Reactivated landslide at Köprülü: multi-sensor insights for slope risk decisions
Reviewed by Tom Sullivan

First reported on Geoengineer.org – News
30 Second Briefing
A 21,000 m² reactivated landslide threatening the settlement of Köprülü in northeastern Turkey forced engineers to combine InSAR, GNSS, inclinometers, piezometers and detailed geomorphological mapping to understand rapidly accelerating ground deformation and building cracking. The team distinguished an active deep-seated slide from adjacent dormant and secondary movements, using displacement rates and groundwater data to refine the failure surface geometry and kinematics. This combined analysis directly informed whether to pursue large-scale stabilisation works or managed relocation, illustrating how multi-sensor monitoring can de-risk high-consequence decisions on inhabited slopes.
Technical Brief
- Research methods integrated remote sensing, geotechnical instrumentation and field mapping within a unified GIS environment.
- Findings mainly apply to similar clay-rich, deep-seated landslides with accessible borehole instrumentation and satellite coverage.
Our Take
Turkey appears only sporadically in our Hazards coverage, so a detailed failure analysis here gives practitioners a relatively rare reference point for slope behaviour and regulatory response in this specific geotechnical and permitting environment.
Within the 40 Hazards stories in our database, most failure pieces focus on operating mines or tailings facilities; a Turkey-based reactivated landslide framed under Projects and Research suggests lessons that are likely transferrable to early-stage design and route selection for linear infrastructure as well as mine slopes.
The emphasis on combined analysis for a reactivated failure aligns with a subset of Safety-tagged items in our coverage where back-analysis of past movements has materially changed design acceptance criteria, signalling that operators are increasingly expected to integrate historical instability data into go/no-go decisions rather than treating it as background noise.
Prepared by collating external sources, AI-assisted tools, and Geomechanics.io’s proprietary mining database, then reviewed for technical accuracy & edited by our geotechnical team.
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