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Physical conditions and frictional properties in the source region of a slow-slip event

Abstract

Recent geodetic studies have shown that slow-slip events can occur on subduction faults, including their shallow (<15 km depth) parts where tsunamis are also generated. Although observations of such events are now widespread, the physical conditions promoting shallow slow-slip events remain poorly understood. Here we use full waveform inversion of controlled-source seismic data from the central Hikurangi (New Zealand) subduction margin to constrain the physical conditions in a region hosting slow slip. We find that the subduction fault is characterized by compliant, overpressured and mechanically weak material. We identify sharp lateral variations in pore pressure, which reflect focused fluid flow along thrust faults and have a fundamental influence on the distribution of mechanical properties and frictional stability along the subduction fault. We then use high-resolution data-derived mechanical properties to underpin rate–state friction models of slow slip. These models show that shallow subduction fault rocks must be nearly velocity neutral to generate shallow frictional slow slip. Our results have implications for understanding fault-loading processes and slow transient fault slip along megathrust faults.

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Fig. 1: Tectonic setting and slow-slip distribution on the Hikurangi subduction interface.
Fig. 2: Seismic structure and geological interpretation across the central Hikurangi margin.
Fig. 3: Physical and mechanical characteristics of the subduction plate boundary and outer Neogene accretionary wedge across the central Hikurangi margin.
Fig. 4: Three-dimensional conceptual model summarizing several key physical, hydrological and fault-slip processes across the central Hikurangi margin.
Fig. 5: RSF model results and stability phase diagram.

Data availability

All multichannel seismic field data, seismic navigation and acquisition logs from the 05CM experiment are archived with the New Zealand government and freely available at https://data.nzpam.govt.nz/GOLD/system/. Our final 2D elastic full waveform inversion velocity models88 (https://doi.org/10.26022/IEDA/330190) can be found on the Marine Geoscience Data System.

Code availability

The seismic processing and imaging codes associated with this paper are maintained by the corresponding author at the Institute for Geophysics at the University of Texas at Austin. Some components are available on request.

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Acknowledgements

In 2005, seismic line 05CM-38, part of the 05CM survey, was acquired by GNS Science and the New Zealand Ministry of Economic Development. We gratefully acknowledge the captain, technical staff and crew of the M/V Pacific Titan. This work was also supported by the Institute for Geophysics at the University of Texas at Austin. S.H., D.B., I.P. and L.M.W. were supported by the MBIE Endeavour fund Hikurangi Subduction Earthquakes and Slip Behaviour.

Author information

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Authors

Contributions

A.F.A. conceived the study and analysed the seismic data. J.B. conducted the rate–state friction modelling. A.F.A. and J.B. wrote the manuscript with contributions and edits from all other authors.

Corresponding author

Correspondence to Adrien F. Arnulf.

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The authors declare no competing interests.

Additional information

Peer review information Nature Geoscience thanks Takeshi Tsuji, William Frank and Andrew Calvert for their contribution to the peer review of this work. Primary Handling Editor: Stefan Lachowycz.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Imaging approach: P-wave velocity models derived from traveltime tomography and elastic full waveform inversion of downward extrapolated (Synthetic Ocean Bottom Experiment) multichannel seismic data along profile 05CM-38, central Hikurangi convergent margin, New Zealand.

a Traveltime tomography P-wave velocity structure (see Supplementary Movie 1); b Elastic full waveform inversion P-wave velocity structure derived after a from refraction and wide-angle reflection seismic energy ahead of the seafloor reflection arrival in downward extrapolated MCS data (see Supplementary Movie 2). c Elastic full waveform inversion P-wave velocity structure derived after a and b from the seismic reflection energy below the seafloor reflection and above the first seafloor multiple in downward extrapolated MCS data (see Supplementary Movie 3). Comparison of 1-D velocity models at a distance of 25 km (d), 34.55 km (e) and 63.5 km (f) from the deformation front. The seafloor streamer tomography model isolates the large wavelength velocity structure (green line), while the two following stages of elastic FWI incrementally improves the fine scale gradient structure (red and black lines). The dashed green lines mark + /− 10% of the seafloor tomography model. Here, we suggest that the error in the high-frequency component of the P-wave velocity model is <10% of the final recovered value (region bounded by the dashed green lines). The error in the low-frequency component (that is ~the green line) is expected to be smaller.

Extended Data Fig. 2 Imaging approach: P-wave velocity gradient models derived from traveltime tomography and elastic full waveform inversion of downward extrapolated multichannel seismic data along profile 05CM-38, central Hikurangi convergent margin, New Zealand.

a Traveltime tomography P-wave velocity gradient structure (see Supplementary Movie 1); b Elastic full waveform inversion P-wave velocity gradient structure derived after a from refraction and wide-angle reflection seismic energy ahead of the seafloor reflection arrival in downward extrapolated MCS data (see Supplementary Movie 2). c Elastic full waveform inversion P-wave velocity gradient structure derived after a and b from the seismic reflection energy below the seafloor reflection and above the first seafloor multiple in downward extrapolated MCS data (see Supplementary Movie 3).

Extended Data Fig. 3 Progress of waveform inversion from iteration 0 to 60 as measured by sum of squares misfit and correlation coefficient.

All downward extrapolated shots along seismic profile 05CM-38 were inverted, for a total of 2321 shots (shots 1070 to 3390, see Supplementary Movie 2). a Sum of squares misfit and cross correlation as a function of shot number. The misfit (green, iteration 0; black, iteration 60) is compared to the data size, blue. The sum of squares misfit is 149% of the data size initially but is reduced to 31% by iteration 60. The average correlation coefficient increases from 0.38 to 0.82 with the remaining lower correlation regions coinciding with regions where free gas is present beneath the bottom simulating reflection (see Figs. 2 and 3; Extended Data Fig. 1). b Histogram of correlation coefficient value between observed and modelled seismograms for iterations 0 and 60, with mean (red cross), median (red line), 25% and 75% quartiles (blue box) and 10% and 90% percentiles (gray whiskers).

Extended Data Fig. 4 Waveform inversion statistics for iteration 90 as measured by sum of squares misfit and correlation coefficient.

All downward extrapolated shots along seismic profile 05CM-38 were inverted, for a total of 2321 shots (shots 1070 to 3390, see Supplementary Movie 3). a Sum of squares misfit and cross correlation as a function of shot number. The misfit (black, iteration 90) is compared to the data size, blue. The final sum of squares misfit is 50% of the data size and the average correlation coefficient is 0.62 capturing most of the constructive signal in the data (that is regions of high signal-to-noise ratio, see Supplementary Movie 3). b Histogram of correlation coefficient value between observed and modelled seismograms for iteration 90, with mean (red cross), median (red line), 25% and 75% quartiles (blue box) and 10% and 90% percentiles (gray whiskers).

Extended Data Fig. 5 Offset dependence (1 km to 9 km) of the downward continued data amplitude spectrum (gray lines).

a. Downward extrapolated dataset filtered with a 6 poles Butterworth bandpass filter with corner frequencies of 3 and 15 Hz. b. Downward extrapolated dataset filtered with a 6 poles Butterworth bandpass filter with corner frequencies of 3 and 30 Hz. We assumed for our elastic FWI that the principal attenuation effects can be incorporated into an effective source wavelet that is updated as part of the inversion process (initial source wavelets: green lines, final source wavelets: red lines).

Extended Data Fig. 6 Elastic properties and state of stress along the megathrust fault.

Average elastic moduli and pore fluid pressure ratio a, or effective stress b, within ± 50 m of the subduction fault. We identify two very compliant and overpressured regions (regions i and iii, see Fig. 3) surrounding a stiffer portion of the subduction fault where elastic moduli increase by > = 40%, and fluid overpressure reduces by up to 35% (region ii, see Fig. 3).

Extended Data Fig. 7 Quantification of bulk and shear modulus uncertainties under various S-wave velocity uncertainty assumptions.

a. Poisson’s ratio and Vp/Vs as a function of Vp for common lithologies (see Fig. 3 in ref. 89). The color map represents the Vp versus Poisson’s ratio (or Vp/Vs) density distribution for seismic line 05CM-38 recovered after 90 iterations of elastic FWI. Dashed blue and solid blue lines correspond to suggested ‘extremely low’ and ‘low’ conversion models to estimate Poisson’s ratio (or Vp/Vs) from known Vp values. Conversely, dashed red and solid red lines correspond to suggested ‘extremely high’ and ‘high’ conversion models to estimate Poisson’s ratio (or Vp/Vs) from known Vp values. The two dashed lines can be seen as minimum and maximum models encompassing most common lithologies. b. Shear modulus along the megathrust fault under various assumptions (blue and red lines refer to the models discuss in a. c, Bulk modulus along the megathrust fault under various assumptions (blue and red lines refer to the models discuss in a. The black lines on b and c correspond to the values recovered by our elastic FWI.

Extended Data Fig. 8 Average P-wave velocity between 3 km and 4 km depths along profile 05CM-38, central Hikurangi convergent margin, New Zealand.

Dashed green, orange and yellow lines are the average velocities within the Cretaceous to Paleogene foundation, Neogene inner wedge and Neogene outer wedge, respectively. P-wavespeeds increase by ~0.5 km/s moving landward from the Neogene inner wedge into the deforming foundation of pre-subduction, Cretaceous to Paleogene rocks.

Extended Data Fig. 9 Parameters for rate-state friction continuum fault model shown in Fig. 5b (main manuscript).

Upper panel: rate-state friction parameter b-a plotted with distance from the trench. Lower panel: FWI-derived effective normal stress, fault and upper plate shear moduli, and elastic thickness used in the rate-state numerical model (see Methods section for details).

Extended Data Fig. 10 Homogeneous vs heterogeneous segmentation RSF model results.

Comparison of unstable slip segmentation for models with a. homogeneous shear modulus and effective normal stress; b. heterogeneous shear modulus derived from FWI; c. heterogeneous effective normal stress from FWI. Upper panels show the mechanical parameters used in each model. For all models, dc is constant for the entire fault. The b-a value listed refers to the peak velocity-weakening value of a curve otherwise identical to that of Extended Data Fig. 9. The middle panels show the logarithm of normalized slip velocity (slip velocity / plate velocity) with time and distance from the trench for multiple cycles of each model. The lower panels show the percentage of total cumulative slip accumulated at velocities faster than, slower than, and equal to plate velocity over all model cycles. These plots help distinguish persistent slip segmentation from temporary spontaneous slip segmentation. In a, spontaneous segmentation can be seen in the middle panel at portions of the fault (indicated by arrows) where unstable slip fronts tend to arrest over multiple cycles. This type of slip segmentation, which has been reported in previous rate-state friction models of slow-slip39,48, is considered ‘spontaneous’ because it arises spontaneously from stress and slip interactions on a homogeneous fault, rather than at transitions in geometric, mechanical or loading conditions. The lower panel of a shows that this segmentation has little effect on the cumulative distribution of fast slip on the fault over hundreds of cycles, implying that segment boundaries are temporary and migrate over many cycles. In contrast, the segment boundaries in b-c are long-lived, ‘persistent’ barriers to unstable slip that influence the spatial distribution of the long-term accumulation of fast slip. These segment boundaries occur at sharp gradients in shear modulus b and effective normal stress c. Note that the FWI-derived shear moduli b and effective normal stresses c promote longer-recurrence transient slip events of ~25 years b and ~10-15 years c relative to the shorter-term slip cycles of ~2 years in the homogeneous case a; however, these differences in recurrence interval are most likely caused by the different values of b-a between the three models, which were selected to highlight segmentation patterns of slow-slip events between the three cases.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2, and captions for Suppplementary Videos 1–3.

Supplementary Video 1

Progress of ray-based traveltime tomography from iteration 0–15 as measured by traveltime residuals. Top panel is the residual map for each shot/receiver pair. Bottom panel is the histogram of traveltime residuals with mean (red cross), median (red line), 25% and 75% quartiles (blue box), and 10% and 90% percentiles (grey whiskers). The root-mean-square misfit is 119 ms initially but is reduced to 8 ms after 15 iterations.

Supplementary Video 2

Comparison of observed and modelled data after 60 iterations of elastic FWI. Predicted wavefields (right) for shots 1,070 to 3,390 after 60 iterations of elastic FWI, compared with the field data after downward extrapolation (left). The inversion was limited to the energy ahead of the seafloor reflection arrival, in a frequency band constrained by a sixth-order Butterworth filter with corner frequencies of 3 and 15 Hz. The sum-of-squares misfit and cross-correlation value between predicted and observed data is presented in Extended Data Fig. 3 (black lines). Note the excellent similarity between the modelled and observed data, where most of the constructive signal in the field data is predicted by our model.

Supplementary Video 3

Comparison of observed and modelled data after 90 iterations of elastic FWI. Predicted wavefields (right) for shots 1,070 to 3,390 after 90 iterations of elastic FWI, compared with the field data after downward extrapolation (left). The inversion was limited to the energy below the seafloor reflection arrival and above the first seafloor multiple, in a frequency band constrained by a sixth-order Butterworth filter with corner frequencies of 3 and 30 Hz. The sum-of-squares misfit and cross-correlation value between predicted and observed data is presented in Extended Data Fig. 4 (black lines). Note the excellent similarity between the modelled and observed data, where most of the constructive signal in the field data is predicted by our model.

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Arnulf, A.F., Biemiller, J., Lavier, L. et al. Physical conditions and frictional properties in the source region of a slow-slip event. Nat. Geosci. 14, 334–340 (2021). https://doi.org/10.1038/s41561-021-00741-0

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