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Записей: 2735
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Kochegura V.V., Zubov A.G., Braytseva O.A. Magnetostratigraphy of Kamchatkan Holocene formations of soil and pyroclastics // Volcanology and Seismology. 1990. Vol. 8. № 6. P. 825-849.
   Аннотация
An account is given of magnetostratigraphic studies of Kamchatkan Holocene formations: the cover of soil and pyroclastics and the rocks of the cinder cones from the flank eruptions of Klyuchevskoi Volcano. А study was made of seven sections of the soil and pyroclastics and of samples from 17 cinder cones. А detailed account is given of the data processing procedure. Consideration is given to the reasons for the established incompleteness of the paleomagnetic record in the sections and it is demonstrated that adequately detailed reconstruction of the history of the geomagnetic 1ield is possible only provided that а study is made of а series of рагаllеl sections. The trajесtory of the geomagnetic field vector over the last 4000 years is determined on the basis of the material on radiocarbon datings. Seven cycles of paleosecular variations are distinguished in the age range investigated; each of these cycles has individual features by which they can be recognised and used for stratigraphic correlation. The, features taken were the direction of rotation of the vector, the shape and size of its loops, and the length of the cycles. Correlation of the sections based on paleomagnetic data was found to be in good agreement with the tephrostratigraphic correlation and enabled corrections to be made to the age of some horizons, including the archeological layers of the primitive settlement at Zhupanovo and the cinder cones. The metachronous magnetization present in some tephra layers was found to be an obstacle to any improvement in the accuracy and detail of magnetochronological reconstructions.
Koloskov A.V., Flerov G.B., Perepelov A.B., Melekestsev I.V., Puzankov M.Yu., Filosofova T.M. Evolution Stages and Petrology of the Kekuknai Volcanic Massif as Reflecting the Magmatismin Backarc Zone of Kuril-Kamchatka Island Arc System. Part 1. Geological Position and Geochemistry of Volcanic Rocks // Journal of Volcanology and Seismology. 2011. Vol. 5. № 5. P. 312-334. doi: 10.1134/S074204631104004X.
   Аннотация
The evolution of the Quaternary Kekuknai volcanic massif (the western flank of the Sredinnyi Range in Kamchatka) has been subdivided into five stages: (I) the pre-caldera trachybasalt- basaltic andes- ite, (2) the extrusive trachyandesite-trachydacite, (3) the early trachybasalt, (4) the middle hawaiite- mugearite (with occasional occurrences of basaltic andesites), and (5) the late trachybasalt-hawaiite- mugearite (with occasional andesites) of areal volcanism. On the basis of petrologic data we identified the island arc and the intraplate geochemical types of rocks in the massif. The leading part in petrogenesis was played by dynamics of the fluid phase with a subordinated role of fractional crystallization and hybridism. Successive saturation of rocks with the fluid phase in the course of melt evolution stopped at the time of caldera generation when most fluid mobile elements and silica had been extracted. The geological and petrologic data attest to the formation of the massif in the environment of a backarc volcanic basin during the beginning of rifting with active participation of mantle plume components.
Koloskov A.V., Flerov G.B., Perepelov A.B., Melekestsev I.V., Puzankov M.Yu., Filosofova T.M. The Evolutionary Stages and Petrology of the Kekuknai Volcanic Massif Reflecting the Magmatism in the Backarc Zone of the Kuril-Kamchatka Island Arc System. Part II. Petrologic and Mineralogical Features, Petrogenesis Model // Journal of Volcanology and Seismology. 2013. Vol. 7. № 2. P. 145-169. doi: 10.1134/S0742046313020048.
   Аннотация
The Kekuknai massif was formed in the course of tectono-magmatic activity that involved the origin of a shield volcano and a caldera depression with associated emplacement of extrusions that terminated in intense post-caldera areal volcanism. The mineralogical compositions of the massifs rocks have been considered in detail. The use of previously known and newly developed indicator properties of rock-forming minerals allowed the reconstruction of the general picture of the magmatic melt evolution and conditions of rock crystallization (various fluid and water saturation levels, as well as the oxidation state of the system). Essentially island-arc or intraplate characteristics of the massif s rock compositions are found at different stages of development of a single fluid-magmatic system. Decompression evolution of the parent deep-seated basanitic magma occurred via occurrence in intermediate magma chambers of daughter magmas of trachybasalt (pre-caldera stage) or hawaiite (areal volcanism) composition. Subsequent emanate-magmatic differentiation of these melts, combined with crystallization differentiation under changing P-T-f0l conditions, resulted in the formation of the entire diversity of the Kekuknai rocks.
Kontorovich A.E., Bortnikova S.B., Karpov G.A., Kashirtsev V.A., Kostyreva E.A., Fomin A.N. Uzon volcano caldera (Kamchatka): A unique natural laboratory of the present-day naphthide genesis // Russian Geology and Geophysics. 2011. Vol. 52. № 8. P. 768 - 772. doi: 10.1016/j.rgg.2011.07.002.
   Аннотация
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.
Kopylova G.N., Boldina S.V. Groundwater Pressure Changes Due to Magmatic Activation: Case Study of The E-1 Well, Kamchatka Peninsula, Russia // Geothermal Volcanology Workshop 2020. September 03-09, 2020, Petropavlovsk-Kamchatsky, Institute of Volcanology and Seismology. 2020.
Kopylova G.N., Boldina S.V. On the Relationships of Water-Level Variations in the E-1 Well, Kamchatka to the 2008–2009 Resumption of Activity on Koryakskii Volcano and to Large (M ≥ 5) Earthquakes // Journal of Volcanology and Seismology. 2012. Vol. 6. № 5. P. 312-328. doi: 10.1134/S074204631205003X.
   Аннотация
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.
Korolev S.P., Romanova I.M., Girina O.A., Sorokin A.A., Malkovsky S.I., Urmanov I.P. Software platform for volcano video monitoring // 10th Biennual workshop on Japan-Kamchatka-Alaska subduction processes (JKASP-2018). Petropavlovsk-Kamchatsky, Russia, August 20-26. Petropavlovsk-Kamchatsky: IVS FEB RAS. 2018. P. 117-119.
Korolev S.P., Sorokin A.A., Urmanov I.P., Kamaev A., Girina O.A. Classification of Video Observation Data for Volcanic Activity Monitoring Using Computer Vision and Modern Neural NetWorks (on Klyuchevskoy Volcano Example) // Remote Sensing. 2021. Vol. 13. Vol. 23. № 4747. P. 1-20. https://doi.org/10.3390/rs13234747.
   Аннотация
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.
Korolev S.P., Urmanov I.P., Kamaev A., Girina O.A. Parametric Methods and Algorithms of Volcano Image Processing / Software Engineering Perspectives in Intelligent Systems. Advances in Intelligent Systems and Computing. Cham: Springer. 2020. Vol. 1295. P. 253-263. https://doi.org/10.1007/978-3-030-63319-6_22.
   Аннотация
A key problem of any video volcano surveillance network is an inconsistent quality and information value of the images obtained. To timely analyze the incoming data, they should be pre-filtered. Additionally, due to the continuous network operation and low shooting intervals, an operative visual analysis of the shots stream is quite difficult and requires the application of various computer algorithms. The article considers the parametric algorithms of image analysis developed by the authors for processing the shots of the volcanoes of Kamchatka. They allow automatically filtering the image flow generated by the surveillance network, highlighting those significant shots that will be further analyzed by volcanologists. A retrospective processing of the full image archive with the methods suggested helps to get a data set, labeled with different classes, for future neural network training.
Korolev S.P., Urmanov I.P., Sorokin A.A., Girina O.A. Detecting Volcano Thermal Activity in Night Images Using Machine Learning and Computer Vision // Remote Sensing. 2023. Vol. 15. Vol. 19. № 4815. https://doi.org/10.3390/rs15194815.
   Аннотация
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.