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Abstract

Summary

A novel algorithm for subsurface characterisation is presented. The algorithm shares many of the benefits of machine learning approaches but it has the advantage of being parameterised in terms familiar to a geoscientist.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202075014
2020-09-07
2021-01-18

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References

  1. Bosch, M., Mukerji, T. and Gonzalez, E.F.
    [2010] Seismic inversion for reservoir properties combining statistical rock physics and geostatistics: A review. Geophysics, 75, No. 5, 75A165–75A176.
     [Google Scholar]
  2. Jullum, M. and Kolbjørnsen, xO.
    [2016] A Gaussian-based framework for local Bayesian inversion of geophysical data to rock properties. Geophysics, 81, No. 3, 1–13.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202075014
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A Novel Subsurface Characterization Algorithm Parameterized in Terms Familiar to Geoscientists
Conference Proceedings 2020, 1 (2020); https://doi.org/10.3997/2214-4609.202075014
/content/papers/10.3997/2214-4609.202075014
/content/papers/10.3997/2214-4609.202075014
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