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Abstract

Summary

Geothermal resources are set to play an ever more important role in supplying a sustainable source of energy for the green transition. Here, we present an example of how recent developments in seismic node technology can provide enhanced monitoring of subsurface seismicity and crustal stress-state during geothermal field development. We show results for a network of 450 nodes deployed at a geothermal site in Cornwall, UK, during a well stimulation. A catalogue of 241 earthquakes are detected using a waveform-migration based method. The earthquakes are relocated using double-difference methods and used to map fault structure. Moment magnitudes, stress-drops, fault radii and focal mechanisms are calculated for the catalogue, in order to infer the stress-state of the faults and how fluids interact with the faults. Finally, S-wave velocity anisotropy measurements are used to compare local fault stresses to the orientation of the prevailing, macroscopic crustal stress-state. We find that the significant increase in spatial sampling provided by recent developments in seismic node technology allow us to elucidate fluid-fault interactions in far more detail than would otherwise be possible. These findings show the potential of recent technology advances for monitoring geothermal systems going forward.

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/content/papers/10.3997/2214-4609.202321058
2023-11-14
2026-01-16
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References

  1. Brune, J. N. [1970]. Tectonic Stress and the Spectra of Seismic Shear Waves from Earthquakes.Journal of Geophysical Research, 75(26), 4997–5009.
    [Google Scholar]
  2. Hudson, T. S., Kendall, J. M., Blundy, J. D., Pritchard, M. E., MacQueen, P., Wei, S. S., Gottsmann, J.H., Lapins, S.. [2023a]. Hydrothermal Fluids and Where to Find Them: Using Seismic Attenuation and Anisotropy to Map Fluids Beneath Uturuncu Volcano, Bolivia.Geophysical Research Letters, 50(5), 1–16.
    [Google Scholar]
  3. Hudson, T. S., Kufner, S. K., Brisbourne, A. M., Kendall, J. M., Smith, A. M., Alley, R. B., Arthern, R. J., Murray, T.. [2023b]. Highly variable friction and slip observed at Antarctic ice stream bed.Nature Geoscience.
    [Google Scholar]
  4. Hudson, T. S., Kendall, J. M., Pritchard, M. E., Blundy, J. D., & Gottsmann, J. H. [2022]. From slab to surface: Earthquake evidence for fluid migration at Uturuncu volcano, Bolivia.Earth and Planetary Science Letters, 577, 117268.
    [Google Scholar]
  5. Hudson, T. S., Smith, J., Brisbourne, A. M., & White, R. S. [2019]. Automated detection of basal icequakes and discrimination from surface crevassing.Annals of Glaciology, 60(79), 167–181.
    [Google Scholar]
  6. Hudson, T. S., Asplet, J., & Walker, A. M. [in review]. Automated shear-wave splitting analysis for single- and multi- layer anisotropic media.Seismica.
    [Google Scholar]
  7. Kingdon, A., Williams, J., Fellgett, M., Rettelbach, N., & Heidbach, O. (2022). Stress Map of Great Britain and Ireland 2022.GFZ German Research Center for Geosciences.
    [Google Scholar]
  8. Lomax, A., & Virieux, J. [2000]. Probabilistic earthquake location in 3D and layered models.Advances in Seismic Event Location, Volume 18 of the Series Modern Approaches in Geophysics, 101–134.
    [Google Scholar]
  9. Ourabah, A., & Chatenay, A. [2022]. Unlocking ultra-high-density seismic for CCUS applications by combining nimble nodes and agile source technologies.The Leading Edge, 41(1), 27–33.
    [Google Scholar]
  10. Pugh, D. J., & White, R. S. [2018]. MTfit: A Bayesian Approach to Seismic Moment Tensor Inversion.Seismological Research Letters, XX(Xx), 1–7. https://doi.org/10.1785/0220170273
    [Google Scholar]
  11. Pugh, D. J., White, R. S., & Christie, P. A. F. [2016]. Automatic Bayesian polarity determination.Geophysical Journal International, 206(1), 275–291.
    [Google Scholar]
  12. Smith, J. D., White, R. S., Avouac, J.-P., & Bourne, S. [2020]. Probabilistic earthquake locations of induced seismicity in the Groningen region, the Netherlands.Geophysical Journal International, 222(1), 507–516.
    [Google Scholar]
  13. Trugman, D. T., & Shearer, P. M. [2017]. GrowClust: A Hierarchical clustering algorithm for relative earthquake relocation, with application to the Spanish Springs and Sheldon, Nevada, earthquake sequences.Seismological Research Letters, 88(2), 379–391.
    [Google Scholar]
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