1887
Volume 16, Issue 5
  • ISSN: 1569-4445
  • E-ISSN: 1873-0604

Abstract

ABSTRACT

Accurate picking of first‐arrival times is important in many seismic studies, particularly in seismic tomography and reservoirs or aquifers monitoring. Many techniques have been developed, mainly for seismological purposes, in order to pick first arrivals automatically or semi‐automatically. However, these techniques do not reach the accuracy required in shallow seismics due to the complexity of near‐surface structures and low signal‐to‐noise ratio. We propose here a new adaptive algorithm to automatically pick first arrival in near‐surface seismic data by combining three picking methods: multi‐nested windows, higher order statistics, and Akaike information criterion. They benefit from combining different properties of the signal in order to highlight first arrivals and finally to provide an efficient and robust automatic picking. This strategy mimics the human first‐break picking, where a global trend is first defined at the beginning of the picking procedure. The exact first breaks are then sought in the vicinity of each point suggested by this trend. Three successive phases are combined in a multistage algorithm, each of them characterizing a specific signal property. Within each phase, the potential picks and their error range are automatically assessed and sequentially used as prior constraints in the following phase picking. Since having realistic estimates of the error in picked traveltimes is crucial for seismic tomography, our adaptive algorithm automatically provides picked arrival times with their associated uncertainties. We demonstrate the accuracy and robustness of the implemented algorithm using synthetic, pseudo‐synthetic and real datasets that pose challenges to classical automatic pickers. A comparison of both manual and adaptive picking procedures demonstrates that our new scheme provides more reliable results even under different noisy conditions. All parameters of our multi‐method algorithm are self‐adaptive, thanks to the sequential integration of each sub‐algorithm results in the workflow. Hence, it is nearly a parameter‐free algorithm, which is straightforward to implement and demands low computational resources.

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2018-08-16
2020-04-05
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References

  1. Ait LaasriE.H., AkhouayriE.S., AglizD. and AtmaniA.2014. Automatic detection and picking of P‐wave arrival in locally stationary noise using cross‐correlation. Digital Signal Processing: A Review Journal26, 87–100.
    [Google Scholar]
  2. AkaikeH.1974. Markovian representation of stochastic processes and its application to the analysis of autoregressive moving average processes. Annals of the Institute of Statistical Mathematics26, 363–387.
    [Google Scholar]
  3. AkiK. and RichardsP.G.1980. Quantitative Seismology. W.H. Freeman and Company.
    [Google Scholar]
  4. AkramJ. and EatonD.2012. Adaptive microseismic event detection and automatic time picking. GeoConvention, pp. 1–5.
  5. AkramJ. and EatonD.W.2016a. A review and appraisal of arrival‐time picking methods for downhole microseismic data. Geophysics81, KS71–KS91.
    [Google Scholar]
  6. AkramJ. and EatonD.W.2016b. Refinement of arrival‐time picks using a cross‐correlation based workflow. Journal of Applied Geophysics135, 55–66.
    [Google Scholar]
  7. AldersonsF.2004. Toward a three‐dimensional crustal structure of the Dead Sea region from local earthquake tomography. PhD thesis, Tel‐Aviv University, Israel.
  8. AllamA.A., Ben‐ZionY. and PengZ.2014. Seismic imaging of a bimaterial interface along the Hayward Fault, CA, with fault zone head waves and direct P arrivals. Pure and Applied Geophysics171, 2993–3011.
    [Google Scholar]
  9. AllenR.1982. Automatic phase pickers: their present use and future prospects. Bulletin of the Seismological Society of America72, S225–S242.
    [Google Scholar]
  10. AllenR.1978. Automatic earthquake recognition and timing from single traces. Bulletin of the Seismological Society of America68, 1521–1532.
    [Google Scholar]
  11. AnantK.S. and DowlaF.U.1997. Wavelet transform methods for phase identification in three‐component seismograms. Bulletin of the Seismological Society of America87, 1598–1612.
    [Google Scholar]
  12. BaerM. and KradolferU.1987. An automatic phase picker for local and teleseismic events. Bulletin of the Seismological Society of America77, 1437–1445.
    [Google Scholar]
  13. BaillardC., CrawfordW.C., BalluV., HibertC. and MangeneyA.2014. An automatic kurtosis‐based P‐ and S‐phase picker designed for local seismic networks. Bulletin of the Seismological Society of America104, 394–409.
    [Google Scholar]
  14. BauerK., MoeckI., NordenB., SchulzeA., WeberM. and WirthH.2010. Tomographic P wave velocity and vertical velocity gradient structure across the geothermal site Groß Schönebeck (NE German Basin): relationship to lithology, salt tectonics, and thermal regime. Journal of Geophysical Research115, 1–22.
    [Google Scholar]
  15. BaziwE., NedilkoB. and Weir‐JonesI.2004. Microseismic event detection Kalman filter: derivation of the noise covariance matrix and automated first break determination for accurate source location estimation. Pure and Applied Geophysics161, 303–329.
    [Google Scholar]
  16. BillingsS.D., SambridgeM.S. and KennettB.L.N.1994. Errors in hypocenter location: picking, model, and magnitude dependence. Bulletin of the Seismological Society of America84, 1978–1990.
    [Google Scholar]
  17. BliasE.2012. Optimization approach to automatic first arrival picking for three‐component three‐dimensional vertical seismic profiling data. Geophysical Prospecting60, 1024–1029.
    [Google Scholar]
  18. BogiatzisP. and IshiiM.2015. Continuous wavelet decomposition algorithms for automatic detection of compressional‐ and shear‐wave arrival times. Bulletin of the Seismological Society of America105, 1628–1641.
    [Google Scholar]
  19. CarcioneJ.M., HermanG.C. and ten KroodeA.P.E.2002. Seismic modeling. Geophysics67, 1304–1325.
    [Google Scholar]
  20. ChenZ.2005. A multi‐window algorithm for automatic picking of microseismic events on 3‐C data. SEG Technical Program, Society of Exploration Geophysicists, Expanded Abstracts, 1288–1291.
  21. CichowiczA.1993. An automatic S‐phase picker. Bulletin of the Seismological Society of America83, 180–189.
    [Google Scholar]
  22. CohenJ.K. and StockwellJ.J.W.2012. CWP/SU: seismic Un* × release no. 43R1—an open source software package for seismic research and processing. Center for Wave Phenomena, Colorado School of Mines.
  23. CoppensF.1985. First arrival picking on common‐offset trace collections for automatic estimation of static corrections. Geophysical Prospecting33, 1212–1231.
    [Google Scholar]
  24. Di StefanoR., AldersonsF., KisslingE., BaccheschiP., ChiarabbaC. and GiardiniD.2006. Automatic seismic phase picking and consistent observation error assessment: application to the Italian seismicity. Geophysical Journal International165, 121–134.
    [Google Scholar]
  25. DiehlT., DeichmannN., KisslingE. and HusenS.2009. Automatic S‐wave picker for local earthquake tomography. Bulletin of the Seismological Society of America99, 1906–1920.
    [Google Scholar]
  26. DouglasA.1997. Bandpass filtering to reduce noise on seismograms: is there a better way?Bulletin of the Seismological Society of America87, 770–777.
    [Google Scholar]
  27. EarleP.S. and ShearerP.M.1994. Characterization of global seismograms using an automatic‐picking algorithm. Bulletin of the Seismological Society of America84, 366–376.
    [Google Scholar]
  28. GaciS.2014. The use of wavelet‐based denoising techniques to enhance the first‐arrival picking on seismic traces. IEEE Transactions on Geoscience and Remote Sensing52, 4558–4563.
    [Google Scholar]
  29. GelchinskyB. and ShtivelmanV.1983. Automatic picking of first arrivals and parameterization of traveltime curves*. Geophysical Prospecting31, 915–928.
    [Google Scholar]
  30. GeoltrainS. and BracJ.1993. Can we image complex structures with first‐arrival traveltime?Geophysics58, 564–575.
    [Google Scholar]
  31. GiannakisG.B. and TsatsanisM.K.1994. Time‐domain tests for Gaussianity and time‐reversibility. IEEE Transactions on Signal Processing42, 3460–3472.
    [Google Scholar]
  32. HafezA.G., KhanT.A. and KohdaT.2009. Earthquake onset detection using spectro‐ratio on multi‐threshold time‐frequency sub‐band. Digital Signal Processing: A Review Journal19, 118–126.
    [Google Scholar]
  33. HanL., WongJ. and BancroftJ.2009. Time picking and random noise reduction on microseismic data. CREWES Research Report.
  34. HatherlyP.J.1982. A computer method for determining seismic first arrival times. Geophysics47, 1431–1436.
    [Google Scholar]
  35. IqbalN., Al‐ShuhailA.A., KakaS.L.I., LiuE., RajA.G. and McClellanJ.H.2017. Iterative interferometry‐based method for picking microseismic events. Journal of Applied Geophysics140, 52–61.
    [Google Scholar]
  36. JiaoL. and MoonW.M.2000. Detection of seismic refraction signals using a variance fractal dimension technique. Geophysics65, 286–292.
    [Google Scholar]
  37. KhalafA.2016. Développement d'une nouvelle technique de pointé automatique pour les données de sismique réfraction. Université Pierre et Marie Curie.
    [Google Scholar]
  38. KüperkochL., MeierT., BrüstleA., LeeJ. and FriederichW.2012. Automated determination of S‐phase arrival times using autoregressive prediction: application to local and regional distances. Geophysical Journal International188, 687–702.
    [Google Scholar]
  39. KüperkochL., MeierT., LeeJ., FriederichW. and Working GroupE.2010. Automated determination of P‐phase arrival times at regional and local distances using higher order statistics. Geophysical Journal International181, 1159–1170.
    [Google Scholar]
  40. LangetN., MaggiA., MicheliniA. and BrenguierF.2014. Continuous kurtosis‐based migration for seismic event detection and location, with application to Piton de la Fournaise Volcano, La Reunion. Bulletin of the Seismological Society of America104, 229–246.
    [Google Scholar]
  41. LanzE., MaurerH. and GreenA.1998. Refraction tomography over a buried waste disposal site. Geophysics63, 1414–1433.
    [Google Scholar]
  42. LawtonD.C.1989. Computation of refraction static corrections using first‐break traveltime differences. Geophysics54, 1289–1296.
    [Google Scholar]
  43. LeonardM.2000. Comparison of manual and automatic onset time picking. Bulletin of the Seismological Society of America90, 1384–1390.
    [Google Scholar]
  44. LeonardM. and KennettB.L.N.1999. Multi‐component autoregressive techniques for the analysis of seismograms. Physics of the Earth and Planetary Interiors113, 247–263.
    [Google Scholar]
  45. LiC., HuangL., DuricN., ZhangH. and RoweC.2009. An improved automatic time‐of‐flight picker for medical ultrasound tomography. Ultrasonics49, 61–72.
    [Google Scholar]
  46. LoisA., SokosE., MartakisN., ParaskevopoulosP. and TselentisG.‐A.2013. A new automatic S‐onset detection technique: application in local earthquake data. Geophysics78, KS1–KS11.
    [Google Scholar]
  47. LomaxA., SatrianoC. and VassalloM.2012. Automatic picker developments and optimization: filterpicker – a robust, broadband picker for real‐time seismic monitoring and earthquake early warning. Seismological Research Letters83, 531–540.
    [Google Scholar]
  48. LouX., van der LeeS. and LloydS.2013. AIMBAT: a Python/Matplotlib tool for measuring teleseismic arrival times. Seismological Research Letters84, 85–93.
    [Google Scholar]
  49. MaedaN.1985. A method for reading and checking phase times in auto‐processing system of seismic wave data. Zisin = Jishin38, 365–380.
    [Google Scholar]
  50. MallinsonI., BharadwajP., SchusterG. and JakubowiczH.2011. Enhanced refractor imaging by supervirtual interferometry. Leading Edge30, 546–550.
    [Google Scholar]
  51. MousaW.A. and Al‐ShuhailA.A.2012. Enhancement of first arrivals using the τ‐p transform on energy‐ratio seismic shot records. Geophysics77, V101–V111.
    [Google Scholar]
  52. MousaW.A., Al‐ShuhailA.A. and Al‐LehyaniA.2011. A new technique for first‐arrival picking of refracted seismic data based on digital image segmentation. Geophysics76, V79–V89.
    [Google Scholar]
  53. NippressS.E.J., RietbrockA. and HeathA.E.2010. Optimized automatic pickers: application to the ANCORP data set. Geophysical Journal International181, 911–925.
    [Google Scholar]
  54. ParolaiS.2009. Denoising of seismograms using the S transform. Bulletin of the Seismological Society of America99, 226–234.
    [Google Scholar]
  55. PavlisG.L. and VernonF.L.2010. Array processing of teleseismic body waves with the USArray. Computers & Geosciences36, 910–920.
    [Google Scholar]
  56. PeraldiR. and ClementA.1972. Digital processing of refraction data study of first arrivals*. Geophysical Prospecting20, 529–548.
    [Google Scholar]
  57. PerssonL.2003. Statistical tests for regional seismic phase characterizations. Journal of Seismology7, 19–33.
    [Google Scholar]
  58. PressW.H., TeukolskyS.A., VetterlingW.T. and FlanneryB.P.1992. Numerical Recipes in C : The Art of Scientific Computing. 2nd edn. Cambridge University Press.
    [Google Scholar]
  59. RamananantoandroR. and BernitsasN.1987. A computer algorithm for automatic picking of refraction first‐arrival time. Geoexploration24, 147–151.
    [Google Scholar]
  60. RickerN.1953. The form and laws of propagation of seismic wavelets. Geophysics18, 10–40.
    [Google Scholar]
  61. RossZ.E. and Ben‐ZionY.2014. Automatic picking of direct P, S seismic phases and fault zone head waves. Geophysical Journal International199, 368–381.
    [Google Scholar]
  62. SabbioneJ.I. and VelisD.2010. Automatic first‐breaks picking: new strategies and algorithms. Geophysics75, V67–V76.
    [Google Scholar]
  63. SaragiotisC.D., HadjileontiadisL.J. and PanasS.M.2002. PAI‐S/K: a robust automatic seismic P phase arrival identification scheme. IEEE Transactions on Geoscience and Remote Sensing40, 1395–1404.
    [Google Scholar]
  64. SaragiotisC.D., HadjileontiadisL.J., RekanosI.T. and PanasS.M.2004. Automatic P phase picking using maximum kurtosis and k‐statistics criteria. IEEE Geoscience and Remote Sensing Letters1, 147–151.
    [Google Scholar]
  65. SenkayaM. and KarslıH.2014. A semi‐automatic approach to identify first arrival time: the cross‐correlation technique. Earth Sciences Research Journal18, 107–113.
    [Google Scholar]
  66. SleemanR. and van EckT.1999. Robust automatic P‐phase picking: an on‐line implementation in the analysis of broadband seismogram recordings. Physics of the Earth and Planetary Interiors113, 265–275.
    [Google Scholar]
  67. StewartW.S.1977. Real‐time detection and location of local seismic events in central California. Bulletin of the Seismological Society of America67, 433–452.
    [Google Scholar]
  68. StockwellR., MansinhaL. and LoweR.P.1996. Localization of the complex spectrum: the S transform. IEEE Transactions on Signal Processing44, 998–1001.
    [Google Scholar]
  69. TakanamiT. and KitagawaG.1988. A new efficient procedure for the estimation of onset times of seismic waves. Journal of Physics of the Earth36, 267–290.
    [Google Scholar]
  70. ThorbeckeJ.W. and DraganovD.2011. Finite‐difference modeling experiments for seismic interferometry. Geophysics76, H1–H18.
    [Google Scholar]
  71. ToomeyD.R., SolomonS.C. and PurdyG.M.1994. Tomographic imaging of the shallow crustal structure of the east Pacific rise at 9°30′N. Journal of Geophysical Research99, 24135–24157.
    [Google Scholar]
  72. TrnkoczyA.2012. Understanding and parameter setting of STA / LTA trigger algorithm 1 Introduction. New Manual of Seismological Observatory Practice22, 1–20.
    [Google Scholar]
  73. TselentisG.‐A., MartakisN., ParaskevopoulosP., LoisA. and SokosE.2012. Strategy for automated analysis of passive microseismic data based on S‐transform, Otsu's thresholding, and higher order statistics. Geophysics77, KS43–KS54.
    [Google Scholar]
  74. VanDecarJ. and CrossonR.1990. Determination of teleseismic relative phase arrival times using multi‐channel cross‐correlation and least squares. Bulletin of the Seismological Society of America80, 150–169.
    [Google Scholar]
  75. WongJ., HanL., BancroftJ.C. and StewartR.2009. Automatic time‐picking of first arrivals on noisy microseismic data. CSEG, CREWES, pp. 1–6.
  76. YilmazÖ.2001. Seismic Data Analysis: Processing, Inversion, and Interpretation of Seismic Data (Investigations in Geophysics, No. 10), 2nd edn. Society of Exploration Geophysicists.
    [Google Scholar]
  77. ZeilerC. and VelascoA.A.2009. Seismogram picking error from analyst review (SPEAR): single‐analyst and institution analysis. Bulletin of the Seismological Society of America99, 2759–2770.
    [Google Scholar]
  78. ZeltC.A., AzariaA. and LevanderA.2006. 3D seismic refraction traveltime tomography at a groundwater contamination site. Geophysics71, H67–H78.
    [Google Scholar]
  79. ZeltC.A., DrewJ.J., YedlinM.J. and EllisR.M.1987. Picking noisy refraction data using semblance supplemented by a Monte Carlo procedure and spectral balancing. Bulletin of the Seismological Society of America77, 942–957.
    [Google Scholar]
  80. ZhangH., ThurberC. and RoweC.2003. Automatic P‐wave arrival detection and picking with multiscale wavelet analysis for single‐component recordings. Bulletin of the Seismological Society of America93, 1904–1912.
    [Google Scholar]
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