Phase Picking

It refers to the identification and measurement of arrival times of distinct seismic phases (P-wave or S-wave phases) within an earthquake waveform. This has applications such as in earthquake location or seismic velocity inversion using travel-time-based methods.

Used ML Approaches:

  • Artificial Neural Networks

  • K‐Means

  • Hidden Markov Model

  • Fuzzy Logic

Used Neural Networks:

  • CNN

  • FC

  • RNN

  • GRU

  • Transformer

  • LSTM

  • ResNet

  • CapsNet

  • Siamese

  • Autoencoder

  • DeepLab

  • VGG

  • U-Net

  • R-CNN

  • Attention

  • GAN

Used Learning Procedures:

  • Supervised Learning

  • Unsupervised Learning

  • Transfer Learning

References:

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  28. He, Z., Peng, P., Wang, L., & Jiang, Y. (2020). PickCapsNet: capsule network for automatic p-wave arrival picking. IEEE Geoscience and Remote Sensing Letters, 18(4), 617-621.

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  33. Yuan, C., & Zhang, J. (2019). Applying deep learning to teleseismic phase detection and picking: PcP and PKiKP cases. arXiv preprint arXiv:1910.09049.

  34. Zhang, G., Lin, C., & Chen, Y. (2020). Convolutional neural networks for microseismic waveform classification and arrival picking. Geophysics, 85(4), WA227-WA240.

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  36. Zhang, J., & Sheng, G. (2020). First arrival picking of microseismic signals based on nested U-Net and Wasserstein Generative Adversarial Network. Journal of Petroleum Science and Engineering, 195, 107527.

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