Oil shows from the thermal springs of the Uzon volcano caldera have been studied by gas chromatography–mass spectrometry methods. Based on the composition and distribution of biomarker molecules, their genetic identity with the organic matter of Pliocene–Quaternary deposits has been established. It has been shown that the Uzon caldera is a unique natural laboratory of the present-day oil formation from the organic matter of Pliocene–Quaternary sediments. It has been stated that attempts to consider the compounds forming these oil shows as a product of hydrothermal abiogenic synthesis are absolutely unfounded.

Abstract—We discuss the water!level variations in the E!1 well for the time period between May 2006 and
2010, inclusive. A trend towards an increasing level at an abnormally high rate occurred from mid!2006 to
December 2009. This increase is regarded as the response of the aquifer of gas!saturated ground water that
exists in the volcanogenic–sedimentary deposits of the Avacha volcano!tectonic depression to volumetric
strain changes during the precursory period and the occurrence of a swarm of small earthquakes ( = 8.3)
in the area of Koryakskii Volcano and to its phreatic eruption. We estimated the volumetric compression as
Δε = –(4.1 × 10–6–1.5 × 10–5) from the amplitude of water!level rise using the elastic parameters of the wa!
ter!saturated rocks. While the strain source was active, we observed a decreasing sensitivity of the hydrologic
regime in the well to the precursory processes before large (M ≥ 5.0) tectonic earthquakes.

Currently, video observation systems are actively used for volcano activity monitoring. Video cameras allow us to remotely assess the state of a dangerous natural object and to detect thermal anomalies if technical capabilities are available. However, continuous use of visible band cameras instead of special tools (for example, thermal cameras), produces large number of images, that require the application of special algorithms both for preliminary filtering out the images with area of interest hidden due to weather or illumination conditions, and for volcano activity detection. Existing algorithms use preselected regions of interest in the frame for analysis. This region could be changed occasionally to observe events in a specific area of the volcano. It is a problem to set it in advance and keep it up to date, especially for an observation network with multiple cameras. The accumulated perennial archives of images with documented eruptions allow us to use modern deep learning technologies for whole frame analysis to solve the specified task. The article presents the development of algorithms to classify volcano images produced by video observation systems. The focus is on developing the algorithms to create a labelled dataset from an unstructured archive using existing and authors proposed techniques. The developed solution was tested using the archive of the video observation system for the volcanoes of Kamchatka, in particular the observation data for the Klyuchevskoy volcano. The tests show the high efficiency of the use of convolutional neural networks in volcano image classification, and the accuracy of classification achieved 91%. The resulting dataset consisting of 15,000 images and labelled in three classes of scenes is the first dataset of this kind of Kamchatka volcanoes. It can be used to develop systems for monitoring other stratovolcanoes that occupy most of the video frame.

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.

KUDRIAVY volcano on Iturup island in the Kuril arc is an active calc-alkaline volcano. It has not erupted this century; its current volcanic activity is characterized by hot (up to 910oC) gas jets which have been stable for at least 30 years. The composition of the gaseous emissions is typical of high-temperature fumaroles, but we report here the discovery of unusual subsurface sublimates associated with one gas jet—a sulphide mineral containing rhenium as the only cation. To our knowledge, this is the first reported occurrence of a pure rhenium mineral. The concentration of rhen-ium in the fumarole gas is only 2–10 p.p.b., so the condensation of pure rhenium sulphide from this gas requires both enrichment of rhenium by eight orders of magnitude and remarkable selectivity. Rhenium is generally believed to exist in only trace amounts at the Earth's surface, but our findings demonstrate that it can be readily mobilized, dispersed and concentrated by degassing magmas.

A high-temperature (up to 940°C) fumarolic activity at Kudriavy volcano had been studied during 1990–1999. The maximum gas temperatures of the fumaroles were measured in 1992 as 940°C, then gradually decreased with time and reached to 907°C in 1999. Gas composition of the high-temperature fumarole became enriched in H2O and depleted in other gas components, in particular in CO2. Hydrogen isotopic compositions of the high-temperature fumarolic gases were gradually depleted in deuterium. The gradual and continuous decrease in temperature and changes in gas composition observed during the last 10-year suggest that a magmatic melt have been degassing in a relatively steady-state manner from a single magma chamber. The detail investigations in 1998 and 1999 revealed short-term changes in gas composition characterized by sporadic increases in H2, CO2, and Stotal after intense precipitations. Small-scale eruptions occurred on October 7, 1999 at the summit. The ratios of major gas components (C/S, C/Cl, S/Cl, C/F, S/F, and Cl/F) significantly increased just prior to the eruption. The eruption at the Kudriavy volcano in 1999 was likely a phreatic eruption as a result of the intense precipitations after unusually long dry period. Meteoric water penetrated into the hot zone of volcano edifice and rapidly boiled causing the eruption.