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Back-propagating supershear rupture in the 2016 Mw 7.1 Romanche transform fault earthquake


How an earthquake rupture propagates strongly influences the potentially destructive ground shaking. Complex ruptures often involve slip along multiple faults, which masks information on the frictional behaviour of fault zones. Geometrically smooth ocean transform fault plate boundaries offer a favourable environment to study fault dynamics, because strain is accommodated along a single, wide fault zone that offsets the homogeneous geology. Here we present an analysis of the 2016 Mw 7.1 earthquake on the Romanche fracture zone in the equatorial Atlantic, using data from both nearby seafloor seismometers and global seismic networks. We show that this rupture had two phases: (1) upward and eastward propagation towards a weaker region where the transform fault intersects the mid-ocean ridge, and then (2) an unusual back-propagation westwards at a supershear speed towards the centre of the fault. We suggest that deep rupture into weak fault segments facilitated greater seismic slip on shallow locked zones. This highlights that even earthquakes along a single distinct fault zone can be highly dynamic. Observations of back-propagating ruptures are sparse, and the possibility of reverse propagation is largely absent in rupture simulations and unaccounted for in hazard assessments.

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Fig. 1: Seismotectonic context.
Fig. 2: Multiple-point-source centroid moment tensor (CMT) inversion using OBS waveforms.
Fig. 3: Teleseismic analyses.
Fig. 4: Interpretation of rupture dynamics for the 2016 Romanche earthquake.

Data availability

The facilities of IRIS Data Services, and specifically the IRIS Data Management Center (IRIS-DMC), were used for access to waveforms, related metadata and/or derived products used in this study. IRIS Data Services are funded through the Seismological Facilities for the Advancement of Geoscience (SAGE) Award of the National Science Foundation under Cooperative Support Agreement EAR-1851048. Additional seismic data were obtained from the GEOFON data centre of the GFZ German Research Centre for Geosciences (GFZ-GEOFON), ORFEUS/EIDA, the INGV Seismological Data Centre, and the RESIF Data Center. RESIF is a national Research Infrastructure, recognized as such by the French Ministry of Higher Education and Research. RESIF is managed by the RESIF Consortium, composed of 18 research institutions and universities in France. RESIF is additionally supported by a public grant overseen by the French National Research Agency (ANR) as part of the Investissements d’Avenir programme (ANR-11-EQPX-0040) and the French Ministry of Ecology, Sustainable Development and Energy. Continuous raw seismic waveform data from the PI-LAB ocean bottom seismometer network53 can be downloaded from IRIS-DMC (network code XS). Continuous raw seismic waveform data from various global seismic networks used for the slip-rate inversion54,55,56,57,58,59,60,61, BP54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81 and Mach cone54,56,57,58,59,60,67,68,69,70,79,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103 analyses are available to download from the data centres named above. Source data are provided with this paper.

Code availability

The ISOLA software used for RMT inversion is available at (accessed June 2020). The Palantiri software used for teleseismic BP analysis is available at (accessed June 2020). We used the ObsPy package for seismic analysis104. Figures were made using GMT105 and matplotlib106.


  1. Zhang, H., Koper, K. D., Pankow, K. & Ge, Z. Imaging the 2016 M w 7.8 Kaikoura, New Zealand, earthquake with teleseismic P waves: a cascading rupture across multiple faults. Geophys. Res. Lett. 44, 4790–4798 (2017).

    Google Scholar 

  2. Meng, L. et al. Earthquake in a maze: compressional rupture branching during the 2012 M w 8.6 Sumatra earthquake. Science 337, 724–726 (2012).

    Google Scholar 

  3. Hicks, S. P. & Rietbrock, A. Seismic slip on an upper-plate normal fault during a large subduction megathrust rupture. Nat. Geosci. 8, 955–960 (2015).

    Google Scholar 

  4. Ross, Z. E. et al. Hierarchical interlocked orthogonal faulting in the 2019 Ridgecrest earthquake sequence. Science 366, 346–351 (2019).

    Google Scholar 

  5. Boettcher, M. & Jordan, T. Earthquake scaling relations for mid-ocean ridge transform faults. J. Geophys. Res. 109, B12302 (2004).

    Google Scholar 

  6. Kuna, V. M., Nábělek, J. L. & Braunmiller, J. Mode of slip and crust–mantle interaction at oceanic transform faults. Nat. Geosci. 12, 138–142 (2019).

    Google Scholar 

  7. Sykes, L. R. & Ekström, G. Earthquakes along Eltanin transform system, SE Pacific Ocean: fault segments characterized by strong and poor seismic coupling and implications for long-term earthquake prediction. Geophys. J. Int. 188, 421–434 (2012).

    Google Scholar 

  8. Froment, B. et al. Imaging along‐strike variations in mechanical properties of the Gofar transform fault, East Pacific Rise. J. Geophys. Res. 119, 7175–7194 (2014).

    Google Scholar 

  9. McGuire, J. J., Boettcher, M. S. & Jordan, T. H. Foreshock sequences and short-term earthquake predictability on East Pacific Rise transform faults. Nature 434, 457–461 (2005).

    Google Scholar 

  10. McGuire, J. Seismic cycles and earthquake predictability on East Pacific Rise Transform faults. Bull. Seismol. Soc. Am. 98, 1067–1084 (2008).

    Google Scholar 

  11. Roland, E. & McGuire, J. J. Earthquake swarms on transform faults. Geophys. J. Int. 178, 1677–1690 (2009).

    Google Scholar 

  12. Avendonk, V. H., Harding, A., Orcutt, J. & McClain, J. Contrast in crustal structure across the Clipperton transform fault from travel time tomography. J. Geophys. Res. 106, 10961–10981 (2001).

    Google Scholar 

  13. Roland, E., Lizarralde, D., McGuire, J. J. & Collins, J. A. Seismic velocity constraints on the material properties that control earthquake behavior at the Quebrada–Discovery–Gofar transform faults, East Pacific Rise. J. Geophys. Res. 117, B11102 (2012).

    Google Scholar 

  14. Schwehr, M., Boettcher, M. S., McGuire, J. J. & Collins, J. A. The relationship between seismicity and fault structure on the Discovery transform fault, East Pacific Rise. Geochem. Geophys. Geosyst. 15, 3698–3712 (2014).

    Google Scholar 

  15. Duputel, Z., Tsai, V. C., Rivera, L. & Kanamori, H. Using centroid time-delays to characterize source durations and identify earthquakes with unique characteristics. Earth. Planet. Sci. Lett. 374, 92–100 (2013).

    Google Scholar 

  16. Abercrombie, R. E. & Ekström, G. Earthquake slip on oceanic transform faults. Nature 410, 74–77 (2001).

    Google Scholar 

  17. Antolik, M., Abercrombie, R. E., Pan, J. & Ekström, G. Rupture characteristics of the 2003 M w 7.6 mid-Indian Ocean earthquake: Implications for seismic properties of young oceanic lithosphere. J. Geophys. Res. 111, B04302 (2006).

    Google Scholar 

  18. Aderhold, K. & Abercrombie, R. E. The 2015 M w 7.1 earthquake on the Charlie‐Gibbs transform fault: repeating earthquakes and multimodal slip on a slow oceanic transform. Geophys. Res. Lett. 43, 6119–6128 (2016).

    Google Scholar 

  19. Wang, D., Mori, J. & Koketsu, K. Fast rupture propagation for large strike-slip earthquakes. Earth. Planet. Sci. Lett. 440, 115–126 (2016).

    Google Scholar 

  20. Yue, H. et al. Supershear rupture of the 5 January 2013 Craig, Alaska (M w 7.5) earthquake. J. Geophys. Res. 118, 5903–5919 (2013).

    Google Scholar 

  21. Huang, Y., Ampuero, J.-P. & Helmberger, D. V. The potential for supershear earthquakes in damaged fault zones—theory and observations. Earth. Planet. Sci. Lett. 433, 109–115 (2016).

    Google Scholar 

  22. Bouchon, M. et al. Faulting characteristics of supershear earthquakes. Tectonophysics 493, 244–253 (2010).

    Google Scholar 

  23. Sokos, E. N. & Zahradnik, J. ISOLA a Fortran code and a Matlab GUI to perform multiple-point source inversion of seismic data. Comput. Geosci. 34, 967–977 (2007).

    Google Scholar 

  24. Vallée, M., Charléty, J., Ferreira, A., Delouis, B. & Vergoz, J. 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, 338–358 (2011).

    Google Scholar 

  25. Shimizu, K., Yagi, Y., Okuwaki, R. & Fukahata, Y. Development of an inversion method to extract information on fault geometry from teleseismic data. Geophys. J. Int. 220, 1055–1065 (2020).

    Google Scholar 

  26. Prieto, G. A., Froment, B., Yu, C., Poli, P. & Abercrombie, R. Earthquake rupture below the brittle–ductile transition in continental lithospheric mantle. Sci. Adv. 3, e1602642 (2017).

    Google Scholar 

  27. Krüger, F. & Ohrnberger, M. Tracking the rupture of the M w = 9.3 Sumatra earthquake over 1,150 km at teleseismic distance. Nature 435, 937–939 (2005).

    Google Scholar 

  28. Vallée, M. & Dunham, E. Observation of far-field Mach waves generated by the 2001 Kokoxili supershear earthquake. Geophys. Res. Lett. 39, L05311 (2012).

    Google Scholar 

  29. Bao, H. et al. Early and persistent supershear rupture of the 2018 magnitude 7.5 Palu earthquake. Nat. Geosci. 12, 200–205 (2019).

    Google Scholar 

  30. Ekström, G., Nettles, M. & Dziewoński, A. The global CMT project 2004–2010: centroid-moment tensors for 13,017 earthquakes. Phys. Earth. Planet. Int. 200–201, 1–9 (2012).

    Google Scholar 

  31. Bouchon, M. & Karabulut, H. The aftershock signature of supershear earthquakes. Science 320, 1323–1325 (2008).

    Google Scholar 

  32. Wang, D., Mori, J. & Uchide, T. Supershear rupture on multiple faults for the M w 8.6 off Northern Sumatra, Indonesia earthquake of April 11, 2012. Geophys. Res. Lett. 39, L21307 (2012).

    Google Scholar 

  33. Xia, K., Rosakis, A. J., Kanamori, H. & Rice, J. R. Laboratory earthquakes along inhomogeneous faults: directionality and supershear. Science 308, 681–684 (2005).

    Google Scholar 

  34. Bonatti, E. et al. Transform migration and vertical tectonics at the Romanche fracture zone, equatorial Atlantic. J. Geophys. Res. 99, 21779–21802 (1994).

    Google Scholar 

  35. Liu, Y., McGuire, J. J. & Behn, M. D. Frictional behavior of oceanic transform faults and its influence on earthquake characteristics. J. Geophys. Res. 117, B04315 (2012).

    Google Scholar 

  36. Michel, S., Avouac, J.-P., Lapusta, N. & Jiang, J. Pulse-like partial ruptures and high-frequency radiation at creeping-locked transition during megathrust earthquakes. Geophys. Res. Lett. 44, 8345–8351 (2017).

    Google Scholar 

  37. Meng, L., Ampuero, J. P., Page, M. T. & Hudnut, K. W. Seismological evidence and dynamic model of reverse rupture propagation during the 2010 M7.2 El Mayor Cucapah earthquake. AGU 2012 Fall Meeting abstr. S52B-04 (AGU, 2012).

  38. Beroza, G. C. & Spudich, P. Linearized inversion for fault rupture behavior: application to the 1984 Morgan Hill, California, earthquake. J. Geophys. Res 93, 6275–6296 (1988).

    Google Scholar 

  39. Yamashita, Y. et al. Migrating tremor off southern Kyushu as evidence for slow slip of a shallow subduction interface. Science 348, 676–679 (2015).

    Google Scholar 

  40. Idini, B., & Ampuero, J. P. Fault-zone damage promotes pulse-like rupture and rapid-tremor-reversals. Preprint at (2019).

  41. Laske, G., Masters, G., Ma, Z. & Pasyanos, M. Update on CRUST1.0—a 1-degree global model of Earth’s crust. Geophys. Res. Abstr. 15, 2658 (2013).

    Google Scholar 

  42. Lomax, A., Virieux, J., Volant, P. & Berge-Thierry, C. Probabilistic earthquake location in 3D and layered models. Adv. Seismic Event Locat. 18, 101–134 (2000).

    Google Scholar 

  43. Bouchon, M. A simple method to calculate Green’s functions for elastic layered media. Bull. Seismol. Soc. Am. 71, 959–971 (1981).

    Google Scholar 

  44. Bie, L., Hicks, S., Garth, T., Gonzalez, P. & Rietbrock, A. ‘Two go together’: near-simultaneous moment release of two asperities during the 2016 M w 6.6 Muji, China earthquake. Earth Planet. Sci. Lett. 491, 34–42 (2018).

    Google Scholar 

  45. Zahradník, J. et al. A recent deep earthquake doublet in light of long-term evolution of Nazca subduction. Sci. Rep. 7, 45153 (2017).

    Google Scholar 

  46. Zahradnik, J. & Sokos, E. N. The M w 7.1 Van, Eastern Turkey, earthquake 2011: two-point source modelling by iterative deconvolution and non-negative least squares. Geophys. J. Int. 196, 522–538 (2014).

    Google Scholar 

  47. Yagi, Y. & Fukahata, Y. Introduction of uncertainty of Green’s function into waveform inversion for seismic source processes. Geophys. J. Int. 186, 711–720 (2011).

    Google Scholar 

  48. Yabuki, T. & Matsu’ura, M. Geodetic data inversion using a Bayesian information criterion for spatial distribution of fault slip. Geophys. J. Int. 109, 363–375 (1992).

    Google Scholar 

  49. Fukuda, J. & Johnson, K. A fully Bayesian inversion for spatial distribution of fault slip with objective smoothing. Bull. Seismol. Soc. Am. 98, 1128–1146 (2008).

    Google Scholar 

  50. Roessler, D., Krueger, F., Ohrnberger, M. & Ehlert, L. Rapid characterisation of large earthquakes by multiple seismic broadband arrays. Nat. Hazards Earth Syst. Sci. 10, 923–932 (2010).

    Google Scholar 

  51. Schimmel, M. & Paulssen, H. Noise reduction and detection of weak, coherent signals through phase-weighted stacks. Geophys J. Int. 130, 497–505 (1997).

    Google Scholar 

  52. Ekström, G. A global model of Love and Rayleigh surface wave dispersion and anisotropy, 25–250 s. Geophys J. Int. 187, 1668–1686 (2011).

    Google Scholar 

  53. Rychert, C., Kendall, J. K. & Harmon, N. Passive Imaging of the Lithosphere–Asthenosphere Boundary (International Federation of Digital Seismograph Networks, 2016);

  54. Geological Survey of Canada Canadian National Seismograph Network (International Federation of Digital Seismograph Networks, 1989);

  55. Institute of Geophysics A. O. S. O. T. C. R. Czech Regional Seismic Network (International Federation of Digital Seismograph Networks, 1973);

  56. GEOSCOPE, French Global Network of Broad Band Seismic Stations (Institut de Physique du Globe de Paris and École et Observatoire des Sciences de la Terre de Strasbourg (EOST), 1982);

  57. GEOFON Data Centre GEOFON Seismic Network (GFZ, 1993);

  58. Albuquerque Seismological Laboratory (ASL)/USGS Global Telemetered Seismograph Network (USAF/USGS) (International Federation of Digital Seismograph Networks, 1993);

  59. Scripps Institution of Oceanography IRIS/IDA Seismic Network (International Federation of Digital Seismograph Networks, 1986);

  60. Albuquerque Seismological Laboratory (ASL)/USGS (Global Seismograph Network (GSN – IRIS/USGS) (International Federation of Digital Seismograph Networks, 1988);

  61. MedNet Project Partner Institutions Mediterranean Very Broadband Seismographic Network (MedNet) (Istituto Nazionale di Geofisica e Vulcanologia (INGV), 1990);

  62. Swiss Seismological Service (SED) at ETH Zurich National Seismic Networks of Switzerland (ETH Zürich, 1983);

  63. Instituto Geofisico Escuela Politecnica Nacional (IG-EPN Ecuador) Ecuador Seismic Network (International Federation of Digital Seismograph Networks, 2002);

  64. BGR SZO Data Center German Regional Seismic Network (GRSN) (Federal Institute for Geosciences and Natural Resources (BGR), 1976);

  65. Instituto Nacional de Sismologia, Vulcanologia, Meteorologia E Hidrologia (INSIVUMEH) Red Sismologica Nacional (International Federation of Digital Seismograph Networks, 2013);

  66. Kövesligethy Radó Seismological Observatory Hungarian National Seismological Network (GFZ, 1992);

  67. Lamont Doherty Earth Observatory (LDEO), Columbia University Lamont–Doherty Cooperative Seismographic Network (International Federation of Digital Seismograph Networks, 1970);

  68. Servicio Sismologico Nacional MX Seismic Network (Universidad Nacional Autónoma de México (UNAM), 2017);

  69. UC San Diego Central and Eastern US Network (International Federation of Digital Seismograph Networks, 2013);

  70. Saint Louis University Cooperative New Madrid Seismic Network (International Federation of Digital Seismograph Networks, 1980);

  71. Nicaraguan Seismic Network (Instituto Nicaraguense de Estudios Territoriales (INETER), 1975);

  72. ZAMG-Zentralanstalt für Meterologie und Geodynamik Austrian Seismic Network (International Federation of Digital Seismograph Networks, 1987);

  73. Protti, M. Observatorio Vulcanológico y Sismológico de Costa Rica (International Federation of Digital Seismograph Networks, 1984);

  74. OGS (Istituto Nazionale Di Oceanografia E Di Geofisica Sperimentale) North-East Italy Seismic Network (International Federation of Digital Seismograph Networks, 2016);

  75. Red Sismica Volcan Baru ChiriNet (International Federation of Digital Seismograph Networks, 2000);

  76. Penn State University Pennsylvania State Seismic Network (International Federation of Digital Seismograph Networks, 2004);

  77. Geological Survey of Canada Portable Observatories for Lithospheric Analysis and Research Investigating Seismicity (POLARIS) (International Federation of Digital Seismograph Networks, 2000).

  78. Servicio Nacional de Estudios Territoriales (SNET El Salvador) Servicio Nacional de Estudios Territoriales (SNET), El Salvador (SNET-BB) (International Federation of Digital Seismograph Networks, 2004);

  79. IRIS Transportable Array USArray Transportable Array (International Federation of Digital Seismograph Networks, 2003);

  80. Red Sismológica Nacional de Costa Rica (RSN: UCR-ICE) (Universidad de Costa Rica, 2016);

  81. University of Western Ontario (UWO Canada) The Southern Ontario Seismic Network (International Federation of Digital Seismograph Networks, 1991);

  82. Universidade de Sao Paulo (USP) Brazilian Lithospheric Seismic Project (BLSP) (International Federation of Digital Seismograph Networks, 1988);

  83. University of Brasilia University of Brasilia Seismic Network (International Federation of Digital Seismograph Networks, 1995);

  84. Universidad de Chile, Dept de Geofisica (DGF UChile Chile) Chilean National Seismic Network (International Federation of Digital Seismograph Networks, 1991);

  85. California Institute of Technology and United States Geological Survey Pasadena Southern California Seismic Network (International Federation of Digital Seismograph Networks, 1926);

  86. Servicio Geologico Colombiano Red Sismologica Nacional de Colombia (International Federation of Digital Seismograph Networks, 1993);

  87. Albuquerque Seismological Laboratory (ASL)/USGS Caribbean USGS Network (International Federation of Digital Seismograph Networks, 2006);

  88. Integrated Plate Boundary Observatory Chile (IPOC) IPOC Seismic Network (GFZ and Institut des Sciences de L’Univers-Centre National de La Recherche CNRS-INSU, 2006);

  89. GEUS Geological Survey of Denmark and Greenland Danish Seismological Network (International Federation of Digital Seismograph Networks, 1976);

  90. National Seismological Centre of Autonomous University of Santo Domingo CNS-UASD (International Federation of Digital Seismograph Networks, 1998);

  91. Instituto Geofisico Escuela Politecnica Nacional (IG-EPN Ecuador) Ecuador Seismic Network (International Federation of Digital Seismograph Networks, 2002);

  92. Geophysical Institute of Israel (GII Israel) Israel National Seismic Network (International Federation of Digital Seismograph Networks, 1982);

  93. INGV Seismological Data Centre Rete Sismica Nazionale (RSN) (Istituto Nazionale di Geofisica e Vulcanologia (INGV), 2006);

  94. Albuquerque Seismological Laboratory (ASL)/USGS Intermountain West Seismic Network (International Federation of Digital Seismograph Networks, 2003);

  95. Institute of Seismology, National Academy of Sciences of Kyrgyz Republic (KIS) Kyrgyz Digital Network (International Federation of Digital Seismograph Networks, 2007);

  96. KNMI Caribbean Netherlands Seismic Network (Royal Netherlands Meteorological Institute (KNMI), 2006);

  97. Utrecht University (UU Netherlands) NARS (International Federation of Digital Seismograph Networks, 1983);

  98. University of Puerto Rico Puerto Rico Seismic Network (PRSN) & Puerto Rico Strong Motion Program (PRSMP) (International Federation of Digital Seismograph Networks, 1986);

  99. New Mexico Tech New Mexico Tech Seismic Network (International Federation of Digital Seismograph Networks, 1999);

  100. Albuquerque Seismological Laboratory (ASL)/USGS United States National Seismic Network (International Federation of Digital Seismograph Networks, 1990);

  101. Fundación Venezolana de Investigaciones Sismológicas (FUNVISIS), Caracas Red Sismológica Satelital Nacional (International Federation of Digital Seismograph Networks, 2000);

  102. IPGP Data Center GNSS, Seismic Broadband and Strong Motion Permanent Networks in West Indies (Institut de Physique du Globe de Paris—IPGP, 2008);

  103. Dublin Institute for Advanced Studies and Geological Survey Ireland INSN, Irish National Seismic Network (International Federation of Digital Seismograph Networks, 1993);

  104. Beyreuther, M. et al. ObsPy: a python toolbox for seismology. Seismol. Res. Lett. 81, 530–533 (2010).

    Google Scholar 

  105. Wessel, P. et al. The generic mapping tools version 6. Geochem. Geophys. Geosyst. 20, 5556–5564 (2019).

    Google Scholar 

  106. Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).

    Google Scholar 

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We thank the captain and crew of the RV Marcus Langseth and the RRS Discovery, and also the scientific technicians. S.P.H. was funded by the Natural Environment Research Council (NERC) grant NE/K010654/1; C.A.R., N.H. and P.B. acknowledge funding from NERC (NE/M003507/1 and NE/K010654/1) and the European Research Council (GA 638665); J.-M.K. and D.S. were funded by NERC (NE/M004643/1). A.S. and H.S. thank the German Research Foundation (DFG) for funding through an Emmy Noether Young Researcher Grant (no. 276464525). J.Z. was supported by the Czech Science Foundation (GACR-18-06716J). We thank M. Mai for taking the time to provide very useful feedback. T. Craig also provided useful comments.

Author information

Authors and Affiliations



S.P.H. managed the study, carried out the mainshock and aftershock source analysis and wrote the manuscript. R.O. computed the teleseismic slip-rate inversion, assisted with the Mach cone analysis and contributed to the manuscript. A.S. and H.S. computed the teleseismic BP images and contributed to the manuscript. C.A.R., N.H. and J.-M.K. conceived the experiment, acquired funding, managed the project and contributed to the manuscript. R.E.A. carried out a preliminary teleseismic slip inversion and contributed to the manuscript. P.B. and D.S. worked on early estimates of source depth, assisted with aftershock detection and relocation and commented on the manuscript. J.Z. assisted with the RMT inversions and contributed to the manuscript. Y.Y. and K.S. assisted with the teleseismic slip-rate inversion and commented on the manuscript.

Corresponding author

Correspondence to Stephen P. Hicks.

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Extended data

Extended Data Fig. 1 Rayleigh wave vertical displacement seismograms.

Waveforms are plotted for the Mw 7.1 Romanche mainshock and Mw 5.8 co-located aftershock, for stations shown in Fig. 3b. The top-left box indicates the network code, station name, epicentral distance (Δ), azimuth relative to rupture direction (Φ), and cross-correlation coefficient. Stations N4.N59A and II.EFI are located in the predicted Mach cone.

Extended Data Fig. 2 Predicted Mach cone.

Azimuthal dependence of directivity factor (blue line) for a rupture velocity of 5.7 km/s, period of 10 s, rupture duration of 25 s and Rayleigh wave velocity of 3.4 km/s. The black-dashed line, red dashed lines, and green shaded areas demarcate azimuths where high waveform similarity between the Romanche mainshock and co-located aftershock can be found. These azimuth values correspond well to the locations where high waveform similarity is observed in the data (Fig. 3b).

Supplementary information

Supplementary Information

Supplementary Notes 1–6, Figs. 1–16 and Tables 1 and 2.

Supplementary Data 1

Compressed zip file comprising a QUAKEML-formatted catalogue containing hypocentral locations for the Romanche mainshock and aftershocks, along with arrival times, picks and magnitudes.

Supplementary Data 2

Microsoft Excel compatible spreadsheet containing full source parameters of the single- and multi-point source regional moment tensor (RMT) inversions.

Supplementary Data 3

csv file containing the station locations used for the teleseismic slip inversion.

Supplementary Data 4

csv file containing the station locations of sub-arrays used for teleseismic back projection imaging.

Supplementary Video 1

Movie showing the evolution of maximum semblance (and uncertainty) from teleseismic back projection imaging, demonstrating the back-propagating rupture in map view. The white star is the mainshock epicentre, the black circles show aftershocks.

Source data

Source Data Fig. 3a

Teleseismic slip rate model for plotting Fig.3a.

Source Data Fig. 3b

Back projection semblance over a 2-D geographic area for 2.5-km depth BSL for each time step. This is used for generating Fig. 3b and Supplementary Video 1.

Source Data Fig. 3c

Rayleigh wave cross-correlations for plotting Fig. 3c.

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Hicks, S.P., Okuwaki, R., Steinberg, A. et al. Back-propagating supershear rupture in the 2016 Mw 7.1 Romanche transform fault earthquake. Nat. Geosci. 13, 647–653 (2020).

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