Klyuchevskoy Volcano. Bibliography
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Kirianov V.Yu. Volcanic Ash in Kamchatka as a Source of Potential Hazard to Air Traffic // Volcanic Ash and Aviation Safety: Proc. First Intern. Symp. on Volcanic Ash and Aviation safety. US Geological Survey Bull. US Geological Survey. 1994. Vol. 2047. P. 57-63. https://doi.org/10.3133/b2047.
Kiryukhin A. V., Bergal-Kuvikas Olga, Lemzikov M.V., Zhuravlev N. B. Magmatic system of the Klyuchevskoy volcano according to seismic data and their geomechanical interpretation // Journal of Mining Institute. 2023. № 263. P. 698-714.
Kiryukhin A.V., Bergal-Kuvikas Olga, Lemzikov M.V. Magmatic activity of Klyuchevskoy volcano triggering eruptions of Bezymianny volcano based on seismological and petrological data // Journal of Volcanology and Geothermal Research. 2023. doi: 10.1016/j.jvolgeores.2023.107892.
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.
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.
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.
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.
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.
Koulakov Ivan, Gordeev Evgeniy I., Dobretsov Nikolay L., Vernikovsky Valery A., Senyukov Sergey, Jakovlev Andrey, Jaxybulatov Kayrly Rapid changes in magma storage beneath the Klyuchevskoy group of volcanoes inferred from time-dependent seismic tomography // Journal of Volcanology and Geothermal Research. 2013. Vol. 263. P. 75 - 91. doi: 10.1016/j.jvolgeores.2012.10.014.
We present the results of time-dependent local earthquake tomography for the Kluchevskoy group of volcanoes in Kamchatka, Russia. We consider the time period from 1999 to 2009, which covers several stages of activity of Kluchevskoy and Bezymianny volcanoes. The results are supported by synthetic tests that recover a common 3D model based on data corresponding to different time windows. Throughout the period, we observe a robust feature below 25 km depth with anomalously high Vp/Vs values (up to 2.2). We interpret this feature as a channel bringing deep mantle materials with high fluid and melt content to the bottom of the crust. This mantle channel directly or indirectly determines the activity of all volcanoes of the Kluchevskoy group. In the crust, we model complex structure that varies over time. During the pre-eruptive period, we detected two levels of potential magma storage: one in the middle crust at 10–12 km depth and one close to the surface just below Kluchevskoy volcano. In 2005, a year of powerful eruptions of Kluchevskoy and Besymiyanny volcanoes, we observe a general increase in Vp/Vs throughout the crust. In the relaxation period following the eruption, the Vp/Vs values are generally low, and no strong anomalous zones in the crust are observed. We propose that very rapid variations in Vp/Vs are most likely due to abrupt changes in the stress and deformation states, which cause fracturing and the active transport of fluids. These fluids drive more fracturing in a positive feedback system that ultimately leads to eruption. We envision the magma reservoirs beneath the Kluchevskoy group as sponge-structured volumes that may quickly change the content of the molten phases as fluids pulse rapidly through the system.
Krasheninnikov Stepan, Portnyagin Maxim, Ponomareva V.V., Bergal-Kuvikas Olga, Mironov Nikita Periodic volcanic activity of Klyuchevskoy and Ushkovsky volcanoes during the early Holocene inferred from tephra study 2009.
Lees J., Symons N., Chubarova O., Gorelchik V., Ozerov A. Tomographic Images of Klyuchevskoy Volcano P-Wave Velocity / Volcanism and Subduction: The Kamchatka Region. Geophysical Monograph Series. Washington, D. C.: American Geophysical Union. 2007. Vol. 172. P. 293-302.
Three-dimensional structural images of the P-wave velocity below the edifice of the great Klyuchevskoy group of volcanoes in central Kamchatka are derived via tomographic inversion. The structures show a distinct low velocity feature extending from around 20 km depth to 35 km depth, indicating evidence of magma ponding near the Moho discontinuity. The extensive low velocity feature represents, at least to some degree, the source of the large volume of magma currently erupting at the surface near the Klyuchevskoy group.
Manevich A.G., Girina O.A., Melnikov D.V., Nuzhdaev A.A. 2016-2017 explosive eruptions of Kamchatka volcanoes based on KVERT data // JKASP-2018. Petropavlovsk-Kamchatsky: IVS FEB RAS. 2018.