Research Note: Sparsity‐Aware Multiple Microseismic Event Localization Blind to the Source Time‐Function

References

1. Aki, K. and Richards, P.2002. Quantitative Seismology, 2nd ed.Sausalito, CA: University Science Books.
   [Google Scholar]
2. Baig, A. and Urbancic, T.2010. Microseismic moment tensors: A path to understanding fracture growth. The Leading Edge29, 320–324.
   [Google Scholar]
3. Cevher, V., Durate, M.F. and Baraniuk, R.G.Distributed target localization via spatial sparsity. Proceedings of European Signal Processing Conference, August 2009.
4. Chen, J. and Huo, X.2006. Theoretical results on sparse representations of multiple‐measurement vectors. IEEE Transactions on Signal Processing54, 4634–4643.
   [Google Scholar]
5. CVX Research Inc
   CVX Research Inc . 2012. CVX: MATLAB software for disciplined convex programming, version 2.0 beta. http://cvxr.com/cvx.
6. Donoho, D.L.2006. Compressed sensing. IEEE Transactions on Information Theory52, 1289–1306.
   [Google Scholar]
7. Droujinine, A., Winsor, J. and Slauenwhite, K.Microseismic elastic full waveform inversion for hydraulic fracture monitoring. Proceedings of EAGE Conference and Exhibition, June 2012. Extended Abstracts, 2312–2316.
8. Eldar, Y., Kuppinger, P. and Bolcskei, H.2010. Block‐sparse signals: Uncertainty relations and efficient recovery. IEEE Transactions on Signal Processing58, 3042–3054.
   [Google Scholar]
9. Feng, C., Au, W.S.A., Valaee, S. and Tan, Z.2012. Received signal strength based indoor positioning using compressive sensing. IEEE Transactions on Mobile Computing11, 1983–1993.
   [Google Scholar]
10. Gibowicz, Y.2009. Seismicity induced by mining: Recent research. Advances in Geophysics51(6), 1–53.
    [Google Scholar]
11. Jamali‐Rad, H. and Leus, G.2013. Sparsity‐aware multi‐source TDOA localization. IEEE Transactions on Signal Processing61, 4874–4887.
    [Google Scholar]
12. Jamali‐Rad, H., Ramezani, H. and Leus, G.2014. Sparsity‐Aware Multi‐Source RSS Localization. Elsevier Signal Processing101, 174–191.
13. Lee, S.‐J., Huang, B.‐S., Liang, W.‐T. and Chen, K.‐C.2010. Grid‐based moment tensor inversion technique by using 3‐D Greens functions database: A demonstration of the 23 October 2004 Taipei earthquake. Terrestrial Atmospheric and Oceanic Sciences21, 503–514.
    [Google Scholar]
14. Li, J., Zhang, H.K.H. and Toksöz, M.N.2009. Focal mechanism determination using high frequency, full waveform information. SEG Expanded Abstracts28, 2312–2316.
    [Google Scholar]
15. Madariaga, R.2007. Seismic source theory. Treatise on Geophysics, 59–82.
    [Google Scholar]
16. Rodriguez, I.V. and Sacchi, M.D.Microseismic source characterization combining compressive sensing with a migration based methodology. Proceedings of EAGE Conference and Exhibition, June 2013.
17. Rodriguez, I.V., Sacchi, M. and Gu, Y.J.2012a. Simultaneous recovery of origin time, hypocentre location and seismic moment tensor using sparse representation theory. Geophysical Journal International188, 1188–1202.
    [Google Scholar]
18. Rodriguez, I.V., Sacchi, M. and Gu, Y.J.2012b. A compressive sensing framework for seismic source parameter. Geophysical Journal International191, 1226–1236.
    [Google Scholar]
19. Scognamiglio, L., Tinti, E. and Michelini, A.2009. Real‐time determination of seismic moment tensor for the Italian region. Bulletin of the Seismological Society of America 99, 2223–2242.
    [Google Scholar]
20. Tang, Z., Blacquière, G. and Leus, G.2011. Aliasing‐free wideband beamforming using sparse signal representation. IEEE Transactions on Signal Processing, 59, 3464–3469.
    [Google Scholar]
21. Yuan, M. and Lin, Y.2006. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B68, 49–67.
    [Google Scholar]