1887
Volume 62 Number 4
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

We develop and apply an imaging procedure for simultaneous location and characterization of seismic source properties called Moment Tensor Migration Imaging. The procedure constructs images for moment tensor components using a weighted diffraction stack migration, and combines ray‐theoretical Green's functions with a reverse time moment tensor imaging methodology. By applying an approximation we term the ‘ray‐angles only approximation’, we form an expression for Moment Tensor Migration Imaging where the migration weights depend only on the take‐off and arrival angles for rays leaving receiver positions and incident upon the image points. Moment Tensor Migration Imaging retains the benefits of diffraction stack procedures for source location and characterization, namely speed, flexibility, and the potential for incorporating non‐linear stacking procedures, whilst also providing the benefits of moment tensor imaging such as: the inclusion of multiple phase and multiple component data; the collapsing of the source radiation pattern; estimation of the moment tensor.

We examine variations of the imaging procedure through a synthetic test. We show that although the assumptions required for the imaging and ray‐angles only approximation may not be strictly valid for realistic survey geometries, a simple weight adjustment can be used to obtain more accurate and stable results in these situations. In our synthetic example we find that the use of a P‐wave only migration without this reweighting structure produces poor results, whereby the resulting images show activity upon incorrect moment tensor components. However, many of these effects are mitigated by use of the reweighting scheme and the results are further improved through the introduction of non‐linear stacking operators such as semblance weighted stacks. The highest quality moment tensor images (for the synthetic test examined here) are obtained through the use of both P‐wave and S‐wave wave fields. This highlights the importance of multicomponent data and multiphase modelling when characterizing seismic sources. We also find that the imaged moment tensor components vary proportionately when the input velocities are perturbed by a scale factor. This suggests, for the geometry investigated here, derived source properties such as fault‐plane solutions and shear‐tensile components will not be influenced by bulk changes in seismic velocities. Finally, we show the application to a real microseismic event observed using a surface array during hydraulic fracturing. We find that the procedure collapses the seismic radiation pattern into an anomaly with a maximum at the hypocentre and our derived mechanism is consistent with the observed radiation pattern from the source.

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2014-04-02
2024-04-19
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References

  1. AkiK. and RichardsP.G.1980. Quantitative seismology. W. H.Freeman .
    [Google Scholar]
  2. ArtmanB., PodladtchikovI. and WittenB.2010. Source location using time‐reverse imaging. Geophysical Prospecting58(5) 861–873.
    [Google Scholar]
  3. BaigA. and UrbancicT.2010. Microseismic moment tensors: a path to understanding frac growth. The Leading Edge29(3) 320–324.
    [Google Scholar]
  4. BakerT.2005. Real‐time Earthquake Location Using Kirchhoff Reconstruction. Bulletin of the Seismological Society of America95(2) 699–707.
    [Google Scholar]
  5. BassisJ.N., FrickerH.A., ColemanR., BockY., BehrensJ., DarnellD.et al., 2007. Seismicity and deformation associated with ice‐shelf rift propagation. Journal of Glaciology53523–536.
    [Google Scholar]
  6. BlundaY. and ChambersK.2013. A generic proceedure for noise supression in microseismic data. Geoconvention, CSEG, Expanded Abstracts.
  7. BukchinB., ClévédéE. and MostinskiyA.2009. Uncertainty of moment tensor determination from surface wave analysis for shallow earthquakes. Journal of Seismology14(3) 601–614.
    [Google Scholar]
  8. ChambersK. and KendallJ.M.2008. A practical implementation of wave front construction for 3‐D isotropic media. Geophysical Journal International173(3) 1030–1038.
    [Google Scholar]
  9. ChambersK., KendallJ.M. and BarkvedO.2010a. Investigation of induced microseismicity at Valhall using the Life of Field Seismic array. The Leading Edge29(3) 290–295.
    [Google Scholar]
  10. ChambersK., KendallJ.M., Brandsberg‐DahlS. and RuedaJ.2010b. Testing the ability of surface arrays to monitor microseismic activity. Geophysical Prospecting58(5) 821–830.
    [Google Scholar]
  11. ChambersK., VelascoR. and WilsonS.A.2013. Visualising microseismic distributions using cumulative image volumes. Annual Meeting, SEG, Expanded Abstracts.
  12. ClarkeJ., ChambersK., VelascoR., DandoB. and WilsonS. A.2013. Surface array moment tensor microseismic imaging. Annual Meeting, EAGE, Expanded Abstracts.
  13. De NataleG.Z., FerraroA. and VirieuxJ.1995. Accurate fault mechansim determinations for 1984 earthquake swarm at Campi Flegrei caldera (Italy) during an unrest episode: Implications for volcanological research. Journal of Geophysical Research100(B12) 24167–24185.
    [Google Scholar]
  14. DuncanP.M. and EisnerL.2010. Reservoir characterization using surface microseismic monitoring. Geophysics75(5) A139–A146.
    [Google Scholar]
  15. DyerB.C., JonesR.H., CowlesJ.E., BarkvedO. and FolstadP.G.1999. Microseismic survey of a North Sea reservoir. World Oil22074–79.
    [Google Scholar]
  16. EisnerL., HulseyB.J., DuncanP., JurickD., WernerH. and KellerW.2010. Comparison of surface and borehole locations of induced seismicity. Geophysical Prospecting58(5) 809–820.
    [Google Scholar]
  17. Forghani‐AraniF., WillisM., HainesS., BatzleM.BehuraJ., and DavidsonM. (2013). “An effective noise‐suppression technique for surface microseismic data.” Geophysics78(6), KS85–KS95.
    [Google Scholar]
  18. GajewskiD. and TessmerE.2005. Reverse modelling for seismic event characterization. Geophysical Journal International163(1) 276–284.
    [Google Scholar]
  19. GrigoliF., CescaS., VassalloM. and DahmT.2013. Automated Seismic Event Location by Travel‐Time Stacking: An Application to Mining Induced Seismicity. Seismological Research Letters84(4) 666–677.
    [Google Scholar]
  20. HenryC., WoodhouseJ.H. and DasS.2002. Stability of earthquake moment tensor inversions: effect of the double‐couple constriant. Tectonophysics356115–124.
    [Google Scholar]
  21. IshiiM., ShearerP.M., HoustonH. and VidaleJ.E.2005. Extent, duration and speed of the 2004 Sumatra‐Andaman earthquake imaged by the Hi‐Net array. Nature435(7044) 933–6.
    [Google Scholar]
  22. JonesG.A., KulessaB., DoyleS.H., DowC.F. and HubbardA.2012. An automated approach to the location of icequakes using seismic waveform amplitudes. Annals of Glaciology54(64) 9.
    [Google Scholar]
  23. JonesG.A., RaymerD., ChambersK. and KendallJ.M.2010. Improved microseismic event location by inclusion of a priori dip particle motion: a case study from Ekofisk. Geophysical Prospecting11.
    [Google Scholar]
  24. KaoH. and ShanS.‐J.2004. The Source‐Scanning Algorithm: mapping the distribution of seismic sources in time and space. Geophysical Journal International157(2) 589–594.
    [Google Scholar]
  25. KawakatsuH. and MontagnerJ.P.2008. Time‐reversal seismic‐source imaging and moment‐tensor inversion. Geophysical Journal International175(2) 686–688.
    [Google Scholar]
  26. KhadhraouiB., LeslieD., DrewJ. and JonesR.2010. Real‐time detection and localization of microseismic events. Annual Meeting, SEG, Expanded Abstracts, 2146–2150.
  27. KimY., LiuQ. and TrompJ.2011. Adjoint centroid‐moment tensor inversions. Geophysical Journal International186(1) 264–278.
    [Google Scholar]
  28. KochnevV.A., GozI.V., PolyakovV.S., MurtayevI.S., SavinV.G., ZommerB.K. and BryksinI.V.2007. Imaging hydraulic fracture zones from surface passive microseismic data. First Break2577–80.
    [Google Scholar]
  29. KushnirA., DrickerI., RozhkovM., VarypaevM., RojkovN., EpiphanskyA.et al., 2013. Optimization of Statistically Optimal (SO) algorithms for surface location of microseismic sources with complex focal mechanisms, 4th Passive Seismic Workshop, EAGE.
  30. McMechanG.A.1982. Determination of source parameters by wavefield extrapolation. Geophysical Journal of the Royal Astronomical Society71613–628.
    [Google Scholar]
  31. McMechanG.A.1983. Migration by extrapolation of the time‐dependent boundary values. Geophysical Prospecting31413–420.
    [Google Scholar]
  32. McMechanG.A., LuetgertJ.H. and MooneyW.D.1983. Imaging of Earthquake Sources in Long Valley caldera, California. Bulletin of the Seismological Society of America75(4) 15.
    [Google Scholar]
  33. Nath‐GhartiH., OyeV., KuhnD. and ZhaoP, 2011. Simultaneous microearthquake location and moment‐tensor estimation using time‐reversal imaging. Annual Meeting, SEG, Expanded Abstracts.
  34. NeidellN.S. and TanerM.T.1971. Semblance and other coherency measures for multichannel data. Geophysics36(3) 482–497.
    [Google Scholar]
  35. OzbekA., ProbertT., RaymerD. and DrewJ.2013. Nonlinear processing methods for detection and location of microseismic events. Annual Meeting, SEG, Expanded Abstracts.
  36. RawlinsonN. and SambridgeM.2004. Wave front evolution in strongly heterogeneous layered media using the fast marching method. Geophysical Journal International156(3) 631–647.
    [Google Scholar]
  37. RebelE., RichardA., MeunierJ. and AugerE.2011. Real‐Time Detection of Microseismic Events using Surface Array, Third Passive Seismic Workshop – Actively Passive!, Athens, Greece.
  38. RentschS., BuskeS., LüthS. and ShapiroA.2007. Fast location of seismicity: A migration‐type approach with application to hydraulic‐fracturing data. Geophysics72(1) 8.
    [Google Scholar]
  39. RodriguezI.V., SacchiM. and GuY.J.2012. Simultaneous recovery of origin time, hypocentre location and seismic moment tensor using sparse representation theory. Geophysical Journal International188(3) 1188–1202.
    [Google Scholar]
  40. RutledgeJ.T. and PhillipsW.S.2003. Hydraulic stimulation of natural fractures as revealed by induced microearthquakes, Carthage Cotton Valley gas field, east Texas. Geophysics68(2) 441–452.
    [Google Scholar]
  41. SaengerE., SteinerB. and SchmalholzS., 2010. Time reverse reservoir localization, Patent no. US7675815B2.
  42. SchimmelM. and PaulssenH.1997. Noise reduction and detection of weak, coherent signals through phase‐weighted stacks. Geophysical Journal International130497–505.
    [Google Scholar]
  43. ShearerP.1999. Introduction to seismology. Cambridge.
    [Google Scholar]
  44. ŠílenýJ., HillD.P., EisnerL. and CornetF.H., 2009. Non–double‐couple mechanisms of microearthquakes induced by hydraulic fracturing. Journal of Geophysical Research114(B8).
    [Google Scholar]
  45. TaisneB., BrenguierF., ShapiroN.M. and FerrazziniV., 2011. Imaging the dynamics of magma propagation using radiated seismic intensity. Geophysical Research Letters38.
    [Google Scholar]
  46. WarpinskiN., 2009. Microseismic monitoring: Inside and out. Journal of Petroleum Technology61(11)80–85.
    [Google Scholar]
  47. XuanR. and SavaP.2010. Probabilistic microearthquake location for reservoir monitoring. Geophysics75(3) MA9‐MA26.
    [Google Scholar]
  48. ZhaoL., ChenP. and JordanT.H.2006. Strain Green's Tensors, Reciprocity, and Their Applications to Seismic Source and Structure Studies. Bulletin of the Seismological Society of America96(5) 1753–1763.
    [Google Scholar]
  49. ZhebelO. and EisnerL.2012. Simultanous microseismic event localization and source mechanism determination. Annual Meeting, SEG, Expanded Abstracts.
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