employee
NUST MISiS (College of Mining, lecturer)
employee
Moscow, Russian Federation
employee
employee
employee
Schmidt Institute of Physics of the Earth, Russian Academy of Sciences (labratory of geoinformatics, junior researcher)
employee
employee
employee
Schmidt Institute of Physics of the Earth, Russian Academy of Sciences (labratory of geoinformatics, leading researcher)
doctoral candidate
VAC 1.6.20 Геоинформатика, картография
VAC 1.6.21 Геоэкология
VAC 1.6 Науки о Земле и окружающей среде
UDK 551.242 Тектонические движения, колебания и т. п. Тектонические структуры земной коры и их элементы
UDK 550.34.016 Экспериментальные исследования: измерения, опыты, полевые эксперименты
UDK 55 Геология. Геологические и геофизические науки
UDK 550.34 Сейсмология
UDK 550.383 Главное магнитное поле Земли
GRNTI 37.31 Физика Земли
GRNTI 37.01 Общие вопросы геофизики
GRNTI 37.15 Геомагнетизм и высокие слои атмосферы
GRNTI 37.25 Океанология
GRNTI 38.01 Общие вопросы геологии
GRNTI 36.00 ГЕОДЕЗИЯ. КАРТОГРАФИЯ
GRNTI 37.00 ГЕОФИЗИКА
GRNTI 38.00 ГЕОЛОГИЯ
GRNTI 39.00 ГЕОГРАФИЯ
GRNTI 52.00 ГОРНОЕ ДЕЛО
OKSO 05.06.01 Науки о Земле
BBK 26 Науки о Земле
TBK 63 Науки о Земле. Экология
BISAC SCI SCIENCE
There are numerous methods for modeling velocity fields of the Earth’s crust. However, only a few of them are capable of modeling data beyond the contour of the geodetic network (extrapolating). Spatial modeling based on a neural network approach allows for the adequate modeling of the field of recent crustal movements and deformations of the Earth’s crust beyond the geodetic network contour. The study extensively examines the hyperparameter settings and justifies the applicability of the neural network model for predicting crustal movement fields using the Ossetian geodynamic polygon as an example. The presented results, when compared to classical modeling methods, demonstrate that the neural network approach confidently yields results no worse than classical methods. The results of modeling for the Ossetian polygon can be used for geodynamic zoning, identification zones of extension and compression, computing the tectonic component of stresses, and identifying areas of high-gradient displacements.
velocity fields, resent crustal movements, spatial modeling, regular grid, extrapolation, interpolation, artificial neural networks
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