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Abstract

Summary

This work presents progress on the development of a compression protocol for passive seismic data that combines compressive sensing and deep learning. The objective of the protocol is to facilitate fast estimations of event location and moment tensor operating over the seismic data in compressed form, thereby permitting fast data transmission and removing the decompression overhead. The compression acts over the recording channel dimension, which makes it especially relevant for dense data acquisitions such as in distributed acoustic sensing.

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/content/papers/10.3997/2214-4609.202131024
2021-03-01
2024-04-26
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References

  1. Bohnhoff, M., Dresen, G., Ellsworth, W. and Ito, H.
    , 2019. Passive seismic monitoring of natural and induced earthquakes: Case studies, future directions and socio-economic relevance, in New Frontiers in Integrated Solid Earth Sciences, eds. Cloethingh, S. & Negendank, J., Springer, Dordrecht.
    [Google Scholar]
  2. Donoho, D.
    , 2006. Compressed Sensing, IEEE Transactions on Information Theory, 52, 1289–1306.
    [Google Scholar]
  3. Vera Rodriguez, I. and Sacchi, M.
    , 2017. Seismic monitoring with compressed sensing, in Compressive Sensing of Earth Observations, ed. Chen, C., CRC Press, Boca Raton.
    [Google Scholar]
  4. Vera Rodriguez, I., Sacchi, M. and Gu, Y.
    , 2012. A compressive sensing framework for seismic source parameter estimation, Geophysical Journal International, 191, 1226–1236.
    [Google Scholar]
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