: Rayleigh and love wave phase velocity maps. Geophys J Int 173(1):281–298

    Article  ADS  Google Scholar 

  • Luo Y, Yang Y, Xie J, Yang X, Ren F, Zhao K, Xu H (2020) Evaluating uncertainties of phase velocity measurements from cross-correlations of ambient seismic noise. Seismol Res Lett 91(3):1717–1729

    Article  Google Scholar 

  • Mousavi SM, Zhu W, Sheng Y, Beroza GC (2019) CRED: A deep residual network of convolutional and recurrent units for earthquake signal detection. Sci Rep 9(1):1–14

    Article  Google Scholar 

  • Mousavi SM, Ellsworth WL, Zhu W, Chuang LY, Beroza GC (2020) Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nat Commun 11(1):3952

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  • Novoselov A, Balazs P, Bokelmann G (2022) Separating and denoising seismic signals with dual-path recurrent neural network architecture. J Geophys Res Solid Earth 127:e2021JB023183

    Article  ADS  Google Scholar 

  • Paszke A, Gross S, Massa F, Lerer A, Bradbury J et al (2019) Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems, vol 32

  • Perol T, Gharbi M, Denolle M (2018) Convolutional neural network for earthquake detection and location. Sci Adv 4(2):e1700578

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  • Shapiro NM, Campillo M, Stehly L, Ritzwoller MH (2005) High-resolution surface-wave tomography from ambient seismic noise. Science 307(5715):1615–1618

    Article  ADS  CAS  PubMed  Google Scholar 

  • Shen Y, Ren Y, Gao H, Savage B (2012) An improved method to extract very-broadband empirical Green’s functions from ambient seismic noise. Bull Seismol Soc Am 102(4):1872–1877

    Article  Google Scholar 

  • Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

  • Song W, Feng X, Wu G, Zhang G, Liu Y, Chen X (2021) Convolutional neural network, res-unet++, -based dispersion curve picking from noise cross-correlations. J Geophys Res Solid Earth 126(11):2021022027

    Article  ADS  Google Scholar 

  • Stehly L, Campillo M, Shapiro NM (2006) A study of the seismic noise from its long-range correlation properties. J Geophys Res Solid Earth 111(B10):B10306

    Article  ADS  Google Scholar 

  • Stehly L, Campillo M, Shapiro NM (2007) Traveltime measurements from noise correlation: stability and detection of instrumental time-shifts. Geophys J Int 171(1):223–230

    Article  ADS  Google Scholar 

  • Tsai VC (2009) On establishing the accuracy of noise tomography travel-time measurements in a realistic medium. Geophys J Int 178(3):1555–1564

    Article  ADS  Google Scholar 

  • Vaswani A, Shazeer N, Parmar N, Uszkoreit J et al (2017) Attention is all you need. In: Advances in neural information processing systems, vol 30

  • Viens L, Van Houtte C (2020) Denoising ambient seismic field correlation functions with convolutional autoencoders. Geophys J Int 220(3):1521–1535

    Article  ADS  Google Scholar 

  • Wegler U, Sens-Schönfelder C (2007) Fault zone monitoring with passive image interferometry. Geophys J Int 168(3):1029–1033

    Article  ADS  Google Scholar 

  • Xie J, Chu R, Ni S (2020) Relocation of the 17 June 2017 Nuugaatsiaq (Greenland) landslide based on Green’s functions from ambient seismic noises. J Geophys Res Solid Earth 125(5):e2019JB018947

    Article  ADS  Google Scholar 

  • Yang X, Bryan J, Okubo K, Jiang C, Clements T, Denolle MA (2022) Optimal stacking of noise cross-correlation functions. Geophys J Int 232(3):1600–1618

    Article  ADS  Google Scholar 

  • Ye F, Lin J, Shi Z, Lyu S (2018) Monitoring temporal variations in instrument responses in regional broadband seismic network using ambient seismic noise. Geophys Prospect 66(5):1019–1036

    Article  ADS  Google Scholar 

  • Zhang Y, Li H, Huang Y, Liu M, Guan Y, Su J, Wang T (2020a) Shallow structure of the Longmen Shan fault zone from a high-density, short-period seismic array. Bull Seismol Soc Am 110(1):38–48

    Article  Google Scholar 

  • Zhang X, Jia Z, Ross ZE, Clayton RW (2020b) Extracting dispersion curves from ambient noise correlations using deep learning. IEEE Trans Geosci Remote Sens 58(12):8932–8939

    Article  ADS  Google Scholar 

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

    Article  Google Scholar 

  • Zhu W, Beroza GC (2019) PhaseNet: a deep-neural-network-based seismic arrival-time picking method. Geophys J Int 216(1):261–273

    ADS  Google Scholar 

  • Zhu W, Mousavi SM, Beroza GC (2019) Seismic signal denoising and decomposition using deep neural networks. IEEE Trans Geosci Remote Sens 57(11):9476–9488

    Article  ADS  Google Scholar 

Download references

Acknowledgements

We thank IRIS for providing the dataset which we used to train and test the model. This research is financially supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ20D040002.

Funding

This work was supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ20D040002.

Author information

Authors and Affiliations

  1. College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou, Zhejiang, China

    Chunwei Jin, Fang Ye, Han Zhang & Xue Bao

Authors
  1. Chunwei Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fang Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Han Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xue Bao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fang Ye.

Ethics declarations

Conflict of interest

The authors acknowledge there are no conflicts of interest recorded.

Additional information

Edited by Dr. Qamar Yasin (ASSOCIATE EDITOR) / Prof. Gabriela Fernández Viejo (CO-EDITOR-IN-CHIEF).

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jin, C., Ye, F., Zhang, H. et al. Travel time picking of ambient noise cross-correlation using a deep neural network combining convolutional neural networks and Transformer. Acta Geophys. 72, 97–114 (2024). https://doi.org/10.1007/s11600-023-01088-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11600-023-01088-3

Keywords

Search

Navigation