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The break of earthquake asperities imaged by distributed acoustic sensing


Rupture imaging of megathrust earthquakes with global seismic arrays revealed frequency-dependent rupture signatures1,2,3,4, but the role of high-frequency radiators remains unclear3,4,5. Similar observations of the more abundant crustal earthquakes could provide critical constraints but are rare without ultradense local arrays6,7. Here we use distributed acoustic sensing technology8,9 to image the high-frequency earthquake rupture radiators. By converting a 100-kilometre dark-fibre cable into a 10,000-channel seismic array, we image four high-frequency subevents for the 2021 Antelope Valley, California, moment-magnitude 6.0 earthquake. After comparing our results with long-period moment-release10,11 and dynamic rupture simulations, we suggest that the imaged subevents are due to the breaking of fault asperities—stronger spots or pins on the fault—that substantially modulate the overall rupture behaviour. An otherwise fading rupture propagation could be promoted by the breaking of fault asperities in a cascading sequence. This study highlights how we can use the extensive pre-existing fibre networks12 as high-frequency seismic antennas to systematically investigate the rupture process of regional moderate-sized earthquakes. Coupled with dynamic rupture modelling, it could improve our understanding of earthquake rupture dynamics.

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Fig. 1: A 100-km fibre-optic cable as a dense DAS local array for imaging of a regional moderate crustal earthquake.
Fig. 2: Seismic recordings on thousands of closely spaced channels of the DAS array that reveal the presence of subevents in the Mw 6.0 earthquake.
Fig. 3: The imaged subevents in relation to the low-frequency finite-fault inversions and recordings from regional seismic stations.
Fig. 4: Subevents as breakage of stronger local fault patches (asperities) and their effect on rupture illustrated by dynamic rupture modelling.

Data availability

Seismic data at conventional stations are from the Northern California Earthquake Data Center. The DAS recordings of the mainshock and the aftershocks used for the empirical Green’s functions are available from Caltech Data at

Code availability

The code for processing the DAS data and performing back-projection is also available at


  1. Ishii, M., Shearer, P. M., Houston, H. & Vidale, J. E. Extent, duration and speed of the 2004 Sumatra–Andaman earthquake imaged by the Hi-Net array. Nature 435, 933–936 (2005).

    Article  ADS  CAS  PubMed  Google Scholar 

  2. Simons, M. et al. The 2011 magnitude 9.0 Tohoku-oki earthquake: mosaicking the megathrust from seconds to centuries. Science 332, 1421–1425 (2011).

    Article  ADS  CAS  PubMed  Google Scholar 

  3. Lay, T. et al. Depth-varying rupture properties of subduction zone megathrust faults. J. Geophys. Res. Solid Earth 117, B04311 (2012).

    Article  ADS  Google Scholar 

  4. Yao, H., Shearer, P. M. & Gerstoft, P. Compressive sensing of frequency-dependent seismic radiation from subduction zone megathrust ruptures. Proc. Natl Acad. Sci. USA 110, 4512–4517 (2013).

    Article  ADS  CAS  PubMed Central  Google Scholar 

  5. Yin, J. & Denolle, M. A. The Earth’s surface controls the depth-dependent seismic radiation of megathrust earthquakes. AGU Adv. 2, e2021AV000413 (2021).

    Article  ADS  Google Scholar 

  6. Allmann, B. P. & Shearer, P. M. A high-frequency secondary event during the 2004 Parkfield earthquake. Science 318, 1279–1283 (2007).

    Article  ADS  CAS  PubMed  Google Scholar 

  7. Mesimeri, M., Zhang, H. & Pankow, K. L. Backprojection imaging of the 2020 Mw 5.5 Magna, Utah, earthquake using a local dense strong‐motion network. Seismol. Res. Lett. 92, 640–646 (2020).

    Article  Google Scholar 

  8. Zhan, Z. Distributed acoustic sensing turns fiber‐optic cables into sensitive seismic antennas. Seismol. Res. Lett. 91, 1–15 (2019).

    Article  ADS  Google Scholar 

  9. Lindsey, N. J. & Martin, E. R. Fiber-optic seismology. Annu. Rev. Earth Planet. Sci. 49, 309–336 (2021).

    Article  ADS  CAS  Google Scholar 

  10. Vallée, M., Charléty, J., Ferreira, A. M. G., 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).

    Article  ADS  Google Scholar 

  11. Goldberg, D. E., Koch, P., Melgar, D., Riquelme, S. & Yeck, W. L. Beyond the teleseism: introducing regional seismic and geodetic data into routine USGS finite‐fault modeling. Seismol. Res. Lett. 93, 3308–3323 (2022).

    Article  Google Scholar 

  12. Ajo-Franklin, J. B. et al. Distributed acoustic sensing using dark fiber for near-surface characterization and broadband seismic event detection. Sci. Rep. 9, 1328 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  13. Xu, Y., Koper, K. D., Sufri, O., Zhu, L. & Hutko, A. R. Rupture imaging of the Mw 7.9 12 May 2008 Wenchuan earthquake from back projection of teleseismic P waves. Geochem. Geophys. Geosyst. (2009).

  14. Avouac, J.-P., Meng, L., Wei, S., Wang, T. & Ampuero, J.-P. Lower edge of locked main Himalayan Thrust unzipped by the 2015 Gorkha earthquake. Nat. Geosci. 8, 708–711 (2015).

    Article  ADS  CAS  Google Scholar 

  15. Fan, W. & Shearer, P. M. Detailed rupture imaging of the 25 April 2015 Nepal earthquake using teleseismic P waves. Geophys. Res. Lett. 42, 5744–5752 (2015).

    Article  ADS  Google Scholar 

  16. Meng, L., Inbal, A. & Ampuero, J.-P. A window into the complexity of the dynamic rupture of the 2011 Mw 9 Tohoku-oki earthquake. Geophys. Res. Lett. (2011).

  17. Koper, K. D., Hutko, A. R., Lay, T. & Sufri, O. Imaging short-period seismic radiation from the 27 February 2010 Chile Mw 8.8 earthquake by back-projection of P, PP, and PKIKP waves. J. Geophys. Res. Solid Earth 117, B02308 (2012).

    ADS  Google Scholar 

  18. Dodge, D. A., Beroza, G. C. & Ellsworth, W. L. Detailed observations of California foreshock sequences: implications for the earthquake initiation process. J. Geophys. Res. Solid Earth 101, 22371–22392 (1996).

    Article  Google Scholar 

  19. Dunham, E. M., Favreau, P. & Carlson, J. M. A supershear transition mechanism for cracks. Science 299, 1557–1559 (2003).

    Article  ADS  CAS  PubMed  Google Scholar 

  20. Huang, Y., Meng, L. & Ampuero, J.-P. A dynamic model of the frequency-dependent rupture process of the 2011 Tohoku-oki earthquake. Earth Planets Space 64, 1 (2013).

    Google Scholar 

  21. McLaskey, G. C. & Kilgore, B. D. Foreshocks during the nucleation of stick-slip instability. J. Geophys. Res. Solid Earth 118, 2982–2997 (2013).

    Article  ADS  Google Scholar 

  22. Schaal, N. & Lapusta, N. Microseismicity on patches of higher compression during larger-scale earthquake nucleation in a rate-and-state fault model. J. Geophys. Res. Solid Earth 124, 1962–1990 (2019).

    Article  ADS  Google Scholar 

  23. Ellsworth, W. L. & Beroza, G. C. Seismic evidence for an earthquake nucleation phase. Science 268, 851–855 (1995).

    Article  ADS  CAS  PubMed  Google Scholar 

  24. Kilb, D. & Gomberg, J. The initial subevent of the 1994 Northridge, California, earthquake: is earthquake size predictable? J. Seismol. 3, 409–420 (1999).

    Article  ADS  Google Scholar 

  25. Ide, S. Frequent observations of identical onsets of large and small earthquakes. Nature 573, 112–116 (2019).

    Article  ADS  CAS  PubMed  Google Scholar 

  26. Meier, M.-A., Ampuero, J.-P., Cochran, E. & Page, M. Apparent earthquake rupture predictability. Geophys. J. Int. 225, 657–663 (2020).

    Article  ADS  Google Scholar 

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

    Article  ADS  CAS  PubMed  Google Scholar 

  28. Zheng, A., Chen, X. & Xu, W. Present-day deformation mechanism of the northeastern Mina deflection revealed by the 2020 Mw 6.5 Monte Cristo Range earthquake. Geophys. Res. Lett. 47, e2020GL090142 (2020).

    Article  ADS  Google Scholar 

  29. Pierce, I. K. D. et al. Accommodation of plate motion in an incipient strike-slip system: the Central Walker Lane. Tectonics 40, e2019TC005612 (2021).

    Article  ADS  Google Scholar 

  30. Pollitz, F. F., Wicks, C. W. & Hammond, W. C. Kinematic slip model of the 2021 M 6.0 Antelope Valley, California, earthquake. Seismic Rec. 2, 20–28 (2022).

    Article  Google Scholar 

  31. Yin, J., Li, Z. & Denolle, M. A. Source time function clustering reveals patterns in earthquake dynamics. Seismol. Res. Lett. 92, 2343–2353 (2021).

    Article  Google Scholar 

  32. Kiser, E. & Ishii, M. Back-projection imaging of earthquakes. Annu. Rev. Earth Planet. Sci. 45, 271–299 (2017).

    Article  ADS  CAS  Google Scholar 

  33. Yagi, Y., Nakao, A. & Kasahara, A. Smooth and rapid slip near the Japan Trench during the 2011 Tohoku-oki earthquake revealed by a hybrid back-projection method. Earth Planet. Sci. Lett. 355-356, 94–101 (2012).

    Article  ADS  CAS  Google Scholar 

  34. Madariaga, R. High frequency radiation from dynamic earthquake. Ann. Geophys. 1, 17 (1983).

    Google Scholar 

  35. Li, B. et al. Rupture heterogeneity and directivity effects in back-projection analysis. J. Geophys. Res. Solid Earth 127, e2021JB022663 (2022).

    Article  ADS  Google Scholar 

  36. Sagy, A., Brodsky, E. E. & Axen, G. J. Evolution of fault-surface roughness with slip. Geology 35, 283–286 (2007).

    Article  ADS  Google Scholar 

  37. Dunham, E. M., Belanger, D., Cong, L. & Kozdon, J. E. Earthquake ruptures with strongly rate-weakening friction and off-fault plasticity, part 2: nonplanar faults. Bull. Seismol. Soc. Am. 101, 2308–2322 (2011).

    Article  Google Scholar 

  38. Lapusta, N. & Liu, Y. Three-dimensional boundary integral modeling of spontaneous earthquake sequences and aseismic slip. J. Geophys. Res. Solid Earth (2009).

  39. California Department of Technology. Statewide Middle-Mile Analysis. (2022).

  40. Lee, E.-J. et al. Full-3-D tomography for crustal structure in Southern California based on the scattering-integral and the adjoint-wavefield methods. J. Geophys. Res. Solid Earth 119, 6421–6451 (2014).

    Article  ADS  Google Scholar 

  41. Meng, L., Ampuero, J.-P., Luo, Y., Wu, W. & Ni, S. Mitigating artifacts in back-projection source imaging with implications for frequency-dependent properties of the Tohoku-oki earthquake. Earth Planets Space 64, 1101–1109 (2012).

    Article  ADS  Google Scholar 

  42. Wurman, G., Allen, R. M. & Lombard, P. Toward earthquake early warning in northern California. J. Geophys. Res. Solid Earth (2007).

  43. Li, Z. et al. Rapid response to the 2019 Ridgecrest earthquake with distributed acoustic sensing. AGU Adv. 2, e2021AV000395 (2021).

    Article  ADS  Google Scholar 

  44. Yao, H., Shearer, P. M. & Gerstoft, P. Subevent location and rupture imaging using iterative backprojection for the 2011 Tohoku Mw 9.0 earthquake. Geophys. J. Int. 190, 1152–1168 (2012).

    Article  ADS  Google Scholar 

  45. Dieterich, J. H. Modeling of rock friction: 1. Experimental results and constitutive equations. J. Geophys. Res. Solid Earth 84, 2161–2168 (1979).

    Article  Google Scholar 

  46. Ruina, A. Slip instability and state variable friction laws. J. Geophys. Res. Solid Earth 88, 10359–10370 (1983).

    Article  Google Scholar 

  47. Marone, C. Laboratory-derived friction laws and their application to seismic faulting. Annu. Rev. Earth Planet. Sci. 26, 643–696 (1998).

    Article  ADS  CAS  Google Scholar 

  48. Noda, H. & Lapusta, N. Three‐dimensional earthquake sequence simulations with evolving temperature and pore pressure due to shear heating: effect of heterogeneous hydraulic diffusivity. J. Geophys. Res. Solid Earth 115, B12314 (2010).

  49. Rubin, A. M. & Ampuero, J.-P. Earthquake nucleation on (aging) rate and state faults. J. Geophys. Res. Solid Earth (2005).

  50. Chen, T. & Lapusta, N. Scaling of small repeating earthquakes explained by interaction of seismic and aseismic slip in a rate and state fault model. J. Geophys. Res. Solid Earth (2009).

  51. Palmer, A. C., Rice, J. R. & Hill, R. The growth of slip surfaces in the progressive failure of over-consolidated clay. Proc. R. Soc. Lond. A 332, 527–548 (1973).

    Article  ADS  MATH  Google Scholar 

  52. Day, S. M., Dalguer, L. A., Lapusta, N. & Liu, Y. Comparison of finite difference and boundary integral solutions to three-dimensional spontaneous rupture. J. Geophys. Res. Solid Earth (2005).

  53. Barbot, S., Lapusta, N. & Avouac, J.-P. Under the hood of the earthquake machine: toward predictive modeling of the seismic cycle. Science 336, 707–710 (2012).

    Article  ADS  CAS  PubMed  Google Scholar 

  54. Dieterich, J. H. & Kilgore, B. D. Direct observation of frictional contacts: new insights for state-dependent properties. Pure Appl. Geophys. 143, 283–302 (1994).

    Article  ADS  Google Scholar 

  55. Bizzarri, A. & Cocco, M. Slip-weakening behavior during the propagation of dynamic ruptures obeying rate- and state-dependent friction laws. J. Geophys. Res. Solid Earth (2003).

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This work is supported by the US National Science Foundation (NSF) (EAR-1848166 and EAR-1724686), United States Geological Survey (G23AP00111 and G21AP10037), the Gordon and Betty Moore Foundation, and the Southern California Earthquake Center (SCEC). This is SCEC contribution 12737. We thank E. Williams for performing the tap test; J. Yin, Z. Jia, W. Wu and Y. Zhang for discussions; and M. T. Ort and the California Broadband Cooperative for providing fibre access for this study.

Author information

Authors and Affiliations



J.L. and Z.Z. designed the work. J.L. performed the back-projection imaging. T.K. and N.L. designed the rupture modelling. T.K. performed the rupture simulations. J.L. and E.B. implemented the processing code. J.L., Z.Z., T.K. and N.L. prepared and revised the paper. E.B. collected the DAS data and determined the fibre channel locations.

Corresponding author

Correspondence to Jiaxuan Li.

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The authors declare no competing interests.

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Nature thanks Martijn van den Ende and Deborah Kilb for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Histograms of source duration for similar earthquakes.

We select earthquakes with magnitudes between 5.9 and 6.1 and depths within 5–10 km from the SCARDEC database10,11. Four colored histograms represent the duration distribution for earthquakes with different numbers of peaks in their moment-rate function. The vertical magenta line represents the approximate source duration of the 2021 Mw 6.0 Antelope Valley earthquake inferred from the SCARDEC average moment rate function10.

Extended Data Fig. 2 Example correlograms and illustration of our back-projection imaging.

a, b, P- and S-phase correlograms of the Mw 6.0 mainshock calculated by cross-correlating the mainshock waveform with the EGFs from the aftershock (event ID: NC73585056) at a frequency band of 1–5 Hz, with noticeable correlation peaks across all channels marked by arrows. For both phases, each channel has been time-shifted by the corresponding phase travel time from the mainshock epicenter in the catalog. In the P correlogram, the correlation peaks after the first peak do not stand out. This is likely caused by the radiation pattern, since the DAS array samples close to the P nodal line. The coda waves are more prominent and contribute more to the individual P correlogram. c, S-phase correlograms of an Mw 4.5 aftershock (event ID: NC73585291) that has a similar depth and focal mechanism to the mainshock using the same EGF. Unlike the complicated S-phase correlogram for the mainshock in b, this correlogram shows only one horizontally aligned correlation peak, indicating a simpler rupture process. d, When we increase the frequency band to 1–8 Hz and stack the correlograms using the same EGF, another correlation peak (indicated by the red arrow) emerges before the highest correlation peak. This correlation peak is further enhanced using another EGF (event ID: NC73585086) (Extended Data Fig. 4a). e, Normalized vertical-component recordings of both EGF events on a closeby broadband station show their similarities at different frequency bands. f, Schematic diagram of back-projection imaging using both P and S phases. The P and S beam power (blue and red contours, respectively) have different slopes due to their differences in slowness. Therefore, joint P and S back-projection helps to reduce the distance-time trade-off. We adopt a “slowness time slice” (blue dashed line) instead of the conventional constant-time slices (gray dashed line) to visualize the back-projection volume, which reduces the swimming artifact and alleviates the ambiguity in interpreting subevents.

Extended Data Fig. 3 Summary of back-projection results.

(ad) Map view of the back-projected correlation value on the finite fault for all four subevents. The contour line represents the slip contour of the USGS finite fault solution. The red dot is the maximum cross-correlation value in the 3D volume. The blue dot is the maximum cross-correlation value on the finite-fault plane. (eh) Vertical cross-section of the back-projected correlation value for all four subevents. The yellow swath in panels eh is caused by the spatial geometry of the earthquake and our DAS array. Another DAS array at a different azimuth would improve the resolution. The red solid line represents the USGS finite-fault plane. The red dot is the location of the global cross-correlation maximum. The blue dot is the cross-correlation maximum on the finite fault plane. The scattered orange circles are the aftershocks. i, Subevent time, location, magnitude, and average rupture speed between the subevents and S0. The reference (zero) time is 2021-07-08 22:49:48.41 UTC. The locations are determined by picking the maximum cross-correlation value on the intersection between the finite-fault plane and the back-projected cross-correlation isochrone. The mean and error of the magnitude are calculated based on the scaling relationship at five strong-motion stations (Fig. 1; see Methods). The average rupture speed is calculated from the distance over the traveling time between S0 and each of the other three subevents.

Extended Data Fig. 4 Summary of back-projection results continue.

(ad) Correlation time series for P- and S-phases and their average for all four subevents (eh) Three orthogonal slices across the point with the highest correlation in the 3D back-projection volume. The red dot denotes the M6.0 mainshock location, the blue dot denotes the aftershock event used for obtaining the EGF i.e., the M4.2 (event ID NC73585086) aftershock for panels A, E, and the M3.7 (event ID NC73585056) aftershock for panels (bd) and (fh). Since we use a higher frequency band of 4–8 Hz for subevent ‘S0’, compared with 1–5Hz for subevents ‘S1–3’, the cross-correlation time series in (a) show more wiggles than (bd). Incidentally, the back-projection imaging at higher frequencies could bring higher resolution but may cause lower robustness due to cycle skipping. Later subevents would also be more difficult to image due to stronger attenuation at higher frequencies.

Extended Data Fig. 5 Velocity model used for back-projection imaging.

1D layered velocity and density model extracted from the median of the CCA 3D model40. At the depth of the M6.0 mainshock, the shear wave velocity is about 3.5 km/s. We use this 1D velocity model to extract the aftershock P and S windows.

Extended Data Fig. 6 Calculate magnitude through empirical scaling relationship.

Comparison between the predicted magnitude with the catalog magnitude based on the scaling relationship described in Methods. The red dots represent the predicted magnitude at each strong-motion station. For each earthquake event, the blue dot and vertical bar represent the mean and standard deviation of predicted magnitude at five strong-motion stations. The predicted magnitudes are close to the catalog magnitudes (scatter around the black diagonal line).

Extended Data Fig. 7 Back-projection imaging with conventional stations.

a, Location of high-broadband stations (green triangles). b, Three orthogonal slices through the point with the highest correlation in the 3D back-projection volume, with the red dot denoting the location of the M6.0 event, and the blue dot denoting the aftershock used for the EGF. c, Cross-correlation time series for the grid point with the highest cross-correlation value. d, *MAD time series for the grid point with the highest cross-correlation value. e, Bootstrap analysis of *MAD increasing with the number of channels used for stacking. The vertical bar represents the standard deviation of the *MAD. The horizontal dark red line represents the detection significance (*MAD = 11) using seven broadband stations following the same imaging procedure. Note that the horizontal axis is on the log scale.

Extended Data Fig. 8 Snapshots of slip-rate distribution for model 1.

The rupture initiates around the first asperity. The rupture of each asperity results first in a rupture delay and then in a splash of additional slip with faster rupture speed; the effect is most pronounced for the largest fourth asperity.

Extended Data Fig. 9 Distribution of initial parameters and final stress drop for the numerical simulation of model 1 with four asperities.

a, The normal stress level within the asperities is higher than in the background. b, Distribution of the initial slip rate. c, The distribution of the initial shear stress over the dynamic level. d, Distribution of \({\boldsymbol{\psi }}=\frac{{{\boldsymbol{V}}}^{* }{\boldsymbol{\theta }}}{{{\boldsymbol{D}}}_{{\boldsymbol{RS}}}}.\) e, Final stress drop distribution after rupture arrest. The average stress drop is 1.4 MPa. The stress drop within the asperities ranges from 5.0 to 6.1 MPa.

Extended Data Table 1 Rupture model parameters

Supplementary information

Supplementary Video 1

Video of the back-projection image (geometry similar to Extended Data Fig. 3a–d) on the ‘slowness time slice’ (equation (4)) using EGF NC73585056. The dotted white lines represent the time contour on the ‘slowness time slice’. The subevent S1 is determined first as it has the highest stacked beam power in the back-projection domain. With S1 identified, we then determine the subevent S2 as the next maximized beam power, without interpreting the swimming artifact from S1. The procedure is repeated for subevent S3.

Supplementary Video 2

Rupture video for model 1 (four-asperity model) showing the evolution of slip rate on the fault.

Supplementary Video 3

Rupture video for model 2 (model with the fourth asperity removed) showing the evolution of slip rate on the fault.

Supplementary Video 4

Rupture video for model 3 (model with a weaker fourth asperity) showing the evolution of slip rate on the fault.

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Li, J., Kim, T., Lapusta, N. et al. The break of earthquake asperities imaged by distributed acoustic sensing. Nature 620, 800–806 (2023).

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