First Break Picking

First-break (or first-arrival) picking refers to the identification of the first arriving seismic energy in time-space dimension of shot gathers and it is used for different applications such as static correction. Static corrections compensate for the effect of near-surface irregularities and differences in the elevation of source and receivers. First-break picking is similar to the phase picking of microseismic events or earthquake data with the difference that it is usually performed on a single-component 2D shot gather and based on the global information like the correlations among traces.

Used ML Approaches:

  • Fuzzy Logic

  • Artificial Neural Networks

Used Neural Networks:

  • FC

  • CNN

  • RNN

  • U-Net

  • 'ResNet

  • AlexNet

  • GoogleNet

  • SqueezeNet

  • LSTM

Used Learning Procedures:

  • Supervised Learning

  • Active Learning

  • Transfer Learning

References:

  1. Chu, C. K., & Mendel, J. M. (1994). First break refraction event picking using fuzzy logic systems. IEEE Transactions on Fuzzy Systems, 2(4), 255-266.

  2. Turhan Taner, M., Lu, L., & Baysal, E. (1988). Unified method for 2-D and 3-D refraction statics with first break picking by supervised learning. In SEG Technical Program Expanded Abstracts 1988 (pp. 772-774). Society of Exploration Geophysicists.

  3. Murat, M. E., & Rudman, A. J. (1992). Automated first arrival picking: A neural network approach 1. Geophysical Prospecting, 40(6), 587-604.

  4. McCormack, M. D., Zaucha, D. E., & Dushek, D. W. (1993). First-break refraction event picking and seismic data trace editing using neural networks. Geophysics, 58(1), 67-78.

  5. Glinsky, M. E., Clark, G. A., Cheng, P. K., Devi, K. S., Robinson, J. H., & Ford, G. E. (2001). Automatic event picking in prestack migrated gathers using a probabilistic neural network. Geophysics, 66(5), 1488-1496.

  6. LI, Z., SHENG, R., XU, W., & YANG, J. (2013). The improvement of neural network cascade-correlation algorithm and its application in picking seismic first break. Advances in Petroleum Exploration and Development, 5(2), 41-51.

  7. Maity, D., Aminzadeh, F., & Karrenbach, M. (2014). Novel hybrid artificial neural network based autopicking workflow for passive seismic data. Geophysical Prospecting, 62(4-Vertical Seismic Profiling and Microseismicity Frontiers), 834-847.

  8. Yuan, S., Liu, J., Wang, S., Wang, T., & Shi, P. (2018). Seismic waveform classification and first-break picking using convolution neural networks. IEEE Geoscience and Remote Sensing Letters, 15(2), 272-276.

  9. Hu, L., Zheng, X., Duan, Y., Yan, X., Hu, Y., & Zhang, X. (2019). First-arrival picking with a U-net convolutional network. Geophysics, 84(6), U45-U57.

  10. Duan, X., & Zhang, J. (2020). Multitrace first-break picking using an integrated seismic and machine learning method. Geophysics, 85(4), WA269-WA277.

  11. Liao, X., Cao, J., Hu, J., You, J., Jiang, X., & Liu, Z. (2019). First Arrival Time Identification Using Transfer Learning With Continuous Wavelet Transform Feature Images. IEEE Geoscience and Remote Sensing Letters, 17(11), 2002-2006.

  12. Ma, Y., Cao, S., Rector, J. W., & Zhang, Z. (2020). Automated arrival-time picking using a pixel-level network. Geophysics, 85(5), V415-V423.

  13. Yuan, P., Wang, S., Hu, W., Wu, X., Chen, J., & Van Nguyen, H. (2020). A robust first-arrival picking workflow using convolutional and recurrent neural networks. Geophysics, 85(5), U109-U119.

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