Classification of sediments on the exposed banks of the Oka River using granulometric composition; Опыт классификации отложений на эрозионных берегах реки Оки по их гранулометрическому составу

An overview of the lithological diversity of soils at the bottom of the Oka River valley, particularly its Ryazan section, was performed. In a semi-stationary study of the geomorphic processes, a total of 231 soil samples were collected from the channel slopes with distinct erosion patterns. The geological data were supplemented by topographic mapping with unmanned aerial vehicles (UAVs), during which the boundaries of sedimentary facies on the exposed banks of the semi-stationary areas were identified and delineated in the GIS products. Granulometric analysis by the hydraulic and sieving methods, along with the analysis of the distribution of coarse clastic material within the geological strata, was carried out to determine the mechanical composition of soils on the Oka River banks. Based on the ratio of sand, silt, and clay measured through clustering and machine learning, the fine clastic soils were classified into four to five homogeneous groups. Four granulotypes of floodplain sections, each with a distinct occurrence of glacial and alluvial facies, can serve as a valuable geological and geomorphological element for applied modeling in regional estimates of horizontal channel deformation rates. © 2025 Elsevier B.V., All rights reserved.

Авторы
Vorobyov Aleksey Yu 1 , Kadyrov Aleksander S. 1 , Burgov E.V. 2, 3 , Lokteev Dmitry S. 4 , Balobina Anna A. 5
Издательство
Казанский (Приволжский) федеральный университет
Номер выпуска
1
Язык
Russian
Страницы
154-180
Статус
Published
Том
167
Год
2025
Организации
  • 1 Ryazan State University named for S. Yesenin, Ryazan, Russian Federation
  • 2 National Research Centre "Kurchatov Institute", Moscow, Russian Federation
  • 3 A.N. Severtsov Institute of Ecology and Evolution Russian Academy of Sciences, Moscow, Russian Federation
  • 4 Moscow State University of Geodesy and Cartography, Moscow, Russian Federation
  • 5 RUDN University, Moscow, Russian Federation
Ключевые слова
floodplain; geological facies; granulometric analysis; machine learning; Oka River; soil; UAV
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