High-quality geomagnetic measurements are widely used in both fundamental research of the magnetic field and numerous industrial applications. However, vector data measured by fluxgate sensors show a dependency on temperature due to sensitive coil core material and components of the sensor electronics. Here, we propose a new method for detecting and eliminating temperature dependence in magnetic observatory data. The method is designed to correct temperature drifts in variation vector magnetometer measurements when preparing quasi-definitive data according to an INTERMAGNET standard. A special feature of the method is the semi-automatic adjustment of localization intervals for temperature correction, which prevents boundary jumps and discontinuities in the course of sequential data processing over long intervals. The conservative nature of the approach implies the minimization of the original data amount subjected to correction. The described method is successfully applied in the routine monthly preparation of quasi-definitive data of the Saint Petersburg Observatory (IAGA-code SPG) and can be efficiently introduced at other magnetic observatories worldwide.

Arctic zone of the Russian Federation (AZRF) is the region of intensive economic development. In this regard, it is critical to give an adequate assessment of natural factors that may have a negative impact on the growing technological infrastructure. Rapid climate change effects show a significant influence on this activity, including the railway network development. Hence, the decision-making community requires relevant information on climatic variations that can put at hazard the construction and operation of railway facilities. This paper presents the analysis of climatic changes within the region of Central and Western Russian Arctic in 1980–2021. It was performed using the new electronic Atlas of climatic variations in main hydrometeorological parameters, created for the Russian Railways in 2023. This geoinformatic product includes about 400 digital maps reflecting the variability of seven climatic parameters over more than four decades within the studied region. These parameters are air temperature, total precipitation, wind speed, soil temperature, soil moisture content, air humidity, and snow cover thickness. The analysis of climatic maps and their comparison between selected periods showed spatial and temporal heterogeneity of climatic variations in this region. This justifies the feasibility of further research using additional analytical instruments, such as Hovmöller diagrams, time series graphs, etc. The implementation of advanced geoinformatic products in the practice of the Russian Railways will facilitate sustainable development of its infrastructure in rapidly altering climatic conditions.

The Arctic Zone of the Russian Federation is characterized by the rapid growth of the mining industry, aimed at the extraction of oil, gas, coal, and ores, including rare earth metals. Railways are essential in the transportation of these resources to different regions of Russia for processing and export. Part of the cargo delivery is performed via the Arctic ports connected to the railway network. Rapid climate change, including regional climate warming, is among the compromising factors for the operation of the Arctic transport infrastructure. System analysis of climatic processes and assessment of potential hazards that they may induce requires adequate geoinformation support. This paper presents the results of spatio-temporal variability analysis of various hydrometeorological parameters for selected railway mainlines within the Arctic region. For this purpose, a new geoinformation method based on the Hovmöller diagrams was elaborated. This tool is useful for representing climate dynamics along the specified railway mainlines over several decades. It allows us to determine railway sections affected by anomalous climatic conditions on the variable time scale. The presented Hovmöller diagrams proved to be an efficient instrument for the regional climate change representation. It might be quite useful for railway infrastructure maintenance, planning, operation, and adaptation.

We constrain lithospheric thickness across northeastern Eurasia using a new thermo-compositional model that jointly interprets seismic tomography and gravity data, including gravity gradients from the GOCE mission. This integrative approach provides a self-consistent threedimensional thermal structure of the lithosphere that incorporates compositional variations within lithospheric keels, yielding robust thickness estimates. The results demonstrate a strong link between lithospheric thickness and tectonic evolution. Archean and Proterozoic terranes such as the Siberian Craton and the eastern Fennoscandian Shield preserve thick keels (>200 km), reflecting early stabilization through melt depletion (and the interactions with mantle plumes and rifting episodes for the Siberian Craton), while the Timan–Pechora block also retains anomalously thick lithosphere, consistent with Paleozoic orogenic reworking and stabilization. The northeastern Barents Sea displays intermediate lithosphere (160 km to 180 km), likely representing a Proterozoic–Paleozoic fragment within the Arctic basement mosaic. In contrast, the Ural Orogen forms a sharp lithospheric boundary between the East European Craton and the thermally modified West Siberian Plate, which was profoundly affected by Mesozoic rifting and plume activity. East of the Verkhoyansk Range, lithospheric thickness decreases to less than 100 km in Phanerozoic terranes such as Chukotka, the Anadyr–Koryak Fold Belt, and Kamchatka, where subduction, terrane accretion, and arc magmatism maintain a hot, dynamic lithosphere. Overall, the lithospheric structure of northern Eurasia reflects the interplay of four fundamental processes: Archean craton stabilization, Paleozoic orogenesis, Mesozoic plume–rift modification, and ongoing Pacific subduction. These processes collectively shape the strong lateral contrasts that define the geodynamic framework of Eurasia

We present a data-driven model for tectonic zonation of the West Siberian Basin (WSB) based on K-means clustering applied to a multivariate geophysical dataset. The initial analysis incorporates lithospheric thickness, crustal thickness, sedimentary thickness, topography, surface heat flow, and S-wave velocity anomaly at 100 km depth. F-statistics and permutation feature importance analysis indicate that only four primary parameters are sufficient to achieve reliable zonation, yielding six clusters that correspond to distinct tectonic domains. These domains reflect the complex geodynamic evolution of the region, including Paleozoic accretion, Mesozoic rifting, and subsequent subsidence. Independent data on hydrocarbon field locations demonstrate that major oil accumulations are primarily associated with two of these clusters, supporting the validity of the approach. The resulting zonation provides a reproducible basis for lithospheric regionalization and resource assessment. This framework can be further developed by integrating supervised machine learning methods to predict a specific structure and thermal regimes in areas with limited data. The quantitative characterization of these domains provides an objective framework for future geodynamic models and resource assessments.