Modelling the Horizontal Velocity Field of the Nizhne-Kansk Massif According to GNSS Observations

Abstract

Within the boundaries of the Nizhne-Kansk granite-gneiss massif, which directly borders on the Atamanovskiy branch of the Yenisei Ridge, the building of an underground research laboratory for validating the safety of disposal of high-level radioactive waste began in 2019. In 2010, researchers of the Mining and Chemical Combine at Zheleznogorsk and the Geophysical Center, Russian Academy of Sciences, organized a satellite geodetic network within the boundaries of the Nizhne-Kansk massif; this network included 30 GNSS stations intended for observations of modern crustal movements.The purpose of this study is to determine vectors and simulate the field of horizontal modern crustal movements from measurements made in 2010 - 2019. The tasks included: creating a catalogue of displacement data; calculating and estimating horizontal velocities of modern crustal movements, modelling the horizontal velocity field using artificial neural networks, developing a kinematic model of the area and comparing it with geological survey data.As a result, the resulting model was found to be in good agreement with the results of structural-geological and geodynamic studies in the area. The rate of convergence between the Siberian Platform and the West Siberian Plate in the interaction zone of the southern part of the Yenisei Ridge can be estimated as 2-4 mm/year. The movements of the selected area are due to sublatitudinal compression along an azimuth of 100-110 degrees. Within the selected tectonic blocks relatively low rates of modern horizontal movements of the earth's crust were obtained, which confirms the stable geodynamic regime of the structural block hosting the underground research laboratory. Thus, the results of the work demonstrate the possibility of disposal of high-level radioactive waste in the selected structural block.

Modeling the Horizontal Velocity Field of the Earth’s Crust in a Regular Grid from GNSS Measurements

“…They are universal for generating gridded data for movement and deformation fields regardless of the studied geological process or phenomenon. These methods include geostatistical methods [Bogusz et al, 2013;Ghiasi and Nafisi, 2015], distance-weighting methods [Bogusz et al, 2013;Shen et al, 1996Shen et al, , 2015, spline and polynomial methods [Bogusz et al, 2013;Sandwell, 1987], machine learning methods [Aleshin et al, 2022;Grishchenkova, 2017;Manevich et al, 2021;Manevich and Tatarinov, 2017;Tatarinov et al, 2018], and others.…”

“…The most prominent among them is the artificial neural network (ANN) method. Experience in its application is known for predicting ground subsidence caused by mining activities [Boubou et al, 2010;Grishchenkova, 2017], modeling post-seismic deformations [Yamaga and Mitsui, 2019], forecasting landslide movements [Yang et al, 2019], volcanic deformations [Anantrasirichai et al, 2018], and slow tectonic movements fields [Manevich et al, 2021;Manevich and Tatarinov, 2017;Tatarinov et al, 2018].…”

“…Classical methods of geographical interpolation that depend on reference points (kriging, the inverse distance method) do not allow for data extrapolation. Methods of machine learning, particularly artificial neural networks, show a good perspective in this regard [Boubou et al, 2010;Grishchenkova, 2017;Manevich et al, 2021;Manevich and Tatarinov, 2017;Tatarinov et al, 2018], and their application will be further discussed.…”