Vol 25, No 1 (2025)

Rare-metal pegmatites containing industrial concentrations of lithium, tantalum, niobium, cesium, beryllium, and tin are becoming increasingly important as raw material objects for the production of electrical equipment, batteries, and electronics. In this context, studying the patterns of their geological and structural setting is highly relevant, as it allows the identification of prospecting criteria to discover new resource-bearing sites and expand the reserves of known rare-metal pegmatite deposits in the Nuristan zone of northeastern Afghanistan. Here, pegmatite fields are spatially associated with the Oligocene granites of the Laghman complex and are located in the provinces of Afghanistan: Nuristan, Kunar, Laghman, Kabul, Nangarhar, Kapisa, Panjshir, and Badakhshan. They have been known since ancient times as sources of gemstones and mica. The Laghman granitoid complex is characterized by the sequential intrusion of three intrusive phases: 1 – diorites, quartz diorites, granodiorites, and plagiogranites; 2 – porphyritic biotite granites, amphibolebiotite granites, and granodiorites; 3 – two mica and biotite granites, granite porphyries, aplite, and pegmatoid granites. Rare-metal pegmatites are confined to the third phase of the Laghman granite intrusion. Here, granites are characterized by heterogeneous texture, from granoblastic to granitic, with fragments of aplitic and poikilitic texture. The host rocks of pegmatites are Triassic phyllite-like quartz-mica schists and Proterozoic gneisses and quartzites. Additionally, pegmatites are found within intrusive rocks, specifically within gabbro-diorites massifs of the Nilaw complex. The morphology of the bodies of rare-metal pegmatites is diverse. Veins with swells and plate-like bodies predominate, while lens-shaped forms and irregularly oriented veins are less common. The thickness of pegmatite veins ranges from 1 to 60 m, and their length varies from tens of meters to 2–5 km. It is assumed that the formation of the Laghman granitoid complex is a consequence of the collision of the Indian continent with Eurasia.

The article considers the Ossetian sector of the Greater Caucasus from the point of view of studying its seismicity and analyzing seismic hazard assessments previously constructed at all levels of seismic zoning (GSZ, DSZ and SMZ). The areas prone to strong earthquakes identified by pattern recognition methods are discussed. The results of the analysis emphasized the high level of seismic hazard of the foothill and mountainous areas of the region. The description of seismic networks that continuously monitor the region is given. The analysis of the most representative earthquake catalog with a unified magnitude scale that was previously created by the authors, and spatial variations of the magnitude of full registration, showed that the level of registration of seismic events in the east of the Ossetian sector of the Greater Caucasus is still worse than in its central and western parts.

The verification of statistical models of sea surface elevations based on the decomposition of the wave profile into degrees of a small parameter (wave steepness) and in terms of multidimensional integrals of wave spectra was carried out. For verification, wave measurement data were used to calculate the skewness and excess kurtosis of surface elevations, as well as the distribution of crests and troughs. Two factors are identified that limit the use of estimates of skewness Aη and excess kurtosis Eη obtained from existing models. First, the model estimates Aη and Eη are always non-negative, although the measurement data show that the lower limit of the ranges in which the skewness and excess kurtosis change is in the region of negative values. Secondly, almost all existing models are one-parameter models, using wave steepness and wave age as predictors; whereas the measured data indicate that there is no clear relationship. The values of Aη and Eη vary greatly for fixed values of the predictors. Existing statistical models can only describe average changes Aη and Eη. This limits the scope of their application. The analysis of the probability density functions of the troughs FT h and crests FCr showed that the function calculated for Aη < 0 in the region above the distribution mode exceeds the values corresponding to the Rayleigh distribution, and the relationship FT h ≈ FCr holds. The second order nonlinear model is inconsistent with this result. Negative skewness values are observed much less frequently than positive ones, so the functions FT h and FCr calculated for the whole ensemble of situations are consistent with the second-order nonlinear model.

On the basis of statistical analysis, studies of the features of the distribution of typhoons in the Spratly Archipelago (the South China Sea) for the period 1884–2020 were carried out using satellite information. As a result, it was revealed that 229 typhoons were observed over the water area of the Spratly Islands during this period. Of these, 27 had a maximum speed of over 33 m/s. A clear trend is identified towards an increasing number of typhoons, which could be a major source of regional climate risks in the future. This is probably due to the intense development of the East Asian Monsoon with a strong tropical convergence zone under current climatic conditions.

Over the past 20 years, increasing temperature and receding ice-cover have led to changes in the Arctic ecosystem. Our study aims to create models that predict the position of high chlorophyll-a concentration (Chl-a) zones in the European Arctic Corridor (the Barents, Norwegian and Greenland Seas) to monitor these changes. Firstly, we use remotely sensed data to assess spatial and temporal changes in correlation between Chl-a and environmental parameters that could influence Chl-a in the region – Photosynthetically Active Radiation (PAR), Sea Surface Temperature (SST), Mixed Layer Depth (MLD) and Sea Surface Salinity (SSS) – over the 2010–2019 time period. We found significant correlation (∣r∣ = 0.6–0.8) between Chl-a and PAR and SST, and medium correlation (∣r∣ = 0.4–0.6) between Chl-a and SSS and MLD, correlation was highest during spring periods. Then, using a Random Forest Machine Learning algorithm in the Classifier modification, we created models for each sea to predict the position of high-productivity zones (Chl-a > 1 mg m−3) using environmental parameters. Our results suggested that Chl-a variability in the European Arctic Corridor is mostly determined by PAR (28–32% of Chl-a class variability), SST (25–29%), and SSS (26–31%); MLD played a lesser role (12–17%). According to validation, all the models showed high performance scores (F1-score = 66–95%) and slightly underestimated the total area of high productivity.

A study of the latitudinal variation of the water cooling process along the eastern coast of the Baltic Sea in 2016 and 2018 was carried out based on field and reanalysis data. The latitudinal features of the anomalous cooling regime, previously noted off the coast of the Curonian Spit (Kaliningrad oblast, Russian Federation), were shown. Estimates of trends in decreasing coastal water temperatures were obtained for typical and anomalous cooling regimes. The role of a water temperature of 9.5 ∘C on the cooling rate of coastal waters is shown. Reaching this temperature from northeast to southwest occurs with a delay of about a month.

The content, distribution and speciation of gold in ores of the Natalka deposit (North East Russia) were studied. According to atomic absorption spectrometry (AAS), the vein and veinlet-vein ores are highest grade in gold, whereas veinlet-disseminated ores are lower grade and disseminated ores are poor in gold. According to light microscopy and electron probe microanalysis, up to 85% of gold in the Natalka ores is represented by large and small grains of free native gold associated with gangue and sulfide minerals. The gold grains of 0.01 to 2 mm in size are dominated and their fineness vary from 720 to 860 ‰. Up to 20% of native gold is represented by finely dispersed particles < 0.01 mm in size and a fineness of 750–990‰. Most of this gold is fixed and bounded mainly to with sulfides. According to “phase” chemical analysis with AAS, arsenopyrite is richest in gold whereas pyrite is poorer in gold. Using AAS with analytical data selections for single crystals, two non-mineral forms of “invisible” gold were found in these sulfides, namely the structurally bound (structural) and surficially bound (surficial) forms. The structural gold is incorporated into the mineral structure. The surficial form is confined to nano-sized non-autonomous phases on the sulfide mineral surfaces and often dominates over the structural form. The maximum gold concentrations on the surface of arsenopyrite and pyrite were confirmed by LA-ICP-MS data. It is expected that not all “invisible” gold is a refractory gold. The major part of gold contained in arsenopyrite and pyrite as finely dispersed, micron- and submicron-sized particles, as well as the surficially bound gold, can be extracted with modification of current flowsheet, which enhances the value of the gold ores at the Natalka deposit.