With the advent of efficient seismic data acquisition, we are having a surplus of seismic data, which is improving the imaging of the earth using full-waveform inversion. However, such inversion suffers from many issues, including (i) substantial network waiting time due to repeated communications of function and gradient values in the distributed environment, and (ii) requirement of the sophisticated optimizer to solve an optimization problem involving non-smooth regularizers. To circumvent these issues, we propose a decentralized full-waveform inversion, a scheme where connected agents in a network optimize their objectives locally while being in consensus. The proposed formulation can be solved using the ADMM method efficiently. We demonstrate using the standard Marmousi model that such scheme can decouple the regularization from data fitting and reduce the network waiting time.


Article metrics loading...

Loading full text...

Full text loading...


  1. Berenger, J.P.
    [1994] A perfectly matched layer for the absorption of electromagnetic waves. Journal of computational physics, 114(2), 185–200.
    [Google Scholar]
  2. Bertsekas, D.P. and Tsitsiklis, J.N.
    [1989] Parallel and distributed computation: numerical methods, 23. Prentice hallEnglewood Cliffs, NJ.
    [Google Scholar]
  3. Boyd, S., Parikh, N., Chu, E., Peleato, B. and Eckstein, J.
    [2011] Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine Learning, 3(1), 1–122.
    [Google Scholar]
  4. Brougois, A., Bourget, M., Lailly, P., Poulet, M., Ricarte, P. and Versteeg, R.
    [1990] Marmousi, model and data. In: EAEG Workshop-Practical Aspects of Seismic Data Inversion.
    [Google Scholar]
  5. Combettes, P.L. and Pesquet, J.C.
    [2011] Proximal splitting methods in signal processing. In: Fixed-point algorithms for inverse problems in science and engineering, Springer, 185–212.
    [Google Scholar]
  6. Moreau, J.J.
    [1965] Proximité et dualité dans un espace hilbertien. Bull. Soc. Math. France, 93(2), 273–299.
    [Google Scholar]
  7. Nedic, A. and Ozdaglar, A.
    [2009] Distributed subgradient methods for multi-agent optimization. IEEE Transactions on Automatic Control, 54(1), 48–61.
    [Google Scholar]
  8. Nocedal, J.
    [1980] Updating quasi-Newton matrices with limited storage. Mathematics of computation, 35(151), 773–782.
    [Google Scholar]
  9. Pratt, R.G.
    [1999] Seismic waveform inversion in the frequency domain, Part 1: Theory and verification in a physical scale model. Geophysics, 64(3), 888–901.
    [Google Scholar]
  10. Schmidt, M., Berg, E., Friedlander, M. and Murphy, K.
    [2009] Optimizing costly functions with simple constraints: A limited-memory projected quasi-newton algorithm. In: Artificial Intelligence and Statistics. 456–463.
    [Google Scholar]
  11. Virieux, J. and Operto, S.
    [2009] An overview of full-waveform inversion in exploration geophysics. Geophysics, 74(6), WCC1–WCC26.
    [Google Scholar]

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