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May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Numerical simulation of ground‐penetrating radar data for studying the geometry of fault zone

Near Surface Geophysics. 2021. Vol. 19. No. 2. P. 261–277.
Bricheva S., Dubrovin I., Lunina O., Denisenko I., Matasov V., Turova I., Entin A., Panin A., Deev E.

Palaeoseismology studies the footprints of ancient earthquakes to improve the knowledge about the modern seismicity of the territory. A ground-penetrating radar (GPR), among other geophysical methods, is used for quick determination of shallow stratigraphy – displaced, oblique layers within the fault zone. GPR data interpretation from diverse and complex reflection patterns of the fault zone heavily depends on the interpreter's experience. The range of different fault zone parameters in which this method can be successfully applied has not yet been investigated. We used a numerical simulation of GPR data to determine how GPR images the elements of faults (fault plane, hanging wall, footwall) in comparison with other reflections. Furthermore, we studied which parameters have the most significant impact on GPR wave patterns. We performed a series of numerical models of a fault, changing its geometry with increasing complexity from elementary models to realistic ones. The resulting synthetic profiles allowed finding specific GPR signatures from the fault plane, the hanging wall and the footwall. We collected field GPR data from two different fault zones and examined them for verification.

Research target: Earth Sciences Physics
Language: English
DOI
Text on another site
Keywords: earthquakesGPR
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