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Abstract

Summary

Microseismic monitoring is a key tool for many industry activities, where human-induced seismicity must be monitored for risk mitigation, production enhancement or operations continuity. Recorded signals are complex and composed of different classes: actual microearthquakes, anthropogenic noise, electromagnetic interferences, natural earthquakes, quarry blasts, etc. requiring time-consuming manual expert review. While manual processing can feel like a quality assurance, it is impacted by individual’s interpretation, fatigue and availability. Furthermore, profitability is reduced due to the blend of pertinent and non-essential data.

In this paper, we use Deep Learning with multi-input Convolutional Neural Networks (CNNs) to automate microseismic monitoring signals classification. Goals are threefold: improved quality thanks to consistent machine decisions, enhanced productivity by removing the need for manual sorting of false positives, and new technologies applications such as early warning of risks through 24/7 automatic processing. For that purpose, we train multi-input CNNs to identify images of our labelled data transformed into a time-frequency representation known as the scalogram. We demonstrate the efficiency of the method, capable of successfully reproducing expert classifications in real time, and providing a tool that reduces workload by 90 up to 100%.

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/content/papers/10.3997/2214-4609.202132010
2021-03-08
2024-03-29
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References

  1. Byeon, Y.H., Pan, S.B., Kwak, K.C.
    [2019] Intelligent deep models based on scalograms of electrocardiogram signals for biometrics, Sensors19(4):935, doi:10.3390/s19040935
    https://doi.org/10.3390/s19040935 [Google Scholar]
  2. Chen, Y., Zhang, G., Bai, M., Zu, S., Guan, Z., Zhang, M.
    [2019] Automatic waveform classification and arrival picking based on convolutional neural network. Earth and Space Science, 6, 1244–1261, doi:10.1029/2018EA000466
    https://doi.org/10.1029/2018EA000466 [Google Scholar]
  3. Sun, Y., Zhu, L., Wang, G., Zhao, F
    [2017] Multi-Input Convolutional Neural Network for Flower Grading. Hindawi, Journal of Electrical and Computer Engineering, Volume 2017, Article ID 9240407, doi:10.1155/2017/9240407
    https://doi.org/10.1155/2017/9240407 [Google Scholar]
  4. Peng, P., He, Z., Wang, L., Jiang, Y.
    [2020] Automatic classification of microseismic records in underground mining: a deep learning approach, IEEE Access, doi:10.1109/ACCESS.2020.2967121
    https://doi.org/10.1109/ACCESS.2020.2967121 [Google Scholar]
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