The earthquake struck within the Kunar-Spin Ghar fault system, a zone of active crustal deformation in the Hindu Kush region where the ongoing convergence between the Indian and Eurasian plates is absorbed through a complex interplay of strike-slip, reverse and transpressional faults. The event caused widespread infrastructure damage, triggered landslides and rock avalanches, and left visible surface deformation across terrain so steep and inaccessible that ground-based investigation was severely limited. For a region with a long and destructive seismic history but sparse instrumental coverage, the quake offered a rare and urgent opportunity to observe how deformation is actually distributed in this part of the collision zone.

Led by Hamedullah Torabi of Kabul Polytechnic University, with colleagues from institutions in Afghanistan, India, South Korea and the United Kingdom, the research team carried out field reconnaissance at accessible sites along the affected valleys. Their observations documented structural damage to buildings, ground cracks, slope failures, rock avalanches and newly emerged springs along the Kunar River valley. These ground effects, recorded in difficult and sometimes dangerous conditions, provided the essential field anchor for interpreting what the satellite data would later reveal. Emergent springs in particular can indicate changes in subsurface permeability caused by shaking and fault movement, offering clues about where the crust was disturbed.

The technological heart of the study lies in Persistent Scatterer Interferometric Synthetic Aperture Radar, or PSInSAR, a technique that tracks millimeter-scale ground movements over time by comparing radar reflections from stable features such as rock outcrops, buildings and boulders. The team analyzed time series of Sentinel-1A radar data acquired by the European Space Agency’s Copernicus mission between April and October 2025, processing both ascending and descending orbital passes. Because each viewing geometry is sensitive to a different combination of horizontal and vertical motion, combining the two allows researchers to decompose the observed radar signal into east-west and vertical components of surface displacement, effectively reconstructing a two-dimensional picture of how the ground moved during the earthquake period.

What emerged from the radar analysis was striking. Both the ascending and descending line-of-sight velocity fields revealed a laterally continuous deformation pattern extending for several tens of kilometres along the strike of the fault system. Decomposition of the signal showed dominant horizontal motion, consistent with the strike-slip and transpressional character of the region, accompanied by broader and spatially variable patterns of uplift and subsidence. In other words, the ground was not simply sliding along one clean break; it was warping, rising and sinking across a wide swath of terrain in a manner that reflects the distributed nature of the forces at work beneath the Hindu Kush.

Swath-profile analysis, which examines how velocities change across cross-sections of the deformed zone, pinpointed the strongest east-west velocity gradients south of the mapped Kunar Fault and within or adjacent to the Spin Ghar mountain-front structural corridor. This detail matters enormously. The most intense deformation did not coincide neatly with the fault trace that appears on existing maps. Instead, it spilled into neighboring structures, indicating that the earthquake-period deformation signal was shared among multiple interacting faults rather than released on a single rupture plane. The pattern is exactly what geologists would expect from distributed transpressional deformation, a regime in which crustal blocks are simultaneously squeezed and sheared, forcing strain to spread across networks of faults rather than concentrate on one master structure.

The concept of transpression, formally described in the structural geology literature in the 1980s, describes deformation that combines pure compression with a component of shear, producing complex fault geometries and strain partitioning. Eastern Afghanistan sits squarely within such a regime. The India-Eurasia collision, which has built the Himalaya and the Tibetan Plateau, transmits enormous compressive stresses deep into the Asian interior, and the Hindu Kush is one of the places where those stresses find release through intracontinental faulting. Previous studies, including GPS-based measurements of plate convergence partitioning in the Pamir-Hindu Kush region and historical catalogs of Afghan earthquakes, have long suggested the region is tectonically active, but the density of observations needed to map how strain is actually distributed has been lacking.

The new results carry a direct and uncomfortable message for seismic hazard assessment. Hazard models typically rely on mapped fault traces to define the locations and widths of potential rupture zones, and building codes and land-use planning follow from those definitions. If deformation in the Kunar-Spin Ghar system is distributed across a corridor tens of kilometres wide rather than focused on individual mapped faults, then hazard assessments based only on those traces may underestimate both the width of the active deformation zone and the spatial extent of ground deformation and secondary hazards such as landslides, rock avalanches and liquefaction. Communities located well away from any mapped fault trace could still sit within the zone of strongest shaking and ground failure in a future event.

The vulnerability of the region amplifies these concerns. Eastern Afghanistan combines steep, landslide-prone topography, dense rural populations living in unreinforced masonry construction, and limited institutional capacity for emergency response, a combination that recent earthquakes in the region have shown to be lethal. The study’s authors, along with earlier researchers who have called for greater earthquake resilience across Central Asia, argue that satellite-based deformation monitoring offers a practical path forward in terrain where traditional field mapping and dense seismic networks are difficult to establish. Because Sentinel-1 radar data are freely available through the Copernicus Open Access Hub, the approach demonstrated here could be extended systematically across Afghanistan and neighboring countries to build a more realistic picture of active deformation corridors.

The 2025 Asadabad-Kunar earthquake lasted only seconds, but its scientific legacy may prove durable. By pairing boots-on-the-ground observation with space-based radar, the research team has shown that the Hindu Kush does not obey the tidy assumption that earthquakes follow the fault lines drawn on maps. The active deformation zone is wider, more interconnected and more structurally complex than previously recognized, and the seismic hazard it poses is correspondingly broader. For the millions of people who live in the shadow of the Spin Ghar mountains, that widened hazard corridor is not an abstract geodynamic detail. It is a warning that the ground beneath their villages can move in places no map currently marks, and that planning for the next earthquake must account for a danger that is distributed, not localized.

Subject of Research: Distributed transpressional deformation and seismic hazard implications of the 2025 Mw 6.0 Asadabad-Kunar earthquake in eastern Afghanistan

Article Title: Evidence for distributed transpressional deformation and seismic hazard implications from the 2025 Mw 6.0 Asadabad- Kunar earthquake, Eastern Afghanistan

Article References: Torabi, H., Kandregula, R. S., Naik, S. P., Shnizai, Z., Ghildiyal, D., Chufal, V. S., & Kim, Y.-S. (2026). Evidence for distributed transpressional deformation and seismic hazard implications from the 2025 Mw 6.0 Asadabad- Kunar earthquake, Eastern Afghanistan. Natural Hazards, 122(18), Article 624. https://doi.org/10.1007/s11069-026-08397-y

Image Credits: AI Generated

DOI: 10.1007/s11069-026-08397-y

Keywords: Afghanistan, Hindu Kush, earthquake, transpressional deformation, PSInSAR, Sentinel-1A, Kunar fault, Spin Ghar, seismic hazard, India-Eurasia collision, strain partitioning, landslides

The research, conducted by Nihal Tekin Ünlütürk of Erciyes University and Uğur Doğan of Yıldız Technical University, evaluated the effect of tropospheric corrections derived from Global Navigation Satellite System (GNSS) observations against those produced by the Generic Atmospheric Correction Online Service for InSAR, widely known as GACOS. Tropospheric delay is one of the dominant error sources in InSAR processing, and if left uncorrected it can distort the interpretation of earthquake-induced deformation fields—the very patterns scientists rely on to infer fault slip, rupture extent, and seismic hazard. The team’s central question was deceptively simple: which correction approach actually improves the displacement maps, and by how much?

To find out, the researchers processed both ascending and descending Sentinel-1A radar images from the European Space Agency’s Copernicus mission using the Sentinel Application Platform (SNAP) within a standard Differential InSAR (DInSAR) workflow. This involved generating interferometric pairs spanning the earthquake, applying phase filtering to suppress noise, and performing phase unwrapping with the Statistical-Cost, Network-Flow Algorithm for Phase Unwrapping, or SNAPHU—a computational step that recovers continuous displacement values from the wrapped fringes that radar phase measurements naturally produce. Working with both ascending and descending orbits matters because each viewing geometry samples the ground differently along the satellite line-of-sight, providing complementary constraints on the three-dimensional deformation field.

The GNSS-based correction strategy exploited Turkey’s dense TUSAGA-Active continuous GNSS network, whose data were provided by the General Directorate of Mapping. Because GNSS signals also pass through the troposphere, each station continuously estimates the zenith tropospheric delay—the total delay a signal experiences traveling vertically through the atmosphere. By regressing these delays against station elevation, the researchers derived parameters that allowed them to predict the topography-correlated component of the atmospheric delay across the entire interferogram, interpolating between stations and removing the estimated delay from the radar phase. GACOS, by contrast, is an online service that generates correction maps by combining global weather model data with an iterative tropospheric decomposition model, separating the stratified, elevation-dependent part of the delay from turbulent components.

The results are a sobering reminder that atmospheric correction is not a solved problem. Both GNSS- and GACOS-based corrections clearly influenced the spatial distribution of the line-of-sight (LOS) displacement fields, reshaping the apparent deformation patterns in ways that could alter scientific conclusions. Yet their effects were not spatially uniform across the study area. In some regions the corrections agreed closely; in others, the corrected products diverged noticeably, leaving analysts with genuinely different pictures of how the ground moved. The study found that neither correction strategy produced a spatially uniform improvement across all interferometric pairs—a finding that challenges the common assumption that applying any atmospheric correction is automatically better than applying none.

The local discrepancies between the corrected products, the authors suggest, likely stem from a combination of factors: regional atmospheric variability, the rugged topography of the East Anatolian Fault Zone, the acquisition geometry of the satellite passes, and the uneven distribution of GNSS stations. Eastern Anatolia is a region of sharp elevation contrasts, where stratified tropospheric delay can change rapidly over short horizontal distances. Where GNSS stations are sparse, interpolation of zenith delays becomes uncertain, and turbulent water vapor variations—which are poorly correlated with elevation—can dominate. Weather-model-based corrections like GACOS, meanwhile, are limited by the spatial and temporal resolution of the underlying meteorological data, which may not capture localized convective moisture features at the moment of a satellite overpass.

Rather than declaring a single winner, the study advocates a more rigorous, diagnostic approach. The researchers emphasize the need to carefully evaluate atmospheric correction performance using spatial statistics, profile comparisons across the deformation field, and displacement-difference analyses between corrected and uncorrected products. In other words, the choice of correction should be treated as a hypothesis to be tested for each interferogram and each tectonic setting, not a routine checkbox. This methodological caution has practical consequences: deformation signals from the Elazığ–Sivrice rupture feed into models of fault slip and earthquake source parameters, and atmospheric artifacts of comparable magnitude could bias estimates of how much the fault moved and where.

The 2020 Elazığ–Sivrice earthquake itself makes a compelling case study. The event struck the East Anatolian Fault Zone, a major strike-slip boundary accommodating the westward extrusion of the Anatolian plate between the Arabian and Eurasian plates. Previous studies have used InSAR and GNSS data to characterize its rupture behavior, and the region’s tectonic importance has motivated extensive geodetic monitoring. The fault zone’s combination of strong topographic relief, semi-arid climate with pronounced seasonal atmospheric variation, and a growing but still uneven GNSS infrastructure makes it an ideal proving ground for atmospheric correction techniques that must ultimately work worldwide.

The broader significance of the study lies in its framing of GNSS- and GACOS-based corrections as complementary rather than competing tools. Because they draw on fundamentally different information sources—direct physical measurements of atmospheric delay at ground stations versus model-derived estimates from meteorological data—their residuals behave differently. Comparing them side by side provides a way to identify atmospheric contributions embedded in interferometric phase and to assess the uncertainty of InSAR-derived earthquake deformation fields. For hazard assessment, volcano monitoring, and land subsidence studies, where InSAR increasingly underpins operational decision-making, this kind of uncertainty quantification is becoming essential.

The work also fits into a vibrant international research effort. Recent studies have compared GNSS local observations with global weather-based models over tropical volcanoes, applied GNSS spatial interpolation for atmospheric correction in New Zealand, and used neural networks trained on GNSS delays for terrain-challenging regions. The Turkish study adds a critical data point from an active continental strike-slip setting, demonstrating that the performance hierarchy of correction methods is context-dependent. As Sentinel-1 and future radar missions deliver ever more frequent global coverage, and as GNSS networks densify, the message from Elazığ–Sivrice is clear: the atmosphere will always leave its fingerprint on radar interferograms, and taming it demands both better correction products and smarter validation of when, where, and how much those products actually help.

Subject of Research: Comparative assessment of GNSS- and GACOS-based tropospheric corrections for InSAR-derived surface deformation from the 2020 Elazığ–Sivrice earthquake

Article Title: Comparative assessment of GNSS- and GACOS-based tropospheric corrections for InSAR-derived surface deformation: a case study of the 2020 Elazığ–Sivrice earthquake

Article References: Ünlütürk, N. T., & Doğan, U. (2026). Comparative assessment of GNSS- and GACOS-based tropospheric corrections for InSAR-derived surface deformation: a case study of the 2020 Elazığ–Sivrice earthquake. Earth Science Informatics, 19(10), Article 182. https://doi.org/10.1007/s12145-026-02236-1

Image Credits: AI Generated

DOI: 10.1007/s12145-026-02236-1

Keywords: InSAR, GNSS, GACOS, tropospheric correction, surface deformation, Elazığ–Sivrice earthquake, Sentinel-1A, DInSAR, East Anatolian Fault Zone, SNAPHU, atmospheric delay, Earth Science Informatics