1887

Abstract

Summary

A cross-well tomography with a geostatistical constraint via fuzzy C-means clustering (FCM) is proposed to extract geologically realistic velocity models for acquisition during mineral resource definition drilling. Constraining a damped least squares inversion process with the additional requirement to keep velocity values in clusters counters the smearing of geological boundaries by the addition of smoothing constraints, which are typically used. The FCM center values were either set as priori known values from borehole measurements or estimated from the data during the cross-well inversion process. We have applied our FCM constrained inversion to three synthetic models and then compared these results to a conventional smoothness constrained inversion result. The three models approximate three simple-to-challenging possible scenarios within a polymetallic deposit embedded in meta-sedimentary rocks. A significant improvement resulted using FCM constraints in the recovery of the true structure, particularly at the boundaries, and various artifacts were better damped. The additional accuracy can provide considerable benefits in improve the resource models in the mine planning stage, and optimize ground-support design and blasting parameter estimation during mining.

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/content/papers/10.3997/2214-4609.201802636
2018-09-09
2024-04-25
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References

  1. Berget, I., Mevik, B., Vebø, H., and Næs, T.
    , 2005, A strategy for finding relevant clusters; with an application to microarray data: Journal of Chemometrics, v. 19, no. 9, p. 482–491.
    [Google Scholar]
  2. Bezdek, J. C., Ehrlich, R., and Full, W.
    , 1984, FCM: The fuzzy c-means clustering algorithm: Computers & Geosciences, v. 10, no. 2–3, p. 191–203.
    [Google Scholar]
  3. Constable, S. C., Parker, R. L., and Constable, C. G.
    , 1987, Occam’s inversion; a practical algorithm for generating smooth models from electromagnetic sounding data: Geophysics, v. 52, no. 3, p. 289–300.
    [Google Scholar]
  4. Giroux, B., Gloaguen, E., and Chouteau, M.
    , 2007, bh_tomo—a Matlab borehole georadar 2D tomography package: Computers & Geosciences, v. 33, no. 1, p. 126–137.
    [Google Scholar]
  5. Kieu, T. D., and Kepic, A.
    , 2015, Incorporating Prior Information into Seismic Impedance Inversion Using Fuzzy Clustering Technique, SEG Annual Meeting: New Orleans, USA.
    [Google Scholar]
  6. Paige, C. C., and Saunders, M. A.
    , 1982, LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares: ACM Trans. Math. Softw., v. 8, no. 1, p. 43–71.
    [Google Scholar]
  7. Russell, B. H.
    , 1988. Introduction to seismic inversion methods / Brian H.Russell. Tulsa, Okla. (8801 South Yale St., Tulsa OK 74137-3175), Society of Exploration Geophysicists.
    [Google Scholar]
  8. Sun, J., and Li, Y.
    , 2011, Geophysical inversion using petrophysical constraints with application to lithology differentiation: 24th European Meeting of Environmental and Engineering Geophysics, p. 2644–2648.
    [Google Scholar]
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