Advertisement

Using a Hierarchical Temporal Memory Cortical Algorithm to Detect Seismic Signals in Noise

  • Ruggero MichelettoEmail author
  • Kahoko Takahashi
  • Ahyi Kim
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 858)

Abstract

Recognizing seismic waves immediately is very important for the realization of efficient disaster prevention. Generally, these systems consist of a network of seismic detectors that send real time data to a central server. The server elaborates the data and attempts to recognize the first sign of an earthquake. The problem with this approach is that it exists a critical trade-off between sensitivity of the system and error rate. To overcame this problem, an artificial neural network based intelligent learning system can be used. However, conventional supervised ANN systems are difficult to train, CPU intensive and prone to false alarms. To surpass these problems, here we attempt to use a next-generation unsupervised cortical algorithm hierarchical temporal memory (HTM). This novel approach does not learn particular waveforms, but adapts to continuously fed data reaching the ability to discriminate between normality (seismic sensor background noise in no-earthquake conditions) and anomaly (sensor response to a jitter or an earthquake). Main goal of this study is to test the ability of the HTM algorithm to be used to signal earthquakes automatically in a feasible disaster prevention system. We describe the methodology used and give the first qualitative assessments of the recognition ability of the system. Our preliminary results show that the cortical algorithm used is very robust to noise and that it can successfully recognize synthetic earthquake-like signals efficiently and reliably.

Keywords

Earthquake Hierarchical temporal memory (HTM) Seismic waves Unsupervised 

Notes

Acknowledgment

The authors would like to thank Matt Taylor of Numenta Open Source Community.

References

  1. 1.
    Rex, A.: Automatic earthquake recognition and timing from single traces. Bull. Seismol. Soc. Am. 68(5), 1521–1532 (1978)Google Scholar
  2. 2.
    Takahashi, K., Matsumoto, M., Uematsu, T., Micheletto, R., Kim, A.: Development of a low signal/noise ratio seismic device using an artificial neural networks. In: The Seismological Society of Japan: Fall Meeting. SSJ, October 2017Google Scholar
  3. 3.
    Dai, H., MacBeth, C.: Automatic picking of seismic arrivals in local earthquake data using an artificial neural network. Geophys. J. Int. 120, 758–774 (1995)CrossRefGoogle Scholar
  4. 4.
    Akram, J., Eaton, D.W.: A review and appraisal of arrival-time picking methods for downhole microseismic data. Geophysics 81(2), KS71–KS91 (2016)CrossRefGoogle Scholar
  5. 5.
    Adeli, H., Panakkat, A.: A probabilistic neural network for earthquake magnitude prediction. Neural Netw. 22(7), 1018–1024 (2009)CrossRefGoogle Scholar
  6. 6.
    Cui, Y., Ahmad, S., Hawkins, J.: Continuous online sequence learning with an unsupervised neural network model. Neural Comput. 28(11), 2474–2504 (2016).  https://doi.org/10.1162/NECOa00893
  7. 7.
    Numenta, Nupic installation and files (github) (2016). https://github.com/numenta/nupic
  8. 8.
    Taylor, M.: Tutorial on swarming for nupic (2017). https://youtu.be/KuFfm3ncEwI

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ruggero Micheletto
    • 1
    Email author
  • Kahoko Takahashi
    • 1
  • Ahyi Kim
    • 1
  1. 1.Graduate School of NanobiosystemsYokohama City UniversityYokohama, Kanazawa-kuJapan

Personalised recommendations