The subsidence and thermal history of the Baikit anteclise sedimentary basin

The Riphean rocks of the Baikit anteclise have been examined using pyrolysis Rock–Eval 6 to evaluate the subsidence history and erosion level. The studied Riphean rocks have the МK₃–МK₄ catagenesis grade. Based on the catagenesis of organic matter we propose a model of maximum burial before the beginning of the accumulation of Vendian deposits. Estimated calculations of subsidence and erosion have shown that the assessed catagenesis grade could be reached at a depth of 7 km, while the erosion level was approximately 5–7 km.     

Fracturing in the high-carbon formation of the northern flank of the South-Tatar arch

The problem of studying the Domanik deposits and their reservoir potential is among the most important ones. The discovery of hydrocarbon deposits in similar rocks at various depths, the considerable variability of reservoir properties, and ambiguity in establishing the values of the effective thickness set the task for researchers to estimate them more reliably. The high-carbon kerogenic-carbonate-siliceous stratum that is characterized by the development of differently-oriented systems of fractures is of outstanding interest.     

The upper Bajocian of the Crimean Mountains: Paleogeography and sedimentation conditions from palynological data

Based on the spore-pollen data, the mineral composition of clay rocks, analyzed structures and facies, and a general paleogeographic analysis, the sedimentation conditions and landscapes of islands located during the Late Bajocian in the region of the present-day the Crimean Mountains have been reconstructed. It is shown that the sublatitudinally elongated insular land zone had a width of 30 km, the heights of the islands were no more than 1 km, with steep mudflow-affected northern slopes and with an extensive river system on the southern slopes.     

A study on precursors leading to geomagnetic storms using artificial neural network

Space weather prediction involves advance forecasting of the magnitude and onset time of major geomagnetic storms on Earth. In this paper, we discuss the development of an artificial neural network-based model to study the precursor leading to intense and moderate geomagnetic storms, following halo coronal mass ejection (CME) and related interplanetary (IP) events. IP inputs were considered within a 5-day time window after the commencement of storm. The artificial neural network (ANN) model training, testing and validation datasets were constructed based on 110 halo CMEs (both full and partial halo and their properties) observed during the ascending phase of the 24th solar cycle between 2009 and 2014. The geomagnetic storm occurrence rate from halo CMEs is estimated at a probability of 79%, by this model.