We present a new dataset for whole-rock major, trace, isotopic, and phenocryst compositions indicating a genetic link between andesites of the Holocene eruptions of the Bezymianny stratovolcano (the Bezymianny stage), the andesitic to dacitic Late Pleistocene lava dome complex (the pre-Bezymianny stage), and the magnesian to high-alumina basalts of the adjacent Kliuchevskoi Volcano. We demonstrate that volcanic products from the Bezymianny stage of volcano evolution are most likely the products of magma mixing between silicic products of the earliest stages of magma fractionation and the less evolved basaltic andesite parental melts periodically injected into the magma reservoir. In contrast, the intermediate and silicic magmas of the pre-Bezymianny stage together with basalts from Kliuchevskoi much more closely resemble the liquid line of descent and may represent a unique prolonged and continuous calc-alkaline trend of magma evolution from high-magnesian basalt to dacite. As a result of the geothermobarometry, we recognize variable conditions of magma fractionation and magma storage beneath Bezymianny for different magma types during its evolution since the Late Pleistocene: (1) 1100–1150 °C, 500–640 MPa, 1–2.5 wt. H2O for parental basaltic andesite; (2) 1130–1050 °C, 700–600 MPa, 2.5–5 wt. H2O for two-pyroxene andesites; (3) 1040–990 °C, 560–470 MPa, 5–6.5 wt. H2O for orthopyroxene-bearing andesites; (4) 950–1000 °C, 450–150 MPa, 3.5–5.5 wt. H2O for hornblende-bearing andesites; and (5) 950–900 °C, 410–250 MPa, 6–7 wt. H2O for dacites. Repeated basalt injections and magma fractionation combined with internal mixing in the magma chamber are the main processes responsible for both the complex petrography and the geochemical trends observed in the lavas of Bezymianny Volcano.

Deep long-period (DLP) earthquakes observed beneath active volcanoes are sometimes considered as precursors to eruptions. Their origin remains, however, unclear. Here, we present a possible DLP generating mechanism related to the rapid growth of gas bubbles in response to the slow decompression of over-saturated magma. For certain values of the gas and bubble content, the elastic deformation of surrounding rocks forced by the expanding bubbly magma can be fast enough to generate seismic waves. We show that amplitudes and frequencies of DLP earthquakes observed beneath the Klyuchevskoy volcano (Kamchatka, Russia) can be predicted by our model when considering pressure changes of ~107 Pa in a volume of ~103–104 m3 and realistic magma compositions. Our results show importance of the deep degassing in the generation of volcanic seismicity and suggest that the DLP swarms beneath active volcanoes might be related to the pulses of volatile-rich basaltic magmas rising from the mantle.

One of the most important tasks when studying volcanic activity is to monitor their thermal radiation. To fix and assess the evolution of thermal anomalies in areas of volcanoes, specialized hardware-thermal imagers are usually used, as well as specialized instruments of modern satellite systems. The data obtained with their help contain information that makes it relatively easy to track changes in temperature and the size of a thermal anomaly. At the same time, due to the high cost of such complexes and other limitations, thermal imagers sometimes cannot be used to solve scientific problems related to the study of volcanoes. In the current paper, day/night video cameras with an infrared-cut filter are considered as an alternative to specialized tools for monitoring volcanoes’ thermal activity. In the daytime, a camera operated in the visible range, and at night the filter was removed, increasing the camera’s light sensitivity by allowing near-infrared light to hit the sensor. In that mode, a visible thermal anomaly could be registered on images, as well as other bright glows, flares, and other artifacts. The purpose of this study is to detect thermal anomalies on night images, separate them from other bright areas, and find their characteristics, which could be used for volcano activity monitoring. Using the image archive of the Sheveluch volcano as an example, this article presents the results of developing a computer algorithm that makes it possible to find and classify thermal anomalies on video frames with an accuracy of 98%. The test results are presented, along with their validation based on thermal activity data obtained from satellite systems.