Advertisement

Enhanced performance of ISC focal mechanism computations as a result of automatic first-motion polarity picking optimization

  • K. LentasEmail author
  • J. Harris
Original Article
  • 57 Downloads

Abstract

The International Seismological Centre (ISC) routinely calculates and makes available automatic earthquake focal mechanisms by combining reported parametric data (first-motion polarities) available in the reviewed ISC Bulletin and auto-picked first-motion polarities obtained from waveform data using a broadband automatic picker. In order to further enhance the robustness of the auto-picked polarities, we set up an optimization strategy which is carried out using the neighbourhood algorithm on a 24-processor mini computer cluster. The aim is to minimize an objective misfit function which takes into account the data uncertainties and compares the first P-wave arrival times and polarities of a large dataset of nearly 18,000 manual picks and the associated auto-picked waveform phase arrivals. The optimization yielded an overall increase of matching auto-picks from 15 to 30% in comparison with the default setup of the automatic picker. We then applied the optimized automatic picker to a set of earthquakes from the reviewed ISC Bulletin where we could not obtain well-constrained mechanism solutions using its default setup. As a result of using the optimized picker, we obtained well-trusted mechanism solutions for 28% of these cases by increasing the number of first motion auto-picked polarities, and hence minimizing the station azimuthal gap in some cases, and/or correcting some of the erroneous auto-picked polarities where possible.

Keywords

Body waves Computational seismology Earthquake source observations Optimization strategy 

Notes

Acknowledgements

The authors wish to thank the editor Prof. Anastasia Kiratzi and two anonymous reviewers for their comments and suggestions which helped improve this manuscript. We gratefully acknowledge the availability of global seismograms from the IRIS and Orfeus data centres, as well as the availability of the FilterPicker source code from ALomax Scientific. This study makes use of the computer package neighbourhood algorithm which was made available with support from the Inversion Laboratory (ilab). ilab is a program for construction and distribution of data inference software in the geosciences supported by AuScope Ltd., a non-profit organization for Earth Science infrastructure funded by the Australian Federal Government. The figures in this study have been produced using the Generic Mapping Tools (GMT, Wessel et al. 2013) and the Matplotlib python library (Hunter 2007).

Funding information

The authors acknowledge financial support from 67 member institutions and a National Science Foundation Award (NSF, EAR:1811737).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10950_2019_9862_MOESM1_ESM.tex (35 kb)
(TEX 34.8 KB)

References

  1. Allen RV (1978) Automatic earthquake recognition and timing from single traces. Bull Seismol Soc Am 68(5):1521Google Scholar
  2. Baer M, Kradolfer U (1987) An automatic phase picker for local and teleseismic events. Bull Seismol Soc Am 77(4):1437Google Scholar
  3. Baillard C, Crawford WC, Ballu V, Hibert C, Mangeney A (2013) An automatic kurtosis-based p- and s-phase picker designed for local seismic networks. Bull Seismol Soc Am 104(1):394–409.  https://doi.org/10.1785/0120120347 CrossRefGoogle Scholar
  4. Bassin C, Laske G, Masters TG (2000) The current limits of resolution for surface wave tomography in north america. EOS Trans AGU, 81Google Scholar
  5. Benz HM, Herrmann RB (2014) Rapid estimates of the source time function and mw using empirical green’s function deconvolutionrapid estimates of the source time function and mw using egf deconvolution. Bull Seismol Soc Am 104(4):1812.  https://doi.org/10.1785/0120130325 CrossRefGoogle Scholar
  6. Bondár I, Storchak DA (2011) Improved location procedures at the international seismological centre. Geophys J Int 186 (3):1220–1244.  https://doi.org/10.1111/j.1365-246x.2011.05107.x CrossRefGoogle Scholar
  7. Brillinger DR, Udias A, Bolt BA (1980) A probability model for regional focal mechanism solutions. Bull Seismol Soc Am 70(1):149. https://doi.org/gsw/content_public/journal/bssa/70/1/0037110670010007/3/bssa0700010149.pdf Google Scholar
  8. Dai H, MacBeth C (1995) Automatic picking of seismic arrivals in local earthquake data using an artificial neural network. Geophys J Int 120(3):758–774.  https://doi.org/10.1111/j.1365-246x.1995.tb01851.x CrossRefGoogle Scholar
  9. DeMets C, Gordon RG, Argus DF (2010) Geologically current plate motions. Geophys J Int 181(1):1–80.  https://doi.org/10.1111/j.1365-246x.2009.04491.x CrossRefGoogle Scholar
  10. Diehl T, Kissling E, Husen S, Aldersons F (2009) Consistent phase picking for regional tomography models: application to the greater alpine region. Geophys J Int 176(2):542–554.  https://doi.org/10.1111/j.1365-246x.2008.03985.x CrossRefGoogle Scholar
  11. Dziewonski AM, Chou TA, Woodhouse JH (1981) Determination of earthquake source parameters from waveform data for studies of global and regional seismicity. J Geophys Res: Solid Earth 86(B4):2825–2852.  https://doi.org/10.1029/jb086ib04p02825 CrossRefGoogle Scholar
  12. Earle PS, Shearer PM (1994) Characterization of global seismograms using an automatic-picking algorithm, vol 84Google Scholar
  13. Ekström G, Nettles M, Dziewoński A (2012) The global CMT project 2004–2010: centroid-moment tensors for 13, 017 earthquakes. Phys Earth Planet Inter 200-201:1–9.  https://doi.org/10.1016/j.pepi.2012.04.002 CrossRefGoogle Scholar
  14. Gentili S, Michelini A (2006) Automatic picking of p and s phases using a neural tree. J Seismol 10(1):39–63.  https://doi.org/10.1007/s10950-006-2296-6 CrossRefGoogle Scholar
  15. Gualtieri L, Serretti P, Morelli A (2014) Finite-differencePwave travel time seismic tomography of the crust and uppermost mantle in the italian region. Geochem Geophys Geosyst 15(1):69–88.  https://doi.org/10.1002/2013gc004988 CrossRefGoogle Scholar
  16. Hansen PC (1992) Analysis of discrete ill-posed problems by means of the l-curve. SIAM Rev 34(4):561–580.  https://doi.org/10.1137/1034115 CrossRefGoogle Scholar
  17. Hardebeck JL, Shearer PM (2002) A new method for determining first-motion focal mechanisms. Bull Seismol Soc Am 92(6):2264–2276.  https://doi.org/10.1785/0120010200 CrossRefGoogle Scholar
  18. Hayes GP, Rivera L, Kanamori H (2009) Source inversion of the W-Phase: real-time implementation and extension to low magnitudes. Seismol Res Lett 80(5):817–822.  https://doi.org/10.1785/gssrl.80.5.817 CrossRefGoogle Scholar
  19. Hayes GP, Moore GL, Portner DE, Hearne M, Flamme H, Furtney M, Smoczyk GM (2018) Slab2, a comprehensive subduction zone geometry model. Science 362(6410):58–61.  https://doi.org/10.1126/science.aat4723 CrossRefGoogle Scholar
  20. Hunter JD (2007) Matplotlib: a 2d graphics environment. Comput Sci Eng 9(3):90–95CrossRefGoogle Scholar
  21. International Seismological Centre (2018) On-line bulletin. Internatl. Seismol Cent.. Thatcham. http://www.isc.ac.uk
  22. Kagan YY (1991) 3-d rotation of double-couple earthquake sources. Geophys J Int 106(3):709–716.  https://doi.org/10.1111/j.1365-246x.1991.tb06343.x CrossRefGoogle Scholar
  23. Kanamori H (2005) Real-time seismology and earthquake damage mitigation. Annu Rev Earth Planet Sci 33(1):195–214.  https://doi.org/10.1146/annurev.earth.33.092203.122626 CrossRefGoogle Scholar
  24. Kennett BLN, Engdahl ER, Buland R (1995) Constraints on seismic velocities in the earth from traveltimes. Geophys J Int 122(1):108–124.  https://doi.org/10.1111/j.1365-246x.1995.tb03540.x CrossRefGoogle Scholar
  25. Kilb D (2006) Fault parameter constraints using relocated earthquakes: a validation of first-motion focal-mechanism data. Bull Seismol Soc Am 96(3):1140–1158.  https://doi.org/10.1785/0120040239 CrossRefGoogle Scholar
  26. Kilb D, Gomberg J, Bodin P (2000) Triggering of earthquake aftershocks by dynamic stresses. Nature 408(6812):570–574.  https://doi.org/10.1038/35046046 CrossRefGoogle Scholar
  27. Lentas K (2017) Towards routine determination of focal mechanisms obtained from first motion p-wave arrivals. Geophys J Int 212(3):1665–1686.  https://doi.org/10.1093/gji/ggx503 CrossRefGoogle Scholar
  28. Lentas K, Ferreira A, Clévédé E, Roch J (2014) Source models of great earthquakes from ultra low-frequency normal mode data. Phys Earth Planet In 233:41–67.  https://doi.org/10.1016/j.pepi.2014.05.011 CrossRefGoogle Scholar
  29. Lentas K, Giacomo DD, Harris J, Storchak DA (2019) The ISC bulletin as a comprehensive source of earthquake source mechanisms. Earth Syst Sci Data 11(2):565–578.  https://doi.org/10.5194/essd-11-565-2019 CrossRefGoogle Scholar
  30. Leonard M (2000) Comparison of manual and automatic onset time picking. Bull Seismol Soc Am 90(6):1384–1390.  https://doi.org/10.1785/0120000026 CrossRefGoogle Scholar
  31. Leonard M, Kennett B (1999) Multi-component autoregressive techniques for the analysis of seismograms. Phys Earth Planet In 113(1–4):247–263.  https://doi.org/10.1016/s0031-9201(99)00054-0 CrossRefGoogle Scholar
  32. Lomax A, Virieux J, Volant P, Berge-Thierry C (2000) Probabilistic earthquake location in 3d and layered models. In: Advances in seismic event location. Springer, Netherlands, pp 101–134, DOI  https://doi.org/10.1007/978-94-015-9536-0_5
  33. Lomax A, Satriano C, Vassallo M (2012) Automatic picker developments and optimization: filterPicker–a robust, broadband picker for real-time seismic monitoring and earthquake early warning. Seismol Res Lett 83(3):531–540.  https://doi.org/10.1785/gssrl.83.3.531 CrossRefGoogle Scholar
  34. Marson-Pidgeon K (2000) Source depth and mechanism inversion at teleseismic distances using a neighborhood algorithm. Bull Seismol Soc Am 90(6):1369–1383.  https://doi.org/10.1785/0120000020 CrossRefGoogle Scholar
  35. Mooney WD, Laske G, Masters TG (1998) CRUST 5.1: a global crustal model at 5 x 5. J Geophys Res: Solid Earth 103(B1):727–747.  https://doi.org/10.1029/97jb02122 CrossRefGoogle Scholar
  36. Nippress SEJ, Rietbrock A, Heath AE (2010) Optimized automatic pickers: application to the ANCORP data set. Geophys J Int.  https://doi.org/10.1111/j.1365-246x.2010.04531.x
  37. Oth A, Böse M, Wenzel F, Köhler N, Erdik M (2010) Evaluation and optimization of seismic networks and algorithms for earthquake early warning – the case of istanbul (turkey). J Geophys Res 115:B10.  https://doi.org/10.1029/2010jb007447 CrossRefGoogle Scholar
  38. Pugh D, White R, Christie P (2016a) Automatic bayesian polarity determination. Geophys J Int 206(1):275–291.  https://doi.org/10.1093/gji/ggw146 CrossRefGoogle Scholar
  39. Pugh DJ, White RS, Christie PAF (2016b) A bayesian method for microseismic source inversion. Geophys J Int 206(2):1009–1038.  https://doi.org/10.1093/gji/ggw186 CrossRefGoogle Scholar
  40. Ross ZE, Meier MA, Hauksson E (2018) P wave arrival picking and first-motion polarity determination with deep learning. J Geophys Res: Solid Earth 123(6):5120–5129.  https://doi.org/10.1029/2017jb015251 CrossRefGoogle Scholar
  41. Sambridge M (1999) Geophysical inversion with a neighbourhood algorithm-i. Searching a parameter space. Geophys J Int 138(2):479–494.  https://doi.org/10.1046/j.1365-246x.1999.00876.x CrossRefGoogle Scholar
  42. Sambridge M, Kennett B (2001) Seismic event location: nonlinear inversion using a neighbourhood algorithm. Pure Appl Geophys 158(1):241–257.  https://doi.org/10.1007/pl00001158 CrossRefGoogle Scholar
  43. Saragiotis C, Hadjileontiadis L, Panas S (2002) PAI-s/k: a robust automatic seismic p phase arrival identification scheme. IEEE Trans Geosci Remote Sens 40(6):1395–1404.  https://doi.org/10.1109/tgrs.2002.800438 CrossRefGoogle Scholar
  44. Satriano C, Lomax A, Zollo A (2008) Real-time evolutionary earthquake location for seismic early warning. Bull Seismol Soc Am 98(3):1482–1494.  https://doi.org/10.1785/0120060159 CrossRefGoogle Scholar
  45. Satriano C, Elia L, Martino C, Lancieri M, Zollo A, Iannaccone G (2011) PRESTo, the earthquake early warning system for Southern Italy: concepts, capabilities and future perspectives. Soil Dyn Earthq Eng 31(2):137–153.  https://doi.org/10.1016/j.soildyn.2010.06.008 CrossRefGoogle Scholar
  46. Schweitzer J (2001) HYPOSAT – an enhanced routine to locate seismic events. Pure Appl Geophys 158(1):277–289.  https://doi.org/10.1007/pl00001160 CrossRefGoogle Scholar
  47. Stefano RD, Aldersons F, Kissling E, Baccheschi P, Chiarabba C, Giardini D (2006) Automatic seismic phase picking and consistent observation error assessment: application to the italian seismicity. Geophys J Int 165(1):121–134.  https://doi.org/10.1111/j.1365-246x.2005.02799.x CrossRefGoogle Scholar
  48. Sun W, Kennett BLN (2016) Uppermost mantle structure of the australian continent fromPntraveltime tomography. J Geophys Res: Solid Earth 121(3):2004–2019.  https://doi.org/10.1002/2015jb012597 CrossRefGoogle Scholar
  49. Tarantola A (2004) Inverse problem theory and methods for model parameter estimation. SIAM: Society for Industrial and Applied MathematicsGoogle Scholar
  50. Tikhonov A (2014) Nonlinear ill-posed problems (applied mathematical sciences). SpringerGoogle Scholar
  51. Vallée M, Charléty J, Ferreira AMG, Delouis B, Vergoz J (2010) SCARDEC: a new technique for the rapid determination of seismic moment magnitude, focal mechanism and source time functions for large earthquakes using body-wave deconvolution. Geophys J Int 184(1):338–358.  https://doi.org/10.1111/j.1365-246x.2010.04836.x CrossRefGoogle Scholar
  52. Vassallo M, Satriano C, Lomax A (2012) Automatic picker developments and optimization: a strategy for improving the performances of automatic phase pickers. Seismol Res Lett 83(3):541–554.  https://doi.org/10.1785/gssrl.83.3.541 CrossRefGoogle Scholar
  53. Walsh D, Arnold R, Townend J (2009) A bayesian approach to determining and parametrizing earthquake focal mechanisms. Geophys J Int 176(1):235–255.  https://doi.org/10.1111/j.1365-246x.2008.03979.x CrossRefGoogle Scholar
  54. Wang Z, Zhao B (2017) Automatic event detection and picking of p, s seismic phases for earthquake early warning and application for the 2008 Wenchuan earthquake. Soil Dyn Earthq Eng 97:172–181.  https://doi.org/10.1016/j.soildyn.2017.03.017 CrossRefGoogle Scholar
  55. Wassermann J, Ohrnberger M (2001) Automatic hypocenter determination of volcano induced seismic transients based on wavefield coherence — an application to the 1998 eruption of Mt. Merapi, Indonesia. J Volcanol Geotherm Res 110(1-2):57–77.  https://doi.org/10.1016/s0377-0273(01)00200-1 CrossRefGoogle Scholar
  56. Wessel P, Smith WHF, Scharroo R, Luis J, Wobbe F (2013) Generic mapping tools: improved version released. Eos, Trans Am Geophys Union 94(45):409–410.  https://doi.org/10.1002/2013eo450001 CrossRefGoogle Scholar
  57. Withers M, Aster R, Young C, Beiriger J, Harris M, Moore S, Trujillo J (1998) A comparison of select trigger algorithms for automated global seismic phase and event detection. Bull Seismol Soc Am 88(1):95. https://doi.org/gsw/content_public/journal/bssa/88/1/0037110688010008/3/bssa0880010095.pdf Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.International Seismological CentreThatchamUK

Personalised recommendations