1887
Volume 65, Issue 6
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

We examine the problem of localization of a single microseismic event and determination of its seismic moment tensor in the presence of strongly correlated noise. This is a typical problem occurring in monitoring of microseismic events from a daylight surface during producing or surface monitoring of hydraulic fracturing. We propose a solution to this problem based on the method of maximum likelihood. We discuss mathematical aspects of the problem, some features and weak points of the proposed approach, estimate the required computing resources, and present the results of numerical experiments. We show that the proposed approach is much more resistant to correlated noises than diffraction stacking methods and time reverse modeling.

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/content/journals/10.1111/1365-2478.12485
2017-02-20
2024-04-19
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References

  1. AkiK. and RichardsP.G.1980. Quantitative seismology. Freeman and Co.
    [Google Scholar]
  2. AkiK.1967. Scaling law of Seismic spectrum. Journal of Geophysical Research72, 1217–1231.
    [Google Scholar]
  3. AnikievD., ValentaJ., StanekF. and EisnerL.2014. Joint location and source mechanism inversion of microseismic events: benchmarking on seismicity induced by hydraulic fracturing. Geophys. J. Int. v. 198, 249–258.
    [Google Scholar]
  4. BaigA.M. and UrbancicT.I.2010. Microseismic moment tensor: A path to understanding Growth of Hydraulic Fractures. The Leading Edge, 29, 936–940.
    [Google Scholar]
  5. BaigA.M. and UrbancicT.I.2012. Structural Controls on Vertical Growth of Hydraulic Fractures as Revealed Through Seismic Moment Tensor Inversion Analysis. Paper SPE Annual Techical Conference and Exhibition, San Antonia, Texas, USA, Vol. 4, 3387–3394.
  6. BerezhnoiD.V., BirialtsevE.V., BiryaltsevaT.E., DemidovD.E. and MokshinE.V.2013. Effective modelling of microseismic waves propagation in porous media. XXVth International conference: Mathematical modeling in mechanics of deformable bodies and structures. Boundary and finite elements methods. St. Petersburg, Russia, 140–141. (in Russian)
  7. DemidovD., AhnertK., RuppK. and GottschlingP.2013. Programming CUDA and OpenCL: A case study using modern C++ libraries. SIAM Journal on Scientific Computing35(5), C453–C472.
    [Google Scholar]
  8. ChatfieldC., CollinsA.J.1980. Introduction to Multivariate Analysis. London: Chapman and Hall.
    [Google Scholar]
  9. GajewskiD. and TessmerE.2005. Reverse modelling for seismic event characterization. Geophys. J. Int. 163(1), 276–284.
    [Google Scholar]
  10. GajewskiD., VanelleC., AnikievD., KashtanB., TessmerE. and TisljarM.2007. Source localization by diffraction stacking. SEG Expanded Abstracts26.
    [Google Scholar]
  11. GalimovM. and Biryal'tsevE.2010. Some technological aspects of GPGPU applications in applied program systems. Vychisl. Metody Programm11(3), 77–93. (in Russian)
    [Google Scholar]
  12. GhartiH., OyeV., KühnD. and ZhaoP.2011. Simultaneous microearthquake location and moment tensor estimation using time reversal imaging. SEG Technical Program Expanded Abstracts319, 1632–1637.
    [Google Scholar]
  13. GrassbergerP. and ProcacciaI.1983. Measuring the strangeness of strange attractors. Physica9D, 189–208.
    [Google Scholar]
  14. GrobM. and Van der BaanM.2012. Statistics of microseismic events: implications for geomechanics. CSEG GeoConvention: Vision, Calgary, Canada.
  15. GutenbergR. and RichterC.F.1944. Frequency of earthquakes in California. Bulletin of the Seismological Society of America34, 185–188.
    [Google Scholar]
  16. HagedoornJ.G.1954. A process of seismic reflection interpretation. Geophysical Prospecting2, 85–127.
    [Google Scholar]
  17. HusinS. and HardebeckJ.L.2010. Earthquake location accuracy. Community Online Resource for Statistical Seismicity Analysis V. 1, 3–30.
    [Google Scholar]
  18. KanasewichE.R. and PhadkeS.M.1988. Imaging discontinuities on seismic sections. Geophysics53, 334–345.
    [Google Scholar]
  19. KapetanidisV. and PapadimitriouP.2011. Estimation of arrival‐times in intense seismic sequences using a Master‐Events methodology based on waveform similarity. Geophysical Prospecting187, 889–917.
    [Google Scholar]
  20. KiselevitchV.L., NikolaevA.V., TroitskiyP.A. and ShubikB.M.1991. Emission tomography: Main ideas, results, and prospects. 61st Annual International Meeting, SEG, Expanded Abstracts, 1602.
  21. KreyT.1952. The significance of diffraction in the investigation of faults. Geophysics17, 843–858.
    [Google Scholar]
  22. Kushnir, A., Varypaev, A., Dricker, I., Rozhkov, M. and Rozhkov, N.2014. Passive surface microseismic monitoring as a statistical problem: location of weak microseismic signals in the presence of strongly correlated noise. Geophys. J. Int. 198(2), 1186–1198.
    [Google Scholar]
  23. Landa, E., Shtivelman, V. and Gelchinsky, B.1987. A method for detection of diffracted waves on common‐offset sections. Geophysical Prospecting35, 359–374.
    [Google Scholar]
  24. Lehman, E.L. and Cazella, G.1998. Theory of Point Estimation (2nd ed.). New York: Springer‐Verlage.
    [Google Scholar]
  25. MaxwellS.2009. Microseismic location uncertainty. CSEG Recorder, 41–46.
    [Google Scholar]
  26. RenauxA., ForsterPh., LarzabalP., RichmondCh. D. and NehoraiA.2008. A Fresh Look at the Bayesian Bounds of the Weiss‐Weinstein Family. IEEE Transactions of Signal Processing, Vol. 56. No. 11b5334–5352.
    [Google Scholar]
  27. RutledgeJ.T., PhillipsW.S., HouseL.S. and ZinnoR.J.1998. Microseismic mapping of a Cotton Valley hydraulic fracture using Decimated downhole arrays. In Proc. SEG Annual Meeting, New Orleans.
  28. SipkinS.A.1982. Estimation of earthquake source parameters by the inversion of waveform data: synthetic waveforms. Phys. Earth planet. Inter.30(2–3), 242–259.
    [Google Scholar]
  29. StumpB.W. and JohnsonL.R.1977. The determination of source properties by the linear inversion of seismograms. Bull. Seism. Soc. Am.67, 1489–1502.
    [Google Scholar]
  30. ShapiroS.A.2015. Fluid‐Induced Seismicity. Cambridge University Press. 289 pages.
    [Google Scholar]
  31. TchebotarevaI.I., NikolaevA.V. and SatoH.2000Seismic Emission Activity of Earth's Crust in Northern KantoV. 120N3, 167–182.
  32. ThorntonM. and MuellerM.2013. Uncertainty in surface microseismic monitoring. CSEG GeoConvention.
    [Google Scholar]
  33. UrbancicT.I. and MaxwellS.C.2002. Source Parameters of Hydraulic Fracture Induced Microseismicity. SPE Annual Technical Conference and Exhibiyion 77439‐MS.
    [Google Scholar]
  34. UrbancicT.I., ShumilaV., RutledgeJ.T. and Zinn, R.J.1999. Determining Hydraulic Fracture Behaviour Using Microseismicity. Proc. 37th U.S. Rock Mech. Symp., Vail, CO, USA, 991–996.
  35. UsherP.J., AngusD.A. and VerdonJ.P.2011. Influence of velocity model and source frequency on microseismic waveforms some implications for microseismic locations. Geophysical Prospecting, 1–9.
    [Google Scholar]
  36. WhalenA.D., BookerH.G. and DeclarisN.2013. Detection of Signals in Noise. Elsevier Science, 411 pp.
    [Google Scholar]
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