Deciphering stress perturbations throughout the 2025 M_w 7.1 Dingri, Southern Xizang Earthquake

Domain: physics
Published: 2026-01-16
Editorial outcome: major_revision

Abstract
The 2025 moment magnitude (Mw) 7.1 Dingri earthquake in Southern Xizang, China, caused severe devastation and exhibited a complex stress evolution. Its northward unilateral rupture along the twisted, west-dipping graben edge generated ~2 MPa of dynamic stress, triggering blind coseismic slip on an east-dipping fault. Coseismic stress perturbations activated over 50 previously unmapped fault segments, mostly with aseismic slip, while also inducing an early viscoelastic response in the weak middle-to-lower crustal layer below ~20 km depth. Here we explain these behaviours by deciphering the stress perturbations through earthquake cycle observations and models. A major implication is that tracking stress evolution throughout an earthquake provides critical insights into earthquake mechanics.

Introduction
Earthquake rupture dynamics and their postseismic manifestations are governed by the intricate interplay between stress transfer, fault geometry and friction, as well as the rheological responses of the Earth’s crust1–3. While coseismic rupture represents the sudden release of accumulated elastic strain, the subsequent postseismic phase, including aftershocks, aseismic slip, and viscoelastic relaxation, provides critical insights into the mechanical behaviour of faults and the surrounding crust4–7. High-resolution observations of coseismic rupture process, postseismic deformation and interseismic loading, i.e., earthquake cycle observations, are needed to resolve the stress perturbations and evolutions in and around the fault systems8–10. However, three different earthquake cycle phases, including the interactions between key physical behaviours, have rarely been well-documented for fault system that hosted significant earthquakes, leaving gaps in our understanding of how these processes interconnect11–13. The well-recorded 2025 Mw 7.1 Dingri earthquake, together with observations that capture critical stages of the earthquake cycle, provides a rare opportunity to investigate the complex interactions of stress perturbations during and following dynamic rupture.

The Mw 7.1 earthquake struck Dingri County, Southern Xizang, China, on January 7, 2025, causing strong ground shaking and consequent extensive infrastructure damage and 126 fatalities (https://www.chinadaily.com.cn/china/special_coverage/677cbaa1a310f1265a1d9711). The event ruptured along the Dengme Co Fault, marking the eastern boundary of the Dengme Co Graben, the southern segment of the Shenzha-Dingjie rift system14 (Fig. 1). Field investigations reported the mainshock generated fault scarps up to 3m high and widespread en échelon tension cracks15. However, the rupture process and its interactions with its aftermath, such as aftershocks, aseismic slip, and viscoelastic relaxation remain poorly constrained.

In this work, we integrate geodetic and seismic observations to reveal that the 2025 Mw 7.1 Dingri earthquake involved a complex rupture process, widespread triggered aseismic and seismic slip, and early viscoelastic relaxation, thereby elucidating how stress transfer throughout an earthquake shapes fault behaviour. These results demonstrate that earthquake-cycle observations are essential for constraining the spatiotemporal evolution of stress and for linking rupture processes, fault interaction, and crustal rheology within a unified framework of earthquake mechanics.

Results
Coseismic surfacedeformations and interseismic loading
Interferometric SAR (InSAR) data reveal a complex coseismic deformation pattern produced by the earthquake (Fig. 2A) (Methods). The Line of Sight (LOS) offsets reflect a mixture of east-west and vertical motion, consistent with the normal faulting nature of the event. Sharp transitions between positive and negative displacements delineate a ~30 km long, near north-south oriented, twisted rupture trace, beginning ~15 km north of the epicentre and characterised by three eastward-protruding bends (Fig. 2A). Stronger displacements on the western side of the fault trace indicate a west-dipping fault geometry. The maximum LOS offset contrast across the fault (~2.5 m) agrees well with field measurements of 2.5–3.0 m vertical scarps14. This fault was incompletely mapped in earlier inventories, neither by geological studies16,17 nor by interseismic geodetic imaging18, likely because of limited field access, subdued topography across the north-south trending valleys (Fig. 2B) and the spatial smoothing applied in previous geodetic studies18,19. Although no clear surface rupture is evident near the epicentre, descending InSAR and Pixel Offset Tracking (POT) images show LOS offsets exceeding 50 cm (Supplementary Fig. S1), indicating a relatively deep, blind rupture. Similarly, at ~25 km west of the main rupture, deformation maps reveal a localised displacement pattern with peak LOS motion of ~0.6 m but no surface rupture, requiring buried slip on an east-dipping fault spatially separated from the main ruptured faults.

Interseismic loading is essential to constrain coseismic rupture behaviour and recurrence intervals. We jointly invert Global Navigation Satellite System (GNSS) offsets and decadal InSAR observations (Supplementary Fig. S2–3) acquired before the Dingri earthquake to derive high-resolution 3D interseismic surfacedeformation rate maps for southern Xizang (Fig. 1 and Supplementary Fig. S4) (Methods). These maps constrain lower-crustal strain-rate models (Fig. 1A, Supplementary Fig. S5–6), which illuminate the mechanisms driving both the Dingri earthquake and the broader graben system (Methods). At a regional scale, east-west extensional strain in the lower crust aligns with the major grabens of southern Xizang (Fig. 1A). Locally across the Dengme Co Graben, strain distribution indicates ~2.4 mm/yr of extensional deformation beneath the ruptured faults (Fig. 1B), implying a causal link between lower crustal loading/flow and the earthquake.

Independent inversions of interseismic geodetic observations across the main fault segment yield a locking depth of 7.1 ± 3.2 km and a slip rate of 0.6 ± 0.2 mm/yr (Methods), consistent with geological estimates of 0.1–0.7 mm/yr for the Dengme Co Fault15. Assuming a ~3 m coseismic slip and a fully locked fault, this corresponds to a recurrence interval of ~5000 years for Dingri-like events. In contrast, the western fault segment shows a locking depth of 0.4 ± 0.1 km and a slip rate of ~2.1 ± 0.2 mm/yr, indicating that shallow creep dominates the interseismic period (Fig. 1B) but the same segment also hosted deeper coseismic rupture (Fig. 2a). Such shallow creep may act as a rupture barrier, inhibiting coseismic slip from reaching the surface. A high-resolution coseismic rupture model is therefore required to clarify how interseismic loading relates to coseismic rupture and subsequent postseismic responses.

Kinematic rupture process and dynamic triggering
To resolve the spatiotemporal rupture process of the earthquake, we apply a back-projection (BP) method20 to high-frequency (0.3–1.0 Hz) teleseismic waveforms recorded by the Australian (AU) and Eurasian (EU) arrays (Supplementary Fig. S7–8) (Methods). BP results reveal both the spatial distribution and temporal evolution of high-frequency radiators (Fig. 2A, C). The BP results show that rupture propagated unilaterally northward, initiating at a relatively slow speed of ~1.5 km/s during the first 10 s before accelerating to ~2.7 km/s and continuing for ~30 km. Strong high-frequency radiation in the first 10 s coincides with the region experiencing the most severe damage14. Near the northern termination of the surface rupture, a cluster of intense high-frequency radiation appears, possibly resulted from the stopping phase21 generated by the termination of the rupture. These BP results provide independent constraints on rupture speed, forming a critical input to the subsequent finite fault inversion.

Combining aftershock seismicity22, geodetic observations and both GCMT23 and W-phase24 moment-tensor solutions, we approximate the fault geometry with four fault segments: southern (F1), central (F2), northern (F3), and western (F4) (Fig. 2B). We jointly invert teleseismic body waves, local and regional strong motion waveforms, and down-sampled geodetic data for a finite fault model (Methods). Our preferred rupture model (Fig. 2) reproduces all major features in all these observations (Supplementary Fig. S9–12). The model shows that the rupture initiated on F1 at ~8 km depth, with slip on F1 and the southern part of F2 concentrated between the depth of 5–10 km. This slip coincides with intense high-frequency seismic radiation (Fig. 2B), indicating the heterogenous stress conditions at depth. The largest slip asperity occurred on the northern part of F2, with slip shallower than 7 km that reached to the surface. Despite producing the twisted surface rupture, this dominant slip patch ruptured relatively smoothly at high speed and is relatively depleted of high-frequency radiation, likely reflecting a more uniform rupture process25–28.

Most of the rupture on the western fault (F4) occurred 10–15 s after the origin time, at the depths of 3–8 km (Fig. 2D). Stress transferred from the main fault rupture (F1-F3) produced dynamic Coulomb stress changes on F4 that peaked at ~2 MPa also at 10–15 s (Fig. 2B and Supplementary Fig. S13), coincided with the rupture timing inferred from the finite fault model. Coseismic slip area on F4 was largely confined to the positive Coulomb stress change regime (Fig. 2B). These agreements indicate that the deeper portion of F4 was dynamically activated during the mainshock, while the shallow sections accommodated coseismic stress perturbations through postseismic afterslip (see next Section).

Widespread Triggered Aseismic and Seismic Slip
High resolution Sentinel-1 InSAR data captured postseismic deformation during the month following the mainshock (Methods), revealing intricate fields associated with viscoelastic relaxation, shallow aftershocks, and aseismic afterslip (Fig. 3). Viscoelastic relaxation is characterised by long-wavelength surface deformation (see next section), whereas shallow afterslip and aftershock deformations are in much shorter wavelength (Figs. 3, 4, Supplementary Fig. S14–15). The latter shallow processes can be further distinguished by their different temporal behaviours: aftershock deformation remains static in time, while aseismic slip generally continues to evolve (e.g., profiles 1, 7 vs. 2-6 in Fig. 3b).

Careful inspection of the postseismic InSAR images identified two aftershocks (Supplementary Table S1) and more than 50 mostly unmapped fault segments that exhibited shallow aseismic slip (Fig. 3A, Video. S1 archived in Figshare) (Methods). The largest LOS displacement (~15 cm) occurred between January 8 and February 13, ~5 km north of Dengme Co (Supplementary Fig. S14). We modelled the deformation from the two aftershocks and aseismic afterslip on three fault segments using uniform-slip Okada model (Supplementary Fig. S16–20 and Supplementary Table S1) (Methods). The inferred centroid depths are ~2–8 km, with slip direction showing a substantial strike-slip component, likely because the fault normal directions are misaligned with the extensional principal stress in the crust, as well as fault geometry and friction complexities inherited through geological timescale evolutions.

A striking feature of all triggered slip is their overlap with areas of large (>1 MPa) positive Coulomb stress change from the mainshock at 1 km depth (Fig. 3A). These triggered slip can be grouped into three categories, Type 1 occurred on or very close to the main ruptured faults (F2), particularly at rupture terminations and at strongly curved sections of the surface trace (the three eastward protrusions). These locations coincide with coseismic static stress perturbation exceeding 2 MPa, consistent with geometrically controlled rupture arrest and incomplete coseismic slip. Type 2 afterslip was distributed between the main fault F1-3 and F4, as well as above and south of the hypocentre. These slips occurred on pervasively distributed weak faults across the Dengme Co Graben under modest Coulomb stress changes (1–2 MPa). Such stress change is sufficient to drive both aseismic slip and seismic rupture, as suggested by the two aftershocks in Fig. 3B. Triggering of even bigger events like the Dingri mainshock maybe possible when the background stress condition is sufficient. Their frictional behaviour likely lies near the transition between velocity-strengthening and velocity-weakening regimes29. Type 3 afterslip occurred on F4, representing accelerated interseismic creep at shallow depth (<3 km) under velocity-strengthening friction. At depths of 4–6 km on F4, however, seismic slip occurred due to dynamic stress perturbations, suggesting frictional properties that transition from stable to unstable. This depth-dependent change in friction is the key control on mechanical segmentation of fault slip along dip.

Time-series InSAR data reveals complex evolution of deformation from aseismic afterslip (Fig. 3B, profiles 2–6). From four postseismic SAR image acquisitions, we derived LOS displacements for three consecutive 12-day intervals (Fig. 3B, and Supplementary Fig. S14–15). The temporal patterns differ across profiles. Along profiles 5 and 6, sharp offsets reflect substantial shallow afterslip on the main fault, whereas profiles 2-4 are dominated by long wavelength signals, primarily resulting from viscoelastic response, with smaller steps attributable to shallow creep. The superposition of viscoelastic response and afterslip produces variable differential amplitudes between time intervals. For example, along profile 4 the deformation increment during the first 12-day pair (purple vs. grey dots in Fig. 3B) exceeds that of the following pair (green vs. purple), whereas other profiles show much weaker double-difference signals. To better resolve detailed frictional properties on these faults, longer duration InSAR observations acquired in the future will make it possible. With long wavelength deformation features in the current dataset, the rheological nature of the middle-to-lower crust could be constrained.

Early viscoelastic response due to weak middle-to-lower crust
The long-wavelength uplift around the main fault zone in postseismic InSAR observations suggests a deformation process in the middle-to-lower crust, which is likely to be viscoelastic relaxation (Supplementary Fig. S14–15, and Fig. 4B, D). However, this response is entangled with shallow afterslip in the InSAR data, and distinguishing the two requires high spatiotemporal resolution postseismic observations. To separate these processes, we first inverted for afterslip using the same fault geometry as the coseismic slip model, assuming no additional deformation mechanism. This inversion achieved only 49% data–model correlation and left clear residuals around the main fault (Supplementary Fig. S21). In particular, the descending track shows distinct long-wavelength positive residuals, suggesting a contribution from processes deeper than the seismogenic crust. Deep afterslip can be excluded as the resulted deformation polarity is opposite to this residual (Supplementary Fig. S21B3). We therefore used our finite fault model as input to test the surface displacements predicted by viscoelastic relaxation, adopting a bi-viscous Burgers rheology, while simultaneously inverting for afterslip on faults (Methods). The resulting model reproduces the observations well. The results suggest an optimal viscoelastic layer at depth ≥20 km, with a Kelvin viscosity coefficient of 5×1017 Pa·s (Supplementary Fig. S22), given an assumed Maxwell viscosity of 1×1019 Pa·s that is insensitive to the early postseismic phase.

Building on this, we implement a coupled viscoelastic and elastic inversion framework30,31, using postseismic InSAR data to jointly resolve afterslip and middle-to-lower crustal strain (Methods). Compared with coseismic slip, the resulted afterslip is primarily concentrated in the shallower sections of the main fault, with some minor slip at depth (Fig. 4A). In contrast, viscous strain localises directly beneath the fault zone and produces long wavelength uplift in the LOS observations (Fig. 4B). The preferred model reproduces both ascending and descending observations at short and long wavelengths (Fig. 4B–E), yielding a data-model correlation of 87%, substantially higher than that from afterslip-only inversion. The lower-crustal strain distribution from this joint inversion also closely matches the forward stress field calculated from the finite fault model (Fig. 4a). Resolution tests further confirm the robustness of the inversion (Supplementary Fig. S23). Finally, trade-offs between viscoelastic relaxation and deep afterslip are ruled out, as their opposite polarity in the resulting LOS displacement.

One could argue that the observed uplift in the LOS deformation (Fig. 4B) might be resulted from poroelastic rebound in the upper crust, driven by fluid flow triggered by the mainshock32,33. To test this, we calculated the volumetric stress change induced by the coseismic slip. The resulted stress field shows a clear compressional pattern within the uplifted region (Supplementary Fig. S24), corresponding to a compaction response due to coseismic slip. We then incorporated fluid diffusion and modelled three poroelastic rebound scenarios, with permeability structures extending to the depths of 5 km, 10 km, and 20 km. All scenarios predict >3 cm of surface subsidence, opposite to the observed uplift (Supplementary Fig. S25). We therefore conclude that the poroelastic rebound alone contributed minimally to the postseismic surface deformation, and the observed long wavelength uplift is mostly from viscoelastic relaxation.

In our preferred viscoelastic model, a Kelvin viscosity of 5×1017 Pa·s indicates a fairly weak mid-to-lower crust beneath the Dingri earthquake. This value lies at the lower end of viscosities reported for Xizang and the adjacent regions (Supplementary Table S2). Notably, the top of this viscous layer coincides with the maximum depth of early aftershocks recorded by a local dense array (Supplementary Fig. S26). Independent seismic evidence further supports this interpretation: shear wave seismic tomography and receiver function results along east-west oriented profiles at ~100 km north of Dingri reveal a low-velocity zone at 20–40 km depth34,35 (Supplementary Fig. S27). These independent investigations all point to a weak middle-to-lower crust in the vicinity of the Dingri earthquake. Our findings are consistent with the long-standing hypothesis that the partially molten lower crust flows laterally in response to gravitational forces and crustal thickening in Southern Xizang36–38. Such deep-seated flow could be responsible for driving upper crust viscous buckling and crustal extension in Southern Xizang39–41.

Methods
Coseismic displacement map processing
We used CtSent software to process Sentinel-1 InSAR interferograms (see Supplementary Table S3). We first completed the geometrical coregistration42 and then corrected the azimuth misregistration43 of all images. We simulated the flattened earth and topographic phase and removed from all interferograms. We performed multi-looking operation on all interferograms using a window size of 5×2 (range-by-azimuth). Then an adaptive spectral filter was applied44. We masked pixels with coherence <0.75 and built an All-Pair-Shortest-Path (APSP) network45 using phase gradients to unwrap single-interferogram phases while excluding edges with large gradients (e.g., across faults). All unwrapped interferograms were geocoded at ~25m×25m (Supplementary Fig. S1).

We processed ALOS-2 Stripmap data using ISCE software. A 1×2 multilook (range-by-azimuth) preserved resolution for phase unwrapping, and the same adaptive spectral filtering was applied. We applied ionospheric correction to ALOS-2 data using a 32×64 multilook to enhance signal-to-noise-ratio (SNR) and prevent unwrapping-induced artifacts from the split-spectrum method46. All interferograms were then geocoded (Supplementary Fig. S1).

We performed Pixel Offset Tracking (POT) on Sentinel-1 and ALOS-2 images using ISCE’s ampcor.py, with 128-pixel windows and a 4-pixel step. Results were Gaussian-filtered (5×5) and masked for correlations <0.6. All POT results are shown in Supplementary Fig. S1.

Time series InSAR data processing
We obtained filtered interferograms from the COMET LiCSAR platform47 (Supplementary Table S4), already corrected for topography and flat-earth effects. After applying GACOS atmospheric corrections48, we removed interferograms with mean coherence <0.75 and retained pixels with average coherence >0.75. We computed temporal coherence and masked pixels below 0.75 before APSP-based phase unwrapping refined by coherence quality. Interferograms were referenced to the first acquisition, and velocities were estimated via iterative reweighted least squares, incorporating a Heaviside jump for Mw >5 events to minimise the coseismic effects.

After velocity estimation, we corrected for rigid plate motion by removing rotation rate of the Eurasian plate from the velocity fields. We further aligned InSAR velocities to a Eurasia-fixed GNSS frame by fitting a second-degree polynomial to remove orbital ramps and constant offsets. This deramping effectively mitigates residual orbital ramps and offsets but also removes the long-wavelength vertical component. Consequently, in the plastic strain and fault locking analyses, we used levelling data as vertical constraints instead of InSAR-derived vertical velocities (see next sections). The ascending and descending track velocity maps are shown in Supplementary Fig. S2. A comparison between the final InSAR velocity maps and GNSS velocities is presented in Supplementary Fig. S3.

3D interseismic velocity map, slip rate and locking depth
Decomposing 3D velocities from ascending and descending InSAR data requires additional constraints, as polar-orbiting satellites poorly resolve North–South motion. Interpolating the N–S velocity helps reduce degrees of freedom but may over smooth fault-related deformation, biasing 3D estimates. To address this, we used the interpolated velocity azimuth angle (Supplementary Fig. S28), referenced to a Eurasia-fixed frame, as an additional constraint instead of directly interpolating the North-South velocity49,50. Supplementary Fig. S4 shows the 3D interseismic velocity fields, with GNSS comparison in Supplementary Fig. S29. High correlations (0.96 E–W, 0.88 N–S) confirm its robustness, and uncertainties of all components were also estimated. These uncertainties are derived from the diagonal elements of the inverse of the normal matrix, i.e., from the linear inversion of BX=L, where B is the design matrix and L the combined ascending and descending LOS observations. The uncertainties correspond to the square roots of the diagonal elements of of inv(BTB). Our results in Supplementary Fig. S30A–C show: the East-West direction has the lowest uncertainty; the Up-Down follows; the North-South direction has the highest uncertainty. Regions with overlapping swaths exhibit the lowest uncertainties due to better geometric constraints provided by multiple viewing angles. This directional variation in uncertainty may help explain the relatively lower InSAR–GNSS correlation in the North-South component.

We also computed the residual misfit of the inversion using V=BX-L. The root mean square (RMS) misfit is calculated as sqrt(VTV/(n-1)), where n is the number of observations. This metric quantifies the disagreement between overlapping InSAR tracks. Our results (Supplementary Fig. S30d) show that the RMS misfit is generally less than 3 mm/yr, indicating good consistency among different track combinations.

To estimate the slip rate and locking state of the main fault and F4 segment, we extracted the horizontal velocity along the profile in Fig. 1 and converted them into the fault-normal direction. Due to the potential glacier motion and landslides related deformation in our horizontal velocity map (Fig. 1 and Supplementary Fig. S4), we adopted a simplified Okada model51 to represent the interseismic velocity along the two faults. Each fault is modelled as a dipping segment characterised by its fixed surface trace, locking depth (starting point depth), down-dip width, and slip rate. We set a large down-dip width of 150 km to approximate a deep creeping extension, about three times half of the profile extent. We used a Markov Chain Monte Carlo (MCMC) process to search the final four parameters and a constant offset. The final fit results are presented as Fig. 1B.

Interseismic lower crust strain (Plastic Strain) inversion
We inherited the same framework as introduced in Wang and Barbot52 to invert the lower crust strain. We first collected 4458 GNSS horizontal velocity vectors and 532 levelling measurements from multiple studies53–55, and masked out all observations out of cuboid region. The finally involved 617 GNSS horizontal velocity vectors are relative to the fixed Eurasia plate. All levelling data are relative to the stable sites northeast of the Tibet Plateau. To better approximate the movement of the Tibet Plateau, we first removed the net rotation in GNSS vectors52.

Through least square fitting, we estimated the Euler pole and angular velocity and removed the forwarded net rotation from the GNSS horizontal velocity vectors (Supplementary Fig. S31A). We also calculated the rotation content and removed them from InSAR horizontal velocity maps (Supplementary Fig. S31B–C). We inherited the assumption that these deformations free of net rotations are caused by stratified lithosphere beneath the Tibet Plateau52. The accumulated slip of faults, strain in the lower crust and upper mantle can contribute to the observed surface deformation31,56. Following Wang and Barbot52, we discretize all faults in triangle dislocations57 and their bottom reach the brittle-ductile transition provided by CRUST1.058. We approximated the ductile layers with a size of 2500 km width by 1250 km length along the strike angle of 28 degrees, which is same as the convergency direction of the Indian Plate and Eurasia Plate, using discretized cuboids with 50 km length. There are 1250 cuboids, with 1250×6=7800 strain elements to be solved. The bottom depth of all cuboids is set to the Moho58, with a uniform thickness of 20 km. In the simultaneous inversion of fault slip and cuboid strain elements, we performed Laplacian smoothing on their respective layers. To enforce the deviatoric strain to be constant in the lithosphere, we set an additional constraint to the trace of strain tensor to be zero.

Dense InSAR velocity maps were incorporated as additional constraints beyond Wang and Barbot52. The green functions including the brittle and ductile ones are in the larger scale. To invert this large-scale geodetic problem, we convert the objective function from least squares to quadratic programming59. The mathematical formulation of this geodetic inversion V=GX is given by

[V_ew; V_ns; V_level; V_InSARew; V_InSARns] = [G_f; G_s; G_l] * X

where V_level is the levelling data, V_InSARew and V_InSARns correspond to downsampled interseismic horizontal velocity map in 500 m resolution, G_f is the green function for fault slip s_f, G_s is the green function for strain tensors G_s, and G_l is to accommodate the regional uplift or subsidence Δ_level in levelling data.

In previous studies, the inversion is completed by a regularised least-squares through solving [G; γ*L] * X = V, where γ is the regularisation factor and L is the Laplacian matrix. We converted this question into a quadratic programming problem through searching the optimal solution of

min 1/2 * X^T * H * X + c^T * X
subject to
H=G^T*G+L^T*L
c=-G^T*V
AX=0
lo <= X <= up

where H is the quadratic objective matrix, c is the linear objective vector, A is the constraint matrix to set the trace of strain tensors to be zero, lo is the lower boundary of X and up is their upper boundary.

Supplementary Fig. S5–S6 present the model fits and residuals. Figure 1A shows the estimated maximum shear strain and horizontal orientations. Supplementary Fig. S32 shows the comparison between the lower crust strain presented in the previous study52. Resolution test of our lower crust strain model in the following section can illustrate our model constrained from more dense observations has good resolution.

Tele-seismic back-projection
We resolved the evolution of high-frequency (HF, 0.3-1.0 Hz) radiation of the Dingri Mw 7.1 mainshock using back-projection (BP) technique60. BP takes advantage of source-receiver reciprocity and back-propagates the array HF waveform data to the source region, tracing the location and time of sources by either maximising the stacking power in time domain61 the coherence in frequency domain62. The waveform data (Supplementary Fig. S8) used for BP analysis are downloaded from two large-aperture teleseismic arrays, the Australian and Eurasian arrays (Supplementary Fig. S7) onto the mainshock source region to image the source process of the mainshock. We then adopt the MUltiple SIgnal Classification (MUSIC)20,62 method to conduct BP.

Finite fault inversion of kinematic rupture model
We derived the finite fault model of the kinematic rupture process using a non-linear joint inversion of seismic waveforms and static deformation data63. The seismic datasets include teleseismic P (45 stations) and SH (27 stations) waves, as well as regional strong motion (7 stations) records. Teleseismic waveform data were obtained from IRIS and recorded by the Global Seismological Network (GSN). Instrument responses were removed, and the data were converted to displacement after applying a 1.0 Hz low-pass filter. Three-component waveform records from 7 regional strong motion stations were also incorporated (Supplementary Fig. S9).

For the static constraints, we used the ALOS-2 descending interferogram (acquired 2025-01-07), which captures the coseismic slip and the full spatial coverage. One Sentinel-1 ascending interferogram was also included. Other geodetic datasets were excluded due to redundancy or significant postseismic contamination. Both ascending and descending datasets were downsampled to ~3000 points. Strong-motion and static Green’s functions were computed using a 1-D velocity model64 and a wavenumber-frequency integration method65.

The kinematic rupture model was parameterised with four segments, defined by surface rupture traces, geodetic observation, and aftershock seismicity. Segment strikes follow the main surface rupture (F2), hinge-line (F1, F3) and postseismic creep (F4). All segments were assigned a dip of 48 degrees (including F4), consistent with both GCMT and W-phase solutions. Each fault segment was subdivided into 2 km × 2 km subfaults, with slip amplitude, rake, rise time and rupture velocity solved for each patch. Rake was constrained between −45° and −135°. Rise time was modelled as a cosine arc, ranging from 0.75 to 7.5 s in 0.75 s steps. Slip amplitude was bounded between 0 to 6 m and the rupture velocity between 1.5 km/s to 3.0 km/s. During inversion, we set the empirical weight for the geodetic misfits to be double of that of the waveform misfits, following the previous studies66. The preferred finite fault model is shown in Fig. 2. Fits to strong-motion and teleseismic data are presented in Supplementary Fig. S10–S11, while static deformation fits are provided in Supplementary Fig. S12.

Dynamic stress calculation and triggering potential analysis
To assess the dynamic stress on segment F4, we used the finite fault model of segments F1–F3 as sources to simulate waveforms and resulting stresses on F4. Following Haskell’s (1964) double-couple radiation pattern theory67, we computed radiation coefficients using each segment’s strike, dip, rake, and relative azimuth, and scaled them by seismic moment to obtain source amplitudes. The same 1D velocity model and Green’s functions as in the finite fault inversion were applied, and the source amplitudes were convolved with a cosine time function to generate synthetic displacement waveforms65.

We performed spatial differentiation of the displacement waveforms and calculated the stress tensors. We projected those stress tensors to the traction on the receiver fault to calculate the normal and shear change (Supplementary Fig. S13A–b). The shear modulus was set to 30 GPa, and the friction coefficient was assigned a value of 0.4. We defined negative normal stress as indicative of tension. The rake angle was fixed at −85°, the average rake angle of the F4 finite fault slip model, with positive shear stress signifying promotion of slip in that direction. Finally, we computed the dynamic Coulomb stress change induced by the source fault on the receiver fault (Supplementary Fig. S13C).

Postseismic InSAR deformation processing and potential fault segment mapping
We produced all Sentinel-1 postseismic interferograms using CtSent software. We applied all images (Supplementary Table S5) with the same data processing steps as coseismic interferogram processing. After all data processing steps, we applied an additional atmospheric noise correction method to all postseismic interferograms, due to the contamination of heavy atmospheric noise.

In the atmospheric noise estimation process of each interferogram, we used a sliding window of the wavelength of 100 km to fit the 1D correlation between InSAR phase and elevation68. Note that the 100 km wavelength we used is to reserve the potential long wavelength deformation in interferograms.

We modelled the InSAR phase–elevation correlation as y = a*x + b, where y is phase and x is elevation, estimating spatial distributions of slope a and intercept b through sliding-window fitting. The fitted atmospheric phase ^y was removed from all interferograms. As shown in Supplementary Fig. S14, the postseismic deformation snapshots reveal progressive afterslip and possible viscoelastic relaxation, distinguishing temporally evolving aseismic slip from static seismic offsets. Fig. S15 further shows overlapping short- and long-wavelength deformation patterns.

By tracking postseismic deformation through time, we identified fault segment extensions from zones of strong deformation gradients visible in at least one InSAR track. These gradients mark active faulting near the surface and were integrated with fault traces from Bayesian slip inversion (Supplementary Video S1).

Bayesian slip inversion of aftershocks and aseismic fault slip
The postseismic InSAR deformation field includes contributions from two moderate aftershock and three visually identifiable aseismic fault slip events that occurred off the main fault. We employ a Bayesian inversion approach using Geodetic Bayesian Inversion Software (GBIS)69 to simulate these deformations. The mean deformation field from three postseismic deformation maps was inverted after semi-variogram noise estimation and quadtree subsampling. In Supplementary Fig. S16–S20 we show the posterior PDFs for the nine fault source parameters obtained after 1×10^6 iterations (burn-in calculations of the first 2×10^4 iterations are removed) and data fits and their derived optimal fault plane. The maximum a posteriori probability solution and the 95% confidence intervals are reported as Supplementary Table S1.

Geodetic afterslip and viscoelastic strain inversion
We modelled surface deformation using elastic dislocation theory in a homogeneous half-space (rigidity = 30 GPa, Poisson’s ratio = 0.25), adopting the finite-fault geometry with depth extension and patch sizes increasing from ~500 m near the surface by a factor of 1.1 with depth. Before inversion, we forwarded and removed the contribution from two possible aftershocks. We selected postseismic deformation data exclusively from Sentinel-1 ascending track (2025-01-17 to 2025-02-22) and Sentinel-1 descending track (2025-01-13 to 2025-02-18), assuming that the four-day observation difference between these datasets was negligible.

We inverted InSAR-derived postseismic deformation for fault afterslip using quadratic programming, constraining dip-slip to keep the rake between −135° and −45°, and applying Laplacian smoothing within and between the three fault segments (F1–F3). We set the smoothing factor to 0.045, inherited from Ma et al.28. The corresponding postseismic deformation fits are presented in Supplementary Fig. S21. The data-model correlation only reaches 49%. The residuals shown in Supplementary Fig. S21a3, b3 for the ascending and descending track data all show clear residuals around the fault planes. For the descending track, clear positive residuals can be seen, indicating possible another mechanism.

We also conducted an inversion of InSAR-derived postseismic deformation to estimate both fault afterslip and lower crustal strain. In this inversion, we set the cuboid size to 10 km, given that the postseismic deformation was adaptively downsampled to samples with the highest sampling resolution of 100 m. The top depth of the lower crust was fixed at 20 km based on tests from viscoelastic relaxation forward modelling, while the bottom depth was set to 69 km, following the crustal thickness defined in the CRUST1.0 model.

We applied the same quadratic programming approach to estimate both fault afterslip and strain tensors. Incorporating lower-crustal strain significantly improves the fit, yielding a data–model correlation of 87% (65% from afterslip and 22% from lower-crustal strain) for the postseismic deformation (Fig. 4C, E), compared to the case with afterslip alone. The deformation (Fig. 4C, E) predicted from lower-crustal strain (Supplementary Fig. S33) reproduces the residual patterns left by the afterslip-only model (Supplementary Fig. S34), providing clear evidence for a lower-crustal contribution. Moreover, the associated surface deformation (Fig. 4C, E) exhibits a much longer wavelength than that from afterslip, consistent with a plausible contribution from an early viscoelastic response.

To validate this interpretation, we compared the inferred strain amplitudes with forward-modelled viscoelastic relaxation results, which provide an independent estimate based solely on forward simulations of the response to static coseismic stress changes. The inverted and the simulated surface deformations exhibited a similar distribution, particularly in the uplift pattern directly beneath the main fault zone (Supplementary Fig. S35).

Viscoelastic relaxation forward simulation
In viscoelastic relaxation forward simulation, the coseismic slip model from the finite fault inversion is utilised as an input to compute the associated stress field. We adopted a Burgers body (Kelvin + Maxwell elements) with a fixed Maxwell viscosity of 10^19 Pa·s, given limited postseismic constraints. Using a simplified rheological structure for Dingri, we tested varying elastic-layer depths and Kelvin viscosities by simulating and removing viscoelastic deformation before afterslip inversion and evaluating the residuals (Supplementary Fig. S22).

Our preferred model includes a 20 km elastic upper crust over a 49 km viscoelastic layer (from CRUST1.0), consistent with the aftershock depth cutoff indicating the brittle–ductile transition. The model spans 250×250 km, and minimising residual misfit yields a Kelvin viscosity of 5×10^17 Pa·s, lower than values from previous Tibetan studies on different earthquakes and lake loading in the Tibet Plateau (Supplementary Table S2), suggesting a weak lower crust and a proxy of the lower crust channel flow36,40,41. The supportive evidence can be found from the seismic tomography results34,35. There is a low velocity zone beneath the Dingri county (Supplementary Fig. S27). Its top depth is approximately 20 km, and its thickness is around 20 km, well consistent with our preferred middle-to-lower crust model. The corresponding model-predicted surface displacements are presented in Supplementary Fig. S35. Overall, the model predictions exhibit a reasonable agreement with the InSAR observations. The most prominent feature is the localised uplift around the main fault zone.

Resolution test on the geodetic inversion
We first performed checkerboard tests to evaluate the resolution of our lower crustal strain model (Supplementary Fig. S23, S36). The results indicate that, regardless of whether we inverted fine or coarse strain distributions, regions constrained by the combined InSAR, GNSS and levelling data are well resolved. In contrast, using only GNSS data as constraints leads to a reduction in the strain model’s resolution. This indicates that the additional constraints from levelling and InSAR data significantly enhance the resolution of the lower crustal strain model, making it more reliable and robust compared to models with fewer constraints. Next, we conducted checkerboard tests for the afterslip inversion, inputting strain models and afterslip distributions with varying resolutions. Our model effectively resolves these inputs in the regions with dense InSAR measurements, indicating that the observed afterslip and postseismic can be untangled and the derived lower crustal strain are reliable.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This paper presents comprehensive analyses on the 2025 Mw7.1 Dingri earthquake, focusing on stress perturbations across the earthquake cycle—from interseismic loading to coseismic rupture and postseismic responses. Fault behavior before, during and after an earthquake has been challenges for a long time, because of combined contributions of complicated deformation processes to the observed surface deformation. This paper presents a nice work on deciphering deformation contributions from viscoelastic relaxation and afterslip through different sources of seismic and high-resolution geodetic observations. This paper is certainly of great interests to the community of postseismic deformation studies.\nA 2D model is certainly very helpful to derive intuitive understanding of effects of fundamental processes. If I have already read the paper carefully enough, would it be useful to add some qualitative description of the effects of 3D models? Such discussion may help readers understand the uncertainty of 2D model results presented in this paper.\nWould a more detailed analysis of aftershock focal mechanisms relative to Coulomb stress changes help strengthen the link between stress perturbations and fault activation? For example, could comparing the rake angles of aftershocks with the orientation of maximum shear stress from the mainshock validate the proposed \"conditional velocity strengthening/weakening\" friction hypothesis?\nAm I missing the steady-state Maxwell viscosity? Only the Kelvin viscosity is mentioned in line 195. A summary of the rheological parameters in your viscoelastic model may help readers to reproduce the modeling results.\nFigure Citation: Figure references are sometimes formatted with a space (e.g., \"Fig. 2a\" in line 91) and sometimes without (e.g., \"Fig.3a\" in line 144, \"Fig.4b\" in line 188). Use a consistent format?", "reviewer_2": "By integrating geodetic and seismic data, this manuscript presents the regional crustal strain rate, interseismic velocity, locking states of the Dengmocuo fault and another unmapped eastward dipping faults, Kinematic rupture models of the Mw 7.1 Dingri earthquake and dynamic stress generated by rupture on the Dengmo Co fault, as well as the coseismic surface deformations and interseismic loading, and then discusses the kinematic rupture process and dynamic triggering during the mainshock among the four fault segments to give a preferred rupture model. It also analyses the widespread triggered aseismic and seismic slip using postseismic deformations during nearly one month after the mainshock to reveal a complete deformation field including viscoelastic response, shallow aftershocks, and aseismic afterslip, and further to identify two aftershocks and over 50 mostly unmapped faults segments with shallow aseismic slip and categorize them into three types in terms of their spatial locations to the four fault segments. At last the authors discuss the relationship between the coseismic static stress perturbations and the after aseismic/seismic slips, as well as their related deformation features for discussion on the deep crustal viscoelastic response due to weak middle-to-lower crust. This manuscript is well organized, but most of the contents, such as the coseismic surface deformations and interseismic loading, rupture process have been published both in Chinese and English. The published papers of the Dingri Earthquake can be found in the specially issue in Earth Science, V. 50(5) (in Chinese), Earthquake Science, Seismology and Geology (in Chinese), and so on.\nTherefore, major revisions are needed, along with the supplementation of truly new materials and data.", "reviewer_3": "The manuscript presents observations and models of the 2025 Mw 7.1 Dingri, Southern Xizang Earthquake as well as the associated late-stage interseismic and early postseismic deformation. The strongest and clearest part of the manuscript is the link between the coseismic stress changes derived from their source model and the postseismic afterslip. The paper also makes inferences about interseismic and postseismic deformation viscoelastic processes which I am less convinced by (further details are below). Overall I am not sure that the short format of Nature Communications does justice to the enormous amount of work presented here. It might work for readers not interested in the details of how the work was done, but for me, it was quite challenging to understand all the assumptions and methods as it required reading the extensive methods section and reviewing supplementary figures. Each part of the earthquake cycle could warrant a paper on its own. Having said that, observations of multiple phases of the earthquake cycle in the same location are rare and therefore there is an advantage to having this all in a single article. Perhaps a longer-form journal would be more appropriate if it is to be published in a single article, or alternatively one element (e.g. the coseismic model and early afterslip story) could be pulled out to form a simpler story that is more digestible.\nI have listed below some specific comment and questions that should be addressed in a revised version.\nMajor points\n1. The authors should clarify how the interseismic InSAR was tied to GNSS. A major concern is whether there are enough sites with vertical data to ensure that the vertical motion due to the locked megathrust around the Himalayas is accounted for. The UD velocities in S4 will be incorrect in the longer wavelengths if vertical GNSS are not used. If vertical GNSS (or levelling) is not used, then I think there is also an implicit assumption of zero vertical motion on average across the image in the method used. The authors should also specify whether they just used the difference between InSAR and GNSS LOS at the points of the GNSS, or a region around them, or interpolated the GNSS LOS first (all methods have been used in the literature).\n2. For the interseismic modelling of the main fault and segment F4, a 1D screw dislocation model is used. This is only valid for strike-slip faults – a different formulation is required for dip slip faults. Furthermore, the screw dislocation model is at odds with the plastic strain model – no strain would be required at depth (apart from at the fault) if there is a fault locked at the surface but freely slipping beneath it. My understanding of the plastic strain model is that it also includes shallow fault slip as well as distributed deformation below the “brittle-ductile” transition. Would it not be better to show the results of the “plastic strain + faults” inversion in the profile? Can the authors also specify somewhere what is the top depth of the cuboids – I couldn’t find this.\n3. The authors claim to be able to identify early visco-elastic deformation and isolate this from afterslip. I don’t think their logic is watertight here. They should be more explicit about the assumptions on which this conclusion is based. Fundamentally, the authors are splitting afterslip and viscoelastic postseismic deformation by spatial wavelength. Anything long-wavelength in the data cannot be explained by fault slip in their models because the faults truncate at 20 km depth (I think – this is not clearly specified). Straining cuboids (representing viscoelastic deformation) are the only deformation mechanism that can operate below 20 km. Although my prejudice is also that the dominant postseismic mechanism below 20 km is viscoelastic deformation, the data don’t actually require this. There are models in which velocity-strengthening frictional behaviour is allowed to compete in the lower crust with viscoelastic relaxation (e.g. Allison and Dunham, 2021 https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020JB021394). In these models the temperature at which viscous process take over depends on the rheological parameters and conditions such as temperature. The authors should modify their text to make it clear to the reader that they cannot rule out deep aseismic slip, or indeed viscous flow in narrow shear zones. I think a better logic would be to look at the misfit to the data when only allowing afterslip in the upper 20 km on the specified faults. This would allow the authors to state that a deformation mechanism operating below 20 km is required. And then they can assert that this is likely to be viscoelastic, and hence move to the cuboid model.\nMinor points\nP1, L35. Although the authors are capturing 3 different parts of the earthquake cycle, this study does not cover a “complete” earthquake cycle.\nP2 L11. The LOS coseismic deformation is mixture of vertical and horizontal motion, as evidenced by the very different patterns seen in the Ascending and Descending coseismic interferograms.\nP11 L44 A reference should be given acknowledging use of data from the COMET LiCSAR platform (Lazecky et al 2020)\nP11 L47/P12 L1 When discarding interferograms with coherence < 0.75 is this the mean coherence for whole image? Or some other measure? Please clarify.\nP12 L10 What magnitude threshold was used for earthquake corrections?\nP12 L32 Can you provide a map of the GNSS velocity angle (and its interpolated field) to demonstrate the consistency.\nP12 L34-36 Assessing quality of 3D inversion process by the correlation coefficient is not sufficient. Because InSAR LOS grids are tied to the GNSS via the removal of a quadratic ramp, a correlation is inevitable – even if there is no information in the InSAR. The 0.88 correlation in the North-South suggests a potential issue here (maybe related to the way the vertical is treated). Some alternative approaches include looking at the deviation from the quadratic in both the InSAR and the GNSS (in LOS), or trying to assess uncertainties by exploiting the overlap between InSAR tracks – the approach only requires 1 Ascending and 1 Descending track, whereas in some places you have 3 tracks and some 4.\nP13 Paragraph 2 (Net Rotation). I was unsure why the rotation was removed by this approach rather than the standard one in geodesy/tectonics, which would be to solve the location of the Euler Pole and a single rotation rate. I guess this approach is equivalent. Could the authors show the results of this calculation?\nP13 Equation 1 (and other equations). Avoid using “X” for multiplication as this can be confused with a cross product, particularly in a matrix equation.\nP13 Line 14 (and elsewhere) Replace “forwarded” with “calculated”.\nFig 1. Include coordinates for the inset and a box on the main panel showing where the inset is located.\nP13 Rephrase this section to make it clear earlier on that you are following the Wang and Barbot methodology, and what is different about your work.\nP13: L23 CURST -> CRUST\nPl14 line 3, tenors->tensors\nFig S8 geography -> geographic\nP13 L37 Provide a reference for quadratic programming.\nP3 L23 Can the authors quantify \"quite decently?\""}, "author_response": "Dear Editor,\nWe are pleased to submit the revised version of our manuscript (NCOMMS-25-45049), “Deciphering Stress Perturbations throughout the 2025 Mw7.1 Dingri, Southern Xizang Earthquake”, for your consideration.\nWe are deeply grateful to you and the reviewers for the thoughtful and constructive feedback provided during the review process. The comments have greatly helped us strengthen the clarity, rigor, and overall impact of our study. In revising the manuscript, we carefully addressed all points raised, with changes made to the text, figures, and supplementary materials.\nIn particular, we:\n1.Added further details on the GNSS–InSAR tie process and the 3D interseismic velocity inversion.\n2.Expanded the discussion of the potential triggering effects of aftershock focal mechanisms relative to Coulomb stress changes.\n3.Examined the limits of afterslip-only model and clarified the feasibility of distinguishing viscoelastic relaxation from afterslip.\nA detailed point-by-point response to the reviewers’ comments is provided below.\nWe are grateful for your effort in handling our manuscript and look forward to your feedback.\nSincerely,\nShengji Wei\nKey Laboratory of Deep Petroleum Intelligent Exploration and Development\nInstitute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China\nEmail: shjwei@gmail.com\nReviewer: 1\nThis paper presents comprehensive analyses on the 2025 Mw7.1 Dingri earthquake, focusing on stress perturbations across the earthquake cycle—from interseismic loading to coseismic rupture and postseismic responses. Fault behavior before, during and after an earthquake has been challenges for a long time, because of combined contributions of complicated deformation processes to the observed surface deformation. This paper presents a nice work on deciphering deformation contributions from viscoelastic relaxation and afterslip through different sources of seismic and high-resolution geodetic observations. This paper is certainly of great interests to the community of postseismic deformation studies.\n>> Thank you for the reassuring feedback and thoughtful summary. Please see our responses to your comments item-by-item below.\nA 2D model is certainly very helpful to derive intuitive understanding of effects of fundamental processes. If I have already read the paper carefully enough, would it be useful to add some qualitative description of the effects of 3D models? Such discussion may help readers understand the uncertainty of 2D model results presented in this paper.\n>> We appreciate the reviewer’s suggestion. To clarify, the 2D screw dislocation model and the 3D strain inversion are independent approaches that serve different purposes in our study.\nThe 2D model is deliberately simplified, with only a few parameters (slip rate and locking depth), which makes it well-suited for constraining the slip rates and locking depths of the two faults and for providing an intuitive understanding of strain accumulation. Its uncertainties were quantified using an MCMC approach, and we report the 95% confidence intervals. While it cannot capture along-strike variations or spatial strain patterns, it provides clear parameter trade-offs and robust first-order constraints.\nThe 3D model, in contrast, is inverted using quadratic programming and produces spatially resolved estimates of lower-crustal strain distribution, though without direct data-driven uncertainty estimates. It complements the 2D model by capturing larger spatial scale lower crust strain with a grid size of 50 x 50 x 20 km.\nImportantly, the two models independently produce consistent slip rates: the 2D screw model yields a total of 2.5 mm/yr (2.0 mm/yr for western fault and 0.5 mm/yr for eastern fault), while the 3D model yields a rate of 2.4 mm/yr across the graben (Fig. 1B). This agreement supports the reliability of our InSAR-derived horizontal velocity field.\nWe have clarified their independent roles in the revised manuscript to avoid confusion and to highlight that both approaches contribute to understanding interseismic deformation, albeit in different ways.\nWould a more detailed analysis of aftershock focal mechanisms relative to Coulomb stress changes help strengthen the link between stress perturbations and fault activation? For example, could comparing the rake angles of aftershocks with the orientation of maximum shear stress from the mainshock validate the proposed \"conditional velocity strengthening/weakening\" friction hypothesis?\n>> Thank you for this insightful suggestion. To address it, we compiled all available focal mechanisms (10 in total) from CENC (https://data.earthquake.cn/datashare/) and GCMT (https://www.globalcmt.org/) (Table R1), with one strike-slip and nine normal faulting events. We calculated their maximum horizontal compressional stress (Shmax) orientations. To account for location uncertainty, we randomly sampled points within a 1 km radius around each focal mechanism, repeating this 10,000 times. In the Andersonian stress framework (Jones 1942), the orientation of Shmax is expected to align with the P axes for strike-slip and sigma 2 for normal faulting events. Consistent with this expectation, we find that the strike-slip and normal faulting aftershocks have Shmax orientations close to their P axes and sigma 2. The median Shmax orientations and their uncertainties are shown in blue and red sticks in the figure below (Fig.R1).\nThis consistent between the Shmax from slip model and aftershocks supports the triggering relationship. As discussed in the paper, the frictional state at their depth is velocity weakening. But noted that, some of these shallow faults also hosted aseismic afterslip and interseismic creep. Together they present a complex friction condition.\nTable R1. Aftershock focal mechanisms from CENC and GCMT\nDepth Strike Rake Strike Rake\nUTC+8 Lon Lat Mw Dip1 Dip2\nkm 1 1 2 2\n20250512\n87.54 28.91 5.5 10 348 45 -95 175 45 -85\n05:11:21\n20250308\n87.51 28.38 4.9 6 15 45 -90 195 45 -90\n16:50:15\n20250127\n87.42 28.55 4.5 7 280 73 148 20 60 20\n17:33:36\n20250121\n87.54 28.28 4.3 2 166 46 -100 0 45 -80\n08:14:16\n20250121\n87.56 28.20 4.5 2 10 50 -80 175 41 -102\n05:03:10\n20250119\n87.57 28.36 4.3 3 25 65 -50 142 46 -144\n00:26:27\n20250118\n87.57 28.35 4.6 2 157 50 -113 10 45 -65\n23:36:51\n20250116\n87.50 28.47 4.6 6 10 60 -75 162 33 -114\n11:19:58\n20250113\n87.50 28.47 4.5 8 10 55 -80 173 36 -104\n23:44:42\n20250108\n87.54 28.21 4.9 12 164 42 -93 348 48 -88\n11:41:23\nFig. R1. Aftershock focal mechanisms and maximum horizontal compressional stress (Shmax) orientations derived from the stress field of the fault slip model. Blue sticks indicate the median Shmax orientations, and red sticks denote their 95% confidence intervals.\nAm I missing the steady-state Maxwell viscosity? Only the Kelvin viscosity is mentioned in line 195. A summary of the rheological parameters in your viscoelastic model may help readers to reproduce the modeling results.\n>> Thank you for pointing this out. Since the early postseismic deformation is primarily governed by the Kelvin element, we fixed the Maxwell viscosity at 1019 Pa⋅s. Its contribution is much smaller compared to that of the Kelvin element. To illustrate this, we compared the vertical deformations 40 days after the earthquake by forward modelling, using two end-member Burgers cases: a Maxwell viscosity of 1×1019 Pa⋅s (used in this study) and a relatively low Maxwell viscosity of 5×1018 Pa⋅s, both with Kelvin viscosity of 5×1017 Pa⋅s (best value for this study). As shown in Fig. R2, the peak deformation difference between the two models is only ~1 mm (panel c), confirming that the Maxwell component has little effect during the early postseismic period. Therefore, fixing the Maxwell viscosity is a reasonable simplification.\nWe have clarified this point in the revised manuscript and added a summary of the rheological parameters to facilitate reproducibility. Please see “We used the Burgers body, consisting of a Kelvin element in series with a Maxwell element. Due to the focus on observations approximately one-month post-earthquake, the viscosity of the Maxwell body, which governs long-term deformation, remains poorly constrained; hence, we assigned a value of 10¹⁹ Pa·s for simplification.”.\nFig. R2. Predicted deformations for two different Burgers viscoelastic models with difference Maxwell viscosity. (a) Maxwell viscoisty at 1e19 Pa s. (b) 5e18 Pa s. (c) difference of (a)-(b). The difference between two models is only the Maxwell viscosity.\nFigure Citation: Figure references are sometimes formatted with a space (e.g., \"Fig. 2a\" in line 91) and sometimes without (e.g., \"Fig.3a\" in line 144, \"Fig.4b\" in line 188). Use a consistent format?\n>> Thank you for such patient check. We double checked and unified all formats.\nReviewer: 2\nBy integrating geodetic and seismic data, this manuscript presents the regional crustal strain rate, interseismic velocity, locking states of the Dengmocuo fault and another unmapped eastward dipping faults, Kinematic rupture models of the Mw 7.1 Dingri earthquake and dynamic stress generated by rupture on the Dengmo Co fault, as well as the coseismic surface deformations and interseismic loading, and then discusses the kinematic rupture process and dynamic triggering during the mainshock among the four fault segments to give a preferred rupture model. It also analyses the widespread triggered aseismic and seismic slip using postseismic deformations during nearly one month after the mainshock to reveal a complete deformation field including viscoelastic response, shallow aftershocks, and aseismic afterslip, and further to identify two aftershocks and over 50 mostly unmapped faults segments with shallow aseismic slip and categorize them into three types in terms of their spatial locations to the four fault segments. At last the authors discuss the relationship between the coseismic static stress perturbations and the after aseismic/seismic slips, as well as their related deformation features for discussion on the deep crustal viscoelastic response due to weak middle-to-lower crust. This manuscript is well organized, but most of the contents, such as the coseismic surface deformations and interseismic loading, rupture process have been published both in Chinese and English. The published papers of the Dingri Earthquake can be found in the specially issue in Earth Science, V. 50(5) (in Chinese), Earthquake Science, Seismology and Geology (in Chinese), and so on.\nTherefore, major revisions are needed, along with the supplementation of truly new materials and data.\n>> Thank you for your thoughtful summary and for pointing out the existing publications related to the Dingri Earthquake. We have carefully reviewed all relevant Chinese and English references, including those in Earth Science (Vol. 50(5)), Earthquake Science, and Seismology and Geology. To provide clarity, we summarized their deformation maps and rupture models in the figure below for comparison.\nImportantly, none of the existing publications presents interseismic loading patterns or postseismic deformation as shown in our Figure 1 and 3-4. Additionally, our study uniquely includes deformation maps derived from L-band ALOS-2 SAR data, which, due to its longer wavelength (~24.25 cm), captures near-fault deformation more effectively than the C-band Sentinel-1 (~5.6 cm) used in previous studies.\nRegarding the rupture process, our finite fault model incorporates near-field strong motion data, which were not used in the referenced studies that rely primarily on teleseismic body waves. Furthermore, our inversion is based on higher-frequency (0.5 Hz) waveforms, offering improved resolution compared to the lower-frequency data (0.25 Hz or less) used in previous work. As a result, our study provides more detailed deformation patterns and a more accurate postseismic stress evolution.\nAfter carefully addressing all the reviewers’ comments, we have made substantial revisions throughout the manuscript. We hope this revised version clearly demonstrates the novelty and added value of our work.\nReviewer: 3\nThe manuscript presents observations and models of the 2025 Mw 7.1 Dingri, Southern Xizang Earthquake as well as the associated late-stage interseismic and early postseismic deformation. The strongest and clearest part of the manuscript is the link between the coseismic stress changes derived from their source model and the postseismic afterslip. The paper also makes inferences about interseismic and postseismic deformation viscoelastic processes which I am less convinced by (further details are below). Overall I am not sure that the short format of Nature Communications does justice to the enormous amount of work presented here. It might work for readers not interested in the details of how the work was done, but for me, it was quite challenging to understand all the assumptions and methods as it required reading the extensive methods section and reviewing supplementary figures. Each part of the earthquake cycle could warrant a paper on its own. Having said that, observations of multiple phases of the earthquake cycle in the same location are rare and therefore there is an advantage to having this all in a single article. Perhaps a longer-form journal would be more appropriate if it is to be published in a single article, or alternatively one element (e.g. the coseismic model and early afterslip story) could be pulled out to form a simpler story that is more digestible.\nI have listed below some specific comment and questions that should be addressed in a revised version.\n>> We appreciate your thoughtful assessments and for recognizing the significance of capturing multiple phases of the earthquake cycle in a single study. We agree that each phase—coseismic rupture, early postseismic transients, and late-stage interseismic deformation—could indeed merit a separate paper. However, we believe that presenting them together in one article provides a more integrated and physically consistent view of the 2025 Mw 7.1 Dingri earthquake. Observations across multiple phases at the same location are rare, and their joint analysis and interpretation offers insights that could be missed if treated in isolation.\nWe acknowledge the concern that the short format of Nature Communications can make it challenging for readers to follow the methodological details. To improve accessibility, we have:\n1. Streamlined the main text to emphasize the central storyline linking coseismic stress changes to early afterslip, while keeping the discussion on viscoelastic processes concise but supported by essential results.\n2. Improved the Methods and Supplementary Materials by adding clearer step-by-step summaries and concise explanations of assumptions and parameter choices, to reduce the burden on readers.\n3. Cross-referenced methods and figures more explicitly in the main text so that readers can locate supporting information without searching extensively.\nWe believe these changes make the manuscript more digestible while retaining the value of the integrated multi-phase perspective. We hope the revised version strikes a better balance between depth and readability, while still showcasing the unique dataset and modelling results in their full context.\nMajor points\n1. The authors should clarify how the interseismic InSAR was tied to GNSS. A major concern is whether there are enough sites with vertical data to ensure that the vertical motion due to the locked megathrust around the Himalayas is accounted for. The UD velocities in S4 will be incorrect in the longer wavelengths if vertical GNSS are not used. If vertical GNSS (or levelling) is not used, then I think there is also an implicit assumption of zero vertical motion on average across the image in the method used. The authors should also specify whether they just used the difference between InSAR and GNSS LOS at the points of the GNSS, or a region around them, or interpolated the GNSS LOS first (all methods have been used in the literature).\n>> We appreciate your insightful comment. The vertical GNSS velocity distribution in our study area is too sparse to reliably capture the long-wavelength vertical deformation associated with the locked megathrust along the Himalaya. Consequently, we did not incorporate vertical GNSS velocities into our InSAR–GNSS tie. This approach is consistent with several southern Tibet interseismic studies where vertical GNSS coverage was similarly insufficient (e.g.,(Chen et al. 2024; Wang et al. 2019)).\nIn our processing, we calculated the difference between the InSAR LOS velocities and the LOS velocities projected from the horizontal GNSS velocities at GNSS sites within the InSAR frame. This difference was approximated by a 2D planar surface and removed from the InSAR velocity field. While this method effectively corrects for residual orbital ramps and offsets, it also removes the long-wavelength vertical component, as the reviewer noted.\nSince our analysis focuses on the horizontal interseismic strain distribution rather than the absolute vertical motion field, the omission of the long-wavelength vertical component does not affect our primary conclusions. Furthermore, in the inversion of lower crust strain, we used only the horizontal GNSS velocities and vertical levelling data in addition to the derived InSAR horizontal velocity maps. We have clarified these methodological details and explicitly stated this limitation in the revised Methods section (see “Following velocity estimation, we corrected for rigid plate motion by modelling and removing the Euler pole and rotation rate of the Eurasian plate from the velocity fields. To align InSAR-derived velocities with GNSS velocities in a Eurasia-fixed reference frame, we calculated their differences and applied a second-degree polynomial fit to each InSAR frame to eliminate long-wavelength orbital errors and the constant offset between InSAR’s relative measurements and GNSS’s absolute measurements. This deramping effectively mitigates residual orbital ramps and offsets but also removes the long-wavelength vertical component. Consequently, in the lower crust strain and fault locking analyses, we used levelling data as vertical constraints instead of InSAR-derived vertical velocities (see next sections).”).\n2. For the interseismic modelling of the main fault and segment F4, a 1D screw dislocation model is used. This is only valid for strike-slip faults – a different formulation is required for dip slip faults. Furthermore, the screw dislocation model is at odds with the plastic strain model – no strain would be required at depth (apart from at the fault) if there is a fault locked at the surface but freely slipping beneath it. My understanding of the plastic strain model is that it also includes shallow fault slip as well as distributed deformation below the “brittle-ductile” transition. Would it not be better to show the results of the “plastic strain + faults” inversion in the profile? Can the authors also specify somewhere what is the top depth of the cuboids – I couldn’t find this.\n>> Thank you for raising this concern. We chose to use a 1D screw dislocation model for the main fault and segment F4 because it requires only a few parameters, making it well suited for illustrating the contrast in slip rate and locking depth between two nearby faults. We acknowledge that the 1D screw model is strictly valid for strike-slip faults, and in our case of oblique/dip-slip motion, fault-normal rather than fault parallel velocity components is used, therefore indeed, with different formulation (Segall 2010).\nThe screw dislocation model and the lower crust strain model are independent inversions that targeting different spatial scales. While the screw dislocation model focus on specific faults that ruptured during the earthquake (tens of km scale), the lower crust strain model covers the entire southern Xizang with strain cubic size of 50 km x 50 km x 20 km. In the 1D screw inversion, the two modelled faults were explicitly included. In the lower crust strain inversion, we jointly solved for shallow fault slip and distributed strain in the mid-to-lower crust, but only for the major thrust and strike-slip faults.\nWe present the two models together to highlight that their independently derived slip rates are consistent: the 1D screw model gives a total of 2.5 mm/yr (2.0 mm/yr for western fault and 0.5 mm/yr for eastern fault) and the lower crust strain inversion yields a rate of 2.4 mm/yr across the graben (Fig.1B). This agreement supports the reliability of our InSAR-derived horizontal velocity field.\nWe have now clarified the description of the lower crust strain inversion in the revised manuscript and specified the depth setting of the cuboids in the Methods section. Please see “The bottom depth of all cuboids is set to the Moho (Laske et al. 2013), with a uniform thickness of 20 km.”.\n3. The authors claim to be able to identify early visco-elastic deformation and isolate this from afterslip. I don’t think their logic is watertight here. They should be more explicit about the assumptions on which this conclusion is based. Fundamentally, the authors are splitting afterslip and viscoelastic postseismic deformation by spatial wavelength. Anything long-wavelength in the data cannot be explained by fault slip in their models because the faults truncate at 20 km depth (I think – this is not clearly specified). Straining cuboids (representing viscoelastic deformation) are the only deformation mechanism that can operate below 20 km. Although my prejudice is also that the dominant postseismic mechanism below 20 km is viscoelastic deformation, the data don’t actually require this. There are models in which velocity-strengthening frictional behaviour is allowed to compete in the lower crust with viscoelastic relaxation (e.g. Allison and Dunham, 2021 https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020JB021394). In these models the temperature at which viscous process take over depends on the rheological parameters and conditions such as temperature. The authors should modify their text to make it clear to the reader that they cannot rule out deep aseismic slip, or indeed viscous flow in narrow shear zones. I think a better logic would be to look at the misfit to the data when only allowing afterslip in the upper 20 km on the specified faults. This would allow the authors to state that a deformation mechanism operating below 20 km is required. And then they can assert that this is likely to be viscoelastic, and hence move to the cuboid model.\n>> We thank the reviewer for this insightful comment and for the reference provided. Following your suggestion, we explicitly compared the data misfits for models including only afterslip (i.e., no viscoelastic layer) with those that also include a viscoelastic layer. Figures R4 (a3, b3) show the misfits for two tracks with only afterslip, while Figures R5 (a4, b4) show the misfits when a viscoelastic layer is included. It is clear that the misfits are larger when only afterslip is allowed, indicating that an additional deformation mechanism operating below ~20 km is required.\nTo further illustrate this, we performed two toy models (Fig. R6):\n1.Fault with a shallow velocity-weakening (VW) portion and a deeper velocity-strengthening (VS) portion, simulating stress-driven afterslip down to ~20 km.\n2.Fault with an additional viscoelastic layer beneath 20 km, representing viscoelastic relaxation.\nPostseismic deformation was simulated for 40 days following the earthquake. The results show that:\n1.Stress-driven afterslip produces mainly subsidence (Fig. R7), with a clear pattern along section P–P’.\n2.Including a viscoelastic layer leads to a different pattern: the interplay between afterslip and viscous strain produces both subsidence (mainly from afterslip) and uplift (mainly from viscoelastic strain) (Figs. R8b–R8d).\nThis difference in polarity allows us to separate the contributions from afterslip and viscoelastic deformation, as described in the manuscript: “Finally, trade-offs between viscoelastic relaxation and deep afterslip are ruled out, as their opposite polarity in the resulting LOS displacement.”\nTo make the logic fluent and reader-friendly, we followed your suggestion and added the data misfits with only afterslip. We also added a detailed description on this. Please see “To separate these processes, we first inverted for afterslip using the same fault geometry as the coseismic slip model, assuming no additional mechanisms. This inversion achieved only 49% data–model correlation and left clear residuals around the main fault (Fig.S26). In particular, the descending track shows distinct positive residuals, suggesting a possible contribution from viscoelastic relaxation.”.\nFig. R4. Postseismic deformation and afterslip model fitting considering no viscoelastic layer. (a–b) show ascending and descending postseismic InSAR observations and model predictions. (a1-a3) show observations, afterslip/aftershock model predicts, viscoelastic predicts, and residuals. Fault planes in (a2) and (b2) are displayed as rectangles, with the upper bound indicated by thick black lines. The inset in (a2) presents a map view of the afterslip model.\nFig. R5. Postseismic deformation and afterslip model fitting considering viscoelastic layer. (a–b) show ascending and descending postseismic InSAR observations and model predictions. (a1-a4) show observations, afterslip/aftershock model predicts, viscoelastic predicts, and residuals. Fault planes in (a2) and (b2) are displayed as rectangles, with the upper bound indicated by thick black lines. The inset in (a2) presents a map view of the afterslip model.\nFig. R6. Conceptual models for two simplified fault configurations. (a) Purely elastic layer. The fault consists of two sections: a shallow 10 km–deep velocity-weakening zone accommodating 3 m of coseismic slip, and a deeper 30 km–thick velocity-strengthening zone. The slip rate 𝑉 follows 𝑉 = 2𝑉 𝑠𝑖𝑛ℎ , where the reference slip rate 𝑉 is 5 cm/yr, the effective normal stress 𝜎 is 100 MPa, and the frictional parameter a−b is 0.004. The shear modulus of the entire elastic layer is 30 GPa. (b) Elastic layer overlying a viscoelastic layer. The elastic layer has the same parameters as in (a), except that the velocity-strengthening section extends from 10 km to 20 km depth. Beneath it lies a 20 km–thick viscoelastic layer represented by a Burgers rheology, with Kelvin viscosity of 5×1017 Pa·s and Maxwell viscosity of 1×1019 Pa·s, identical to the values used in the main manuscript.\nFig. R7. Simulated stress-driven afterslip and corresponding surface deformations. (a) Stress-driven afterslip distribution. (b) Simulated surface deformation resulting from afterslip, with color-coded points indicating vertical (up–down) displacement and quivers representing horizontal displacement vectors. (c) Deformation profile along transect P–P′.\nFig. R8. Simulated stress-driven afterslip, viscoelastic strain, and their associated surface deformations. (a) Distribution of stress-driven afterslip and viscoelastic strain. (b) Simulated surface deformation due to afterslip, with color-coded points showing vertical (up–down) displacement and quivers indicating horizontal displacement vectors. (c) Simulated surface deformation from viscoelastic relaxation. (d) Total surface deformation combining contributions from (b) and (c). (e1–e3) Deformation profiles along transect P–P′ for panels (b)–(d).\nMinor points\nP1, L35. Although the authors are capturing 3 different parts of the earthquake cycle, this study does not cover a “complete” earthquake cycle.\n>> Thank you for pointing this out. We revised it as “three different earthquake cycle phases” to tone down.\nP2 L11. The LOS coseismic deformation is mixture of vertical and horizontal motion, as evidenced by the very different patterns seen in the Ascending and Descending coseismic interferograms.\n>> Thank you for pointing it out. We rephrased this part as “The Line of Sight (LOS) offsets reflect a mixture of east-west and vertical motion, consistent with the normal faulting nature of the event. Sharp transitions between positive and negative displacements delineate a ~30 km long, near north-south oriented, twisted rupture trace, beginning ~15 km north of the epicentre and characterized by three eastward-protruding bends …”.\nP11 L44 A reference should be given acknowledging use of data from the COMET LiCSAR platform (Lazecky et al 2020)\n>> Added as suggested.\nP11 L47/P12 L1 When discarding interferograms with coherence < 0.75 is this the mean coherence for whole image? Or some other measure? Please clarify.\n>> Clarified. We used the mean coherence for the whole image.\nP12 L10 What magnitude threshold was used for earthquake corrections?\n>> Added. Please see “For tracks covering potential earthquakes with a moment magnitude above a threshold of 5.”\nP12 L32 Can you provide a map of the GNSS velocity angle (and its interpolated field) to demonstrate the consistency.\n>> Added. Please see the figure below.\nFig. R9. Interpolated velocity azimuth. Color-coded circles represent the velocity azimuth of GNSS velocity vectors.\nP12 L34-36 Assessing quality of 3D inversion process by the correlation coefficient is not sufficient. Because InSAR LOS grids are tied to the GNSS via the removal of a quadratic ramp, a correlation is inevitable – even if there is no information in the InSAR. The 0.88 correlation in the North-South suggests a potential issue here (maybe related to the way the vertical is treated). Some alternative approaches include looking at the deviation from the quadratic in both the InSAR and the GNSS (in LOS), or trying to assess uncertainties by exploiting the overlap between InSAR tracks – the approach only requires 1 Ascending and 1 Descending track, whereas in some places you have 3 tracks and some 4.\n>> Thank you for this insightful suggestion. We agree that relying solely on the GNSS–InSAR correlation is insufficient for evaluating the quality of 3D inversion results. In response, we have added two additional quality assessments to support the robustness of our inversion approach:\n1. Inversion Uncertainty:\nWe now report the formal uncertainties of the inverted 3D velocity components. These are derived from the diagonal elements of the inverse of the normal matrix, i.e., from the linear inversion of 𝐵𝑋 = 𝐿 , where 𝐵 is the design matrix and 𝐿 the combined ascending and descending LOS observations. The uncertainties correspond to the square roots of the diagonal elements of of 𝑖𝑛𝑣(𝐵𝑇𝐵).\nOur results in Fig.R11 show that:\nThe East-West direction has the lowest uncertainty,\nThe Up-Down follows,\nThe North-South direction has the highest uncertainty.\nRegions with overlapping swaths exhibit the lowest uncertainties due to better geometric constraints provided by multiple viewing angles. This directional variation in uncertainty may help explain the relatively lower InSAR–GNSS correlation (0.88) in the North-South component.\n2.Inversion Misfit:\nWe also computed the residual misfit of the inversion using 𝑉 = 𝐵𝑋−𝐿. The root mean square (RMS) misfit is calculated as √ , where 𝑛 is the number of observations.\nThis metric quantifies the disagreement between overlapping InSAR tracks. Our results in Fig.R10d show that the RMS misfit is generally less than 3 mm/yr, indicating good consistency among different track combinations.\nThese two internal quality metrics have been added to the Supplementary Materials, alongside the GNSS–InSAR comparison, to provide a more comprehensive evaluation of the 3D inversion results.\nFig. R10. Uncertainty and root mean square (RMS) misfit of the 3D velocity inversion process. (a) Uncertainty in the East–West (EW) direction. (b) Uncertainty in the North–South (NS) direction. (c) Uncertainty in the Up–Down (UD) direction. (d) Root mean square (RMS) misfit indicating residual differences between observations and modeled values.\nP13 Paragraph 2 (Net Rotation). I was unsure why the rotation was removed by this approach rather than the standard one in geodesy/tectonics, which would be to solve the location of the Euler Pole and a single rotation rate. I guess this approach is equivalent. Could the authors show the results of this calculation?\n>> Thank you for this insightful comment. The approach we used is indeed mathematically equivalent to the standard Euler pole inversion commonly applied in geodesy and tectonics. In the Methods section, we opted to present the linear formulation for clarity and ease of interpretation within our specific context. We have now added the results of the net rotation, shown in the figure below.\nFig. R11. Calculated net rotation. (a) Net rotation component in GNSS horizontal velocities. (b) Net rotation component in InSAR east–west velocities. (c) Net rotation component in InSAR north–south velocities.\nP13 Equation 1 (and other equations). Avoid using “X” for multiplication as this can be confused with a cross product, particularly in a matrix equation.\n>> We corrected this for all equations.\nP13 Line 14 (and elsewhere) Replace “forwarded” with “calculated”.\n>> Replaced.\nFig 1. Include coordinates for the inset and a box on the main panel showing where the inset is located.\n>> Added. Please see the revised Fig.1.\nFig. 1. Lower crust strain rate, interseismic velocity, and locking states of two major faults. (a) Regional map showing maximum shear strain rates in the lower crust, derived from geodetic data (GNSS and InSAR), across a large region of the Tibetan Plateau. Beach balls indicate focal mechanisms of earthquakes (Mw > 6, 2005–2024) from the GCMT catalog. The upper-right inset (white rectangle in (a)) zooms into the region around the Mw 7.1 Dingri earthquake and displays an east-west interseismic velocity map. The white trace in the inset represents the mapped fault trace based on co- and post-seismic deformations. The dashed black ellipse marks Dengme Co, and the red fault trace denotes the Dengme Co Fault. YRR: Yari Rift, LGR: Lunggar Rift, TYR: Tangra-Yumco Rift, SDR: Shenzha-Dingjie Rift, YGR: Yadong‐Gulu Rift, COR: Cona‐Oiga Rift, MFT: Main Frontal Thrust, JLF: Jiali Fault, KKF: Karakash Fault, KF: Karakoram Fault. (b) The upper panel shows the profile of fault-normal velocity along with the corresponding velocity fit using a 1-D screw dislocation model. The lower panel presents the forward-modeled deformation derived from the inverted lower crustal strain (from panel a).\nP13 Rephrase this section to make it clear earlier on that you are following the Wang and Barbot methodology, and what is different about your work.\n>> Thank you for this suggestion. We rephrased this section. Please see “We inherited the same framework as introduced in Wang and Barbot 53 to invert the lower crust strain distribution.”. We also clarified the difference of our work as “In this study, dense InSAR velocity maps are involved, which are functioned as additional constraints than Wang and Barbot 53.” , and “To invert this large-scale geodetic problem, we convert the objective function from least squares to quadratic programming 60, which is more suitable for solution search in large-scale problems, rather than the Tikhonov regularization used in Wang and Barbot 53.”.\nP13: L23 CURST -> CRUST\n>> Thank you for your patient check. Revised.\nPl14 line 3, tenors->tensors\n>> Thank you for your patient check. Revised\nFig S8 geography -> geographic\n>> Revised as suggested.\nP13 L37 Provide a reference for quadratic programming.\n>> Added.\nP3 L23 Can the authors quantify \"quite decently?\"\n>> We deleted “quite decently” to tone down.\nReferences\nChen, H., Qu, C., Zhao, D., Shan, X., Li, C., & Dal Zilio, L. (2024). Large-Scale Extensional Strain in Southern Tibet From Sentinel-1 InSAR and GNSS Data. Geophysical research letters, 51, e2024GL110512\nDziewonski, A.M., & Anderson, D.L. (1981). Preliminary reference Earth model. Physics of the Earth and Planetary Interiors, 25, 297-356\nJones, O.T. (1942). The Dynamics of Faulting and Dyke Formation: with Applications to Britain. Nature, 149, 651-652\nLaske, G., Masters, G., Ma, Z., & Pasyanos, M. (2013). Update on CRUST1. 0—A 1-degree global model of Earth’s crust. In, Geophysical research abstracts (p. 2658)\nSegall, P. (2010). Earthquake and Volcano Deformation. Princeton University Press\nWang, H., Wright, T.J., Liu-Zeng, J., & Peng, L. (2019). Strain Rate Distribution in South-Central Tibet From Two Decades of InSAR and GPS. Geophysical research letters, 46, 5170-5179\nWang, X., Wei, S., Morales-Yáñez, C., Duputel, Z., Chen, L., Hao, T., & Zhao, L. (2024). Plate interface geometry complexity and persistent heterogenous coupling revealed by a high-resolution earthquake focal mechanism catalog in Mentawai, Sumatra. Earth and Planetary Science Letters, 637, 118726\nZhu, L., & Ben-Zion, Y. (2013). Parametrization of general seismic potency and moment tensors for source inversion of seismic waveform data. Geophysical Journal International, 194, 839-843\nZhu, L., & Helmberger, D.V. (1996). Advancement in source estimation techniques using broadband regional seismograms. Bulletin of the Seismological Society of America, 86, 1634-1641\nZhu, L., & Rivera, L.A. (2002). A note on the dynamic and static displacements from a point source in multilayered media. Geophysical Journal International, 148, 619-627"}, {"round": 2, "reviewer_comments": {"reviewer_1": "It is well done. I’m happy with the concerns being clarified.", "reviewer_3": "I thank the authors for their detailed response to the original reviews. In general I am happy with their responses, which have clarified many points and have improved the manuscript. There are a couple of points where additional minor modifications are required in my view, and a describe those below. I am also still of the view that this would be better in longer format (there are now 36 supplementary figures!), but I understand the desire to summarise the key points in more impactful short format and in the end this is an editorial decision. Provided the changes below are made, I am happy to see this nice work published.\nPoints still needing attention:\n1. Use of the screw dislocation model for normal faulting. The authors acknowledge in their response that this is incorrect so I don’t understand why they continue to use it. It is trivial to change to a correct formulation (for example by using the Okada formulations to calculation surface motions due to slip on a deep extension of the surface normal faults). The surface deformation near the fault is quite different near the fault in such a model, so it is worth doing this properly.\n2. The logic of why viscoelastic processes are required by their observations is improved in the revision, but could be even clearer I believe with some very minor changes as listed below.\nOn page 5 of the revised manuscript I suggest something like:\n(line 1-2) The long-wavelength uplift around the main fault zone in postseismic InSAR observations [Delete: indicates ongoing viscoelastic relaxation] [Insert: suggests a deformation process in the mid-lower crust, which is likely to be viscoelastic relaxation]\n(line 7-8) In particular, the descending track shows distinct [Insert: long-wavelength] positive residuals, suggesting a [Delete: possible contribution from viscoelastic relaxation] [Insert: contribution from processes deeper than the seismogenic crust.]\nI also suggest adding a reference to a supplementary figure supporting the statement on line 9 about the incorrect polarity for deep afterslip as a mechanism.\n(line 10-11) We therefore used our finite fault model as input to [Delete: forward-model] [Insert: test] the surface displacements [Insert: predicted by] [Delete: of] viscoelastic relaxation,...\nAnd state that this model can provide a good fit to the observations."}, "author_response": "Dear Editor,\nWe are pleased to submit the revised version of our manuscript (NCOMMS-25-45049A), “Deciphering Stress Perturbations throughout the 2025 Mw7.1 Dingri, Southern Xizang Earthquake”, for your consideration.\nWe are deeply grateful to you and the reviewers for the thoughtful and constructive feedback provided during the review process. The comments have greatly helped us strengthen the clarity of our study. In revising the manuscript, we carefully addressed all points raised, with changes made to the text and models.\nIn particular, we:\n1. replaced the 1D screw dislocation model with Okada model.\n2. followed the suggestions from Reviewer3 and revised logic of why viscoelastic processes are required by our observations.\n3. streamlined the method section.\nA detailed point-by-point response to the reviewers’ comments is provided below.\nWe are grateful for your effort in handling our manuscript and look forward to your feedback.\nSincerely,\nShengji Wei\nKey Laboratory of Deep Petroleum Intelligent Exploration and Development\nInstitute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China\nEmail: shjwei@gmail.com\nReviewer: 1\nIt is well done. I’m happy with the concerns being clarified.\n>> We sincerely thank the reviewer for the positive feedback. We greatly appreciate the constructive comments provided during the first round, which have helped us improve the quality and clarity of the manuscript.\nReviewer: 3\nI thank the authors for their detailed response to the original reviews. In general I am happy with their responses, which have clarified many points and have improved the manuscript. There are a couple of points where additional minor modifications are required in my view, and a describe those below. I am also still of the view that this would be better in longer format (there are now 36 supplementary figures!), but I understand the desire to summarise the key points in more impactful short format and in the end this is an editorial decision. Provided the changes below are made, I am happy to see this nice work published.\n>> We appreciate the reviewer’s positive evaluation and constructive input throughout the review process, which has helped improve the manuscript substantially.\nWe have streamlined the Methods section for better readability. Regarding the number of supplementary figures, we intentionally included detailed materials to ensure transparency and reproducibility. While the main text summarizes key findings, the supplementary figures document the underlying data processing, model tests, and sensitivity analyses, enabling readers to fully understand and reproduce our results.\nAll remaining minor points have been addressed as below.\nPoints still needing attention:\n1. Use of the screw dislocation model for normal faulting. The authors acknowledge in their response that this is incorrect so I don’t understand why they continue to use it. It is trivial to change to a correct formulation (for example by using the Okada formulations to calculation surface motions due to slip on a deep extension of the surface normal faults). The surface deformation near the fault is quite different near the fault in such a model, so it is worth doing this properly.\n>> We appreciate the reviewer’s constructive suggestion. In the previous revision, we acknowledged that the screw dislocation formulation is not physically appropriate for modelling fault-parallel deformation of normal faults. But this model works well for fault-normal deformations, it qualitatively captures the broad, long-wavelength deformation gradient.\nFollowing the reviewer’s recommendation to better represent near-field deformation, we have replaced the screw-dislocation approximation with a dip-slip half-space formulation (following Okada). Each fault is represented by a dipping segment characterized by its surface trace, locking depth (the top depth of this segment), dip angle, down-dip width (set to 150 km to approximate a deep creeping extension, about three times half of the profile extent), and slip rate.\nThe new model resolves a measurable curvature in the near-fault region while preserving the long-wavelength deformation pattern. The revised inversion captures the shallow creeping behaviour along the western fault (F4) more accurately (Fig. R1b), consistent with the data within uncertainty limits, whereas the main fault plane (F1-3) shows no significant change. The updated model achieves a slightly lower RMS misfit (1.05 mm/yr) compared with the 1-D screw-dislocation model (1.09 mm/yr). Overall, the results remain consistent with previous estimates, therefore does not change our main conclusions. All relevant descriptions in the main text and Supplementary Materials have been updated accordingly.\nFig.R1. Velocity profile data fitting comparison. (a) the profile of fault-normal velocity along with the corresponding velocity fit using a 1-D screw dislocation model. (b) data fit using Okada model.\n2. The logic of why viscoelastic processes are required by their observations is improved in the revision, but could be even clearer I believe with some very minor changes as listed below.\n>> We thank the reviewer for the constructive feedback. We have revised the relevant section to further clarify why the observed deformation requires viscoelastic processes, following the minor suggestions listed below.\nOn page 5 of the revised manuscript I suggest something like:\n(line 1-2) The long-wavelength uplift around the main fault zone in postseismic InSAR observations [Delete: indicates ongoing viscoelastic relaxation] [Insert: suggests a deformation process in the mid-lower crust, which is likely to be viscoelastic relaxation]\n>> We deeply appreciate for this suggestion. Revised it as “The long-wavelength uplift around the main fault zone in postseismic InSAR observations suggests a deformation process in the mid-lower crust, which is likely to be viscoelastic relaxation.”.\n(line 7-8) In particular, the descending track shows distinct [Insert: long-wavelength] positive residuals, suggesting a [Delete: possible contribution from viscoelastic relaxation] [Insert: contribution from processes deeper than the seismogenic crust.]\n>> Revised as you suggested. Please see “In particular, the descending track shows distinct long-wavelength positive residuals, suggesting a contribution from processes deeper than the seismogenic crust.”.\nI also suggest adding a reference to a supplementary figure supporting the statement on line 9 about the incorrect polarity for deep afterslip as a mechanism.\n>> Added.\n(line 10-11) We therefore used our finite fault model as input to [Delete: forward-model] [Insert: test] the surface displacements [Insert: predicted by] [Delete: of] viscoelastic relaxation,...\n>> Revised as you suggested. Please see “We therefore used our finite fault model as input to test the surface displacements predicted by viscoelastic relaxation”.\nAnd state that this model can provide a good fit to the observations..\n>> Added as you suggested. Please see “The resulting model reproduces the observations well.”."}]

Decision letter