The two landslides struck within minutes of each other, at approximately 3:50 and 3:53 UTC, from a source area with an initial estimated volume of about 280,000 cubic meters. As the sliding masses entrained surficial material along their paths, they left a debris deposit stretching roughly 1,162 meters in total length. The site lies in the geomorphic transition zone between the Sichuan Basin and the Yunnan–Guizhou Plateau, an area of stepped topography and deeply incised valleys where sharp elevation drops concentrate surface runoff and dissect the terrain. The local stratigraphy consists of Quaternary slope deposits underlain by the Lower Triassic Feixianguan Formation and the Upper Permian Xuanwei Formation, with a rock sequence exhibiting a reverse dip and dominated by interbedded sandstone and weaker mudstone.

To capture the event, the researchers turned to three nearby broadband seismic stations: JLI, YAJ, and HWS. Landslide-induced signals were identifiable at all three, but only JLI, the closest station, delivered clear, complete three-component waveforms with a high signal-to-noise ratio suitable for quantitative inversion. Using the classical short-term average to long-term average energy-ratio method, with a 0.5-second STA window, a 5-second LTA window, and a trigger threshold of 10, the team identified the first event’s detectable signal from 03:51:01 to 03:52:02 UTC, lasting about 61 seconds, and the second event’s from 03:53:48 to 03:54:29 UTC, lasting about 41 seconds. Because no seismic-wave travel-time correction was applied, these durations approximate rather than exactly date the physical movement, but they provide crucial temporal constraints on a process that would otherwise be invisible.

Time-frequency analysis using the Hilbert–Huang Transform revealed that the seismic energy of both landslides was concentrated below 3 hertz, a hallmark of large, coherent mass movements. The first landslide’s signals showed a striking bimodal pulse pattern, with distinct pulses at roughly 19 and 41 seconds after onset. Matching these pulses against field-mapped terrain features, the team interpreted them as impacts at two points along the runout path: one where the slope gradient dropped abruptly and the sliding mass slammed into the ground surface, producing a strong vertical vibration, and another where the terrain azimuth changed sharply, forcing a violent directional adjustment. The second landslide, by contrast, showed weaker amplitudes and only a weak double-peak feature, likely because the first event had already scoured and smoothed the runout path, reducing the intensity of impact interactions with the ground.

The core of the reconstruction came from inverting the low-frequency seismic signals, filtered between 0.035 and 0.1 hertz, into a force-time function. The method treats the sliding mass as an equivalent single-point force acting on the ground and retrieves its temporal evolution by deconvolving the recorded signal with Green’s functions computed from the Crust1.0 global one-dimensional velocity model. Integrating the resulting force divided by the estimated mass once yields centroid velocity, and twice yields displacement. Because the two events’ individual volumes could not be separated from post-event topography, the team used the roughly 2:1 ratio of long-period signal peak amplitudes to apportion the total mass, estimating approximately 4.2 × 10⁸ kilograms for the first landslide and 2.1 × 10⁸ kilograms for the second. A sensitivity analysis spanning mass ratios from 1:1 to 3:1 bounded the uncertainty in the derived kinematics.

The inverted kinematics revealed a three-stage movement process for each event: initiation and acceleration, collision and redirection, and deceleration and accumulation. The first landslide accelerated eastward under gravity, its centroid reaching an initial peak velocity of about 45 meters per second after 19 seconds and roughly 240 meters of displacement. The motion azimuth then swung abruptly from about 90 degrees to 310 degrees as the mass collided with a slope transition, before dropping to 135 degrees at a local topographic rise, where the mass re-accelerated to a second peak of 24 meters per second. It finally ground to a halt in a southeast-oriented gully at about 61 seconds, having traveled approximately 870 meters. The second landslide followed a similar script in compressed time, peaking first at 35 meters per second, deflecting across a topographic scarp, reaching a second peak of 43 meters per second, and stopping after 41 seconds and about 830 meters of total displacement. The authors caution that these velocities and displacements are approximate centroid estimates subject to significant uncertainty from mass estimation, not deterministic values.

A telling detail emerged from the friction analysis. Calculating the sliding friction coefficient during the stable initiation stage, the team found it decreased from about 0.43 to 0.35 in the first event, an 18.6 percent reduction, and from about 0.41 to 0.33 in the second, a 19.5 percent reduction. This systematic weakening during acceleration plausibly reflects the progressive breakdown of asperity interlocking along the sliding surface, fragmentation of the sliding mass into granular material, and enhanced sliding and rolling among blocks, possibly compounded by transient pore-pressure effects that reduced effective normal stress. Notably, an empirical relationship between friction coefficient and landslide volume predicts a coefficient of roughly 0.4 for a volume of 180,000 cubic meters, closely matching the back-calculated values and lending independent credibility to the inversion.

To explain why the slope failed in the first place, the team built a two-dimensional discrete element model in UDEC based on the reconstructed pre-sliding topography, treating the slope as a deformable sliding mass over a rigid bed and progressively reducing shear strength parameters in a strength reduction procedure. The simulation showed deformation concentrating first at the rear edge and toe of the sliding body: tensile cracking developed behind, shear deformation accumulated at the toe, and the two zones progressively coalesced along the potential sliding surface until the mass detached and accelerated downslope. Laboratory uniaxial compression tests on mudstone from the source area, which yielded strengths of 120 to 137 megapascals and an average elastic modulus of about 12.25 gigapascals, confirmed the intact rock’s hard-brittle character, though the authors emphasize that joints, weathering, and water-induced weakening, not intact rock strength, governed the slope-scale behavior.

The geological setting supplied the vulnerability. The Feixianguan Formation’s interbedded soft silty mudstone and hard sandstone predispose slopes to sliding; the mudstone softens in water and weathers spheroidally, while oxidation of pyrite within the formation generates expansive stress that continuously propagates fractures. Seasonal monsoon rainfall, averaging about 1,100 millimeters annually, drives wet-dry cycling that alternately raises pore pressures and shrinks mudstone into tension cracks, and winter freeze-thaw action in shallow fractures further degrades the rock mass. The area also sits within the Huayingshan seismic zone, the most active tectonic zone in the Sichuan Basin, with 57 earthquakes of magnitude 3.0 or greater recorded within 20 kilometers of the site between 2015 and 2024, repeatedly fracturing the rock and opening pathways for water. In the week before failure, roughly 27 millimeters of rain fell, which the authors interpret as a possible short-term contributing factor rather than a sole trigger. Nearby coal mining, 1.2 kilometers from the slide, remains an unquantified potential influence for lack of monitoring data.

Finally, a limit equilibrium sensitivity analysis identified which factors actually controlled stability. Raising the rear tension crack connectivity ratio from 10 to 90 percent slashed the calculated safety factor from 1.4 to 0.54, a 61 percent reduction, because a connected crack simultaneously destroys tensile resistance and channels water pressure into the slope. Increasing the sliding surface’s internal friction angle raised the safety factor by roughly 23 percent across the tested range. By contrast, horizontal seismic acceleration and the water depth in the rear crack had comparatively modest effects, with the safety factor declining only about 4 percent even as crack water depth reached 40 meters. The message is sobering and actionable: for slopes like Junlian’s, it is the slow, invisible deterioration of rear-edge cracks and frictional strength, accumulated over years of wetting, drying, freezing, and shaking, that determines whether a mountainside holds or collapses in under a minute. The seismic-reconstruction framework demonstrated here offers a way to read that story after the fact, and potentially, as regional seismic networks improve, to catch the next one in progress.

Subject of Research: Failure mechanism and dynamics of the February 2025 Junlian landslides in Southwestern China reconstructed through seismic signal inversion, discrete element simulation, and slope stability analysis

Article Title: Failure mechanism analysis of the catastrophic Junlian landslide in Southwestern China based on seismic signal analysis and numerical simulation

Article References: Chen, Q., Tang, Q., Fan, G., Zhou, J., Li, C., & Zhang, B. (2026). Failure mechanism analysis of the catastrophic Junlian landslide in Southwestern China based on seismic signal analysis and numerical simulation. Environmental Earth Sciences, 85(16), Article 409. https://doi.org/10.1007/s12665-026-13142-6

Image Credits: AI Generated

DOI: 10.1007/s12665-026-13142-6

Keywords: Junlian landslide, landslide dynamics, seismic signal analysis, force-time function inversion, discrete element simulation, limit equilibrium analysis, slope stability, Sichuan, high-speed long-runout landslide, Hilbert-Huang Transform, friction coefficient, geohazard reconstruction