1887

Abstract

Summary

We propose a new metric called Spatial Aware Similitude Index (SASI) to compute quality of prediction and models that contain salt bodies. The metric is more informative than traditional general metrics such as SSIM or Jaccard.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202132015
2021-03-08
2024-04-16
Loading full text...

Full text loading...

References

  1. Araya-Polo, M., Jennings, J., Adler, A. and Dahlke, T.
    [2018] Deep-learning tomography. SEG The Leading Edge, 37(1), 58–66.
    [Google Scholar]
  2. Bergen, K.J., Johnson, P.A., de Hoop, M.V. and Beroza, G.C.
    [2019] Machine learning for data-driven discovery in solid Earth geoscience. Science, 363(6433).
    [Google Scholar]
  3. Guillon, S., Joncour, F., Barrallon, P.E. and Castanié, L.
    [2020] Ground-truth uncertainty-aware metrics for machine learning applications on seismic image interpretation: Application to faults and horizon extraction. SEG The Leading Edge, 39(10), 734–741.
    [Google Scholar]
  4. Sun, Y., Denel, B., Daril, N., Evano, L., Williamson, P. and Araya-Polo, M.
    [2020] Deep learning joint inversion of seismic and electromagnetic data for salt reconstruction. In: SEG Technical Program Expanded Abstracts 2020. Society of Exploration Geophysicists, 550–554.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202132015
Loading
/content/papers/10.3997/2214-4609.202132015
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error