1887

Abstract

Summary

We present the results of adapting seismic interferometry (SI) for reflection imaging in mineral exploration. We use a unique dataset of one-month ambient-noise recordings acquired with large-N array (~1000 receivers) deployed in a regular grid (50 m receiver interval, 200 m line interval) directly above the known mineralisation and underground mine infrastructure at the Kylylahti polymetallic mine located in Eastern Finland. Ambient-noise in the study area is dominated by the road traffic and mine activities (both surface and underground) providing quasi omni-directional distribution and broad freqeuncy spectrum of the noise sources. We start from the simple 2D forward modelling using existing geological model. Results of the SI applied to field data from selected receiver lines exhibit reflections related to the bottom of the ore body as well as the reflection from the target area confirmed by synthetics. Finally, we develop robust mineral exploration SI workflow (MESI) tailored for reflection imaging and apply it to our 3D ambient-noise dataset. Migrated sections obtained from the MESI-processed data exhibit high reflectivity, compatible with the active-source seismics and directly related to the known geological structures.

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/content/papers/10.3997/2214-4609.201802703
2018-09-09
2024-04-16
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References

  1. Cheraghi, S., Craven, J. A. and Bellefleur, G.
    [2015] Feasibility of virtual source reflection seismology using interferometry for mineral exploration: A test study in the Lalor Lake volcanogenic massive sulphide mining area, Manitoba, Canada. Geophysical Prospecting, 63(4), 833–848.
    [Google Scholar]
  2. Draganov, D., Campman, X., Thorbecke, J. W., Verdel, A. and Wapenaar, K.
    [2009] Reflection images from ambient seismic noise. Geophysics, 74(5), A63–A67.
    [Google Scholar]
  3. [2013] Seismic exploration-scale velocities and structure from ambient seismic noise (>1 Hz). J. Geophys. Res. Solid Earth, 118(8), 4345–4360.
    [Google Scholar]
  4. Draganov, D. and RuigrokE.
    [2015] Passive Seismic Interferometry for Subsurface Imaging. Encyclopedia of Earthquake Engineering.
    [Google Scholar]
  5. Forghani, S. and SniederR.
    [2010] Underestimation of body waves and feasibility of surface-wave reconstruction by seismic interferometry. The Leading Edge, 29(7), 790–794.
    [Google Scholar]
  6. Luhta, T., Mertanen, S., Koivisto, E., Heinonen, S., Törmälehto, T. and Kukkonen, I.
    [2016] The seismic signature of the Kylylahti deposit: Initial results from new petrophysical measurements. 9th symposium on the Structure, Composition and Evolution of the Lithosphere in Finland, Programme and Extended Abstracts.
    [Google Scholar]
  7. Nakata, N., Chang, J. P., Lawrence, J. F. and Boue, P.
    [2015] Body wave extraction and tomography at Long Beach, California, with ambient-noise interferometry. J. Geophys. Res. Solid Earth, 120(2), 1159–1173.
    [Google Scholar]
  8. Olivier, G., Brenguier, F., CampilloM., LynchR. and Roux, P.
    [2015] Body-wave reconstruction from ambient noise seismic noise correlations in an underground mine. Geophysics, 80(3), KS11–KS25.
    [Google Scholar]
  9. Thorbecke, J. W. and Draganov, D.
    [2011] Finite-difference modeling experiments for seismic interferometry. Geophysics, 76(6), H1–H18.
    [Google Scholar]
  10. Wapenaar, K. and Fokkema, J.
    [2006] Green’s function representations for seismic interferometry. Geophysics, 71(4), SI33–SI46.
    [Google Scholar]
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