As part of the international collaboration of several research groups from Russia, France, and Germany, 77 temporary seismic stations were installed in the summer of 2015 for one-year period to conduct a detailed study of the deep structure of the Earth’s crust and upper mantle in the region of the Klyuchevskoi Volcano Group (KGV) in the Kamchatka Peninsula. One of the results of the KISS experiment (Klyuchevskoi Investigation – Seismic Structure of an extraordinary volcanic system) was the final catalog of the joint data from the temporary stations and the permanent network of the Kamchatka Branch of the Geophysical Survey of the Russian Academy of Sciences (KB GS RAS). The catalog comprises 2136 events, including 560 for which the permanent network catalog lacked sufficient data for correct processing. The catalog in .xlsx format and the station bulletin in .isf format are presented in the supplementary material to the paper. A comparative analysis was conducted on the joint solutions of two catalogs: one obtained solely from the data of the KB GS RAS permanent network stations and another from a denser seismic network integrated with KISS stations.

КАТАЛОГ ЗЕМЛЕТРЯСЕНИЙ ПО ДАННЫМ СЕТИ KISS В 2015-2016 гг.

Системы оперативного оповещения о сейсмических событиях функционируют во всех развитых странах мира и особенную важность они имеют для сейсмоактивных регионов. Информация о параметрах очага землетрясения оперативно используется при планировании работ по ликвидации ЧС. В случае сильного землетрясения особую важность приобретает информация об интенсивности колебаний грунта на территории, попавшей под удар стихии. Наличие таких данных позволяет сравнительно просто превратить систему оповещения в систему прогнозирования последствий землетрясений. Такая система, в свою очередь, позволяет спланировать спасательные работы при разрушительных землетрясениях. В этом случае максимально детальные данные экономят время, следовательно повышают шансы на спасение людей, оказавшихся под завалами. В случае умеренных землетрясений позволяют сэкономить трудовые и материальные затраты служб на обследование зданий и сооружений за счет детальной картины сейсмических воздействий. Классическая Служба срочных сейсмических донесений обеспечивает информацию лишь об основных параметрах землетрясения, которые требуют дополнительной интерпретации.

Вулкан Ключевской – самый активный и мощный базальтовый вулкан Курило-Камчатской вулканической области. Координаты вершины в программе «Google Планета Земля» - 56° 04' (56.062) с. ш. и 160° 38' (160.630) в. д. Абсолютная высота вулкана – 4750 м. Диаметр вершинного кратера, венчающего конус, составляет около 700 м [12]. Ключевской вулкан – типичный стратовулкан с конусом правильной формы. Это самый высокий из действующих вулканов Европы и Азии. Он сложен базальтовыми, андезибазальтовыми потоками лав и пирокластическим материалом. В результате геологических исследований (методом тефрохронологии) определен возраст вулкана ~ 7 тыс. лет [3]. Формирование вулкана началось в голоцене отложениями мощных толщ лав и пирокластики базальтового и андезибазальтового составов на склоны более древних вулкановгигантов Камень и Крестовский. Ключевской вулкан очень активен. Средний расход магмы 60 млн. т/год составляет половину ювенильных продуктов извержений всего Курило-Камчатского региона. Для эруптивной деятельности вулкана характерны вершинные и побочные извержения. Вершинные извержения обычно более продолжительные и имеют главным образом эксплозивный или эксплозивно-эффузивный характер.

Службу предупреждения о возможности цунами с момента организации этой службы в г. Петропавловск-Камчатский выполняет сектор «Петропавловск-Цунами» (Камчатский филиал ФИЦ ЕГС РАН), совместно с ФГБУ «Камчатское УГМС». Дежурный персонал сектора «Петропавловск-Цунами» осуществляет регистрацию и обработку землетрясений Камчатки, Дальнего Востока и мира в трех режимах работы, а именно: – отложенный режим работы (составление бюллетеня опорной станции «Петропавловск» [4]); – оперативный режим работы, как Служба срочных донесений (ССД); – оперативный режим работы, как Служба предупреждения о возможности цунами (СПЦ). Сеть сейсмологических наблюдений на Дальнем Востоке для задач СПЦ по состоянию на 2020 г. состоит из 5 опорных сейсмических станций (ОЦС), 6 вспомогательных сейсмических станций (ВЦС), 16 пунктов регистрации сильных движений (ПР СД), данные которых в режиме реального времени пересылаются в три региональных информационно-обрабатывающих центра (РИОЦ) ФИЦ ЕГС РАН «Петропавловск», «Южно-Сахалинск», «Владивосток» [3]. Кроме того, для работы привлекаются станции Камчатской региональной сейсмической сети, Сахалинской региональной сейсмической сети и сети GSN (IRIS, USGS). Общее количество станций, которые могут быть привлечены к обработке по регламентам СПЦ и ССД, насчитывает порядка 70 цифровых приборов в Дальневосточном регионе.

Длиннопериодные землетрясения и треморы, наравне с вулкано-тектоническими землетрясениями, являются одним из двух основных классов вулкано-сейсмической активности.Считается, что длиннопериодная вулканическая сейсмичность связана с колебаниями давления в магматической и гидротермальной системах под вулканами и поэтому может быть использована в качестве предвестника готовящихся извержений.В тоже время, физический механизм длиннопериодной сейсмичности остаётся не полностью понятым.В данной работе мы исследовали длиннопериодные землетрясения, происходящие на границе кора-мантия под Ключевской группой вулканов на Камчатке, с целью установить их закон повторяемости: связь между магнитудой и частотой событий.Данный тип землетрясений наиболее многочислен в изучаемом районе и характеризует состояние глубинного магматического резервуара, находящегося на границе кора-мантия.Изменения сейсмического режима в этой части магматической системы могут быть одним из ранних предвестников извержений.Для более полной характеризации закона повторяемости мы создали новый каталог глубоких длиннопериодных землетрясений на основе обработки непрерывных сейсмограмм, записанных сетью станций КФ ФИЦ ЕГС РАН в 2011-2012 годах, по методу согласованного фильтра.Также мы применили метод определения магнитуд этих землетрясений, приближенный к моментной шкале

Abstract—Long-period earthquakes and tremors, on a par with volcano-tectonic earthquakes, are one of two main classes of volcano-seismic activity. It is believed that long-period volcanic seismicity is associated with pressure fluctuations in the magmatic and hydrothermal systems beneath volcanoes and can therefore be used as a precursor of the impending eruptions. At the same time, the physical mechanism of the long-period seismicity is still not fully understood. In this work, we have studied the long-period earthquakes that occur at the crust–mantle boundary beneath the Klyuchevskoi volcanic group in Kamchatka in order to establish their recurrence law—the relationship between the magnitude and frequency of occurrence of the events. In the region under study, the earthquakes pertaining to this type are most numerous and characterize the state of the deep magma reservoir located at the crust–mantle boundary. The changes in the seismic regime in this part of the magmatic system can be one of the early precursors of eruptions. For a more thorough characterization of the frequency–magnitude relationship of the discussed events, we compiled a new catalog of the deep long-period earthquakes based on the matched-filter processing of continuous seismograms recorded by the network stations of the Kamchatka Branch of the Geophysical Survey of the Russian Academy of Sciences in 2011–2012. For these earthquakes, we also used a magnitude determination method that provides the estimates close to the moment magnitude scale. The analysis of the obtained catalog containing more than 40 000 events shows that the frequency–magnitude relationships of the earthquakes markedly deviate from the Gutenberg–Richter power-law distribution, probably testifying to the seismicity mechanism and peculiarities of the sources that differ from the common tectonic earthquakes. It is shown that the magnitude distribution of the deep long-period earthquakes is, rather, described by the distributions with characteristic mean values such as the normal or gamma distribution.

Abstract We present a method for automatic location of dominant sources of seismovolcanic tremor in 3‐D, based on the spatial coherence of the continuously recorded wavefield at a seismic network. We analyze 4.5 years of records from the seismic network at the Klyuchevskoy volcanic group in Kamchatka, Russia, when four volcanoes experienced tremor episodes. After enhancing the tremor signal with spectral whitening, we compute the daily cross‐correlation functions related to the dominant tremor sources from the first eigenvector of the spectral covariance matrix and infer their daily positions in 3‐D. We apply our technique to the tremors beneath Shiveluch, Klyuchevskoy, Tolbachik, and Kizimen volcanoes and observe the yearlong preeruptive volcanic tremor beneath Klyuchevskoy from deep to shallow parts of the plumbing system. This observation of deep volcanic tremor sources demonstrates that the cross‐correlation‐based method is a very powerful tool for volcano monitoring.

A new approach is proposed for determining earthquake hypocenters aimed at a more comprehensive characterization of its uncertainty and ambiguity. Application of the new approach to study the seismic focal subduction zones and volcanic seismicity is discussed by the example of the data of the Kamchatka Branch of the Geophysical Survey of the Russian Academy of Sciences.

We develop a network‐based method for detecting and classifying seismovolcanic tremors. The proposed approach exploits the coherence of tremor signals across the network that is estimated from the array covariance matrix. The method is applied to four and a half years of continuous seismic data recorded by 19 permanent seismic stations in the vicinity of the Klyuchevskoy volcanic group in Kamchatka (Russia), where five volcanoes were erupting during the considered time period. We compute and analyze daily covariance matrices together with their eigenvalues and eigenvectors. As a first step, most coherent signals corresponding to dominating tremor sources are detected based on the width of the covariance matrix eigenvalues distribution. Thus, volcanic tremors of the two volcanoes known as most active during the considered period, Klyuchevskoy and Tolbachik, are efficiently detected. As a next step, we consider the daily array covariance matrix's first eigenvector. Our main hypothesis is that these eigenvectors represent the principal components of the daily seismic wavefield and, for days with tremor activity, characterize dominant tremor sources. Those daily first eigenvectors, which can be used as network‐based fingerprints of tremor sources, are then grouped into clusters using correlation coefficient as a measure of the vector similarity. As a result, we identify seven clusters associated with different periods of activity of four volcanoes: Tolbachik, Klyuchevskoy, Shiveluch, and Kizimen. The developed method does not require a priori knowledge and is fully automatic; and the database of the network‐based tremor fingerprints can be continuously enriched with newly available data.

Continuous noise-based monitoring of seismic velocity changes provides insights into volcanic unrest, earthquake mechanisms and fluid injection in the subsurface. The standard monitoring approach relies on measuring traveltime changes of late coda arrivals between daily and reference noise cross-correlations, usually chosen as stacks of daily cross-correlations. The main assumption of this method is that the shape of the noise correlations does not change over time or, in other terms, that the ambient-noise sources are stationary through time. These conditions are not fulfilled when a strong episodic source of noise, such as a volcanic tremor, for example, perturbs the reconstructed Green's function. In this paper, we propose a general formulation for retrieving continuous time-series of noise-based seismic velocity changes without the requirement of any arbitrary reference cross-correlation function (CCF). Instead, we measure the changes between all possible pairs of daily cross-correlations and invert them using different smoothing parameters to obtain the final velocity change curve. We perform synthetic tests in order to establish a general framework for future applications of this technique. In particular, we study the reliability of velocity change measurements versus the stability of noise CCFs. We apply this approach to a complex data set of noise cross-correlations at Klyuchevskoy volcanic group (Kamchatka), hampered by loss of data and the presence of highly non-stationary seismic tremors.

Early warning systems are becoming increasingly important in the modern world. These systems combine several components: predictive systems (For example, tsunami warning systems), earthquake early warning systems, emergency message services, and systems of seismic damage monitoring. Information about shaking intensity becomes especially important in the case of a strong earthquake occurrence. These data are necessary for planning emergency rescue operations, but they are difficult to collect in a natural disasters situation because of possible communication problems. Application of data on instrumental seismic intensity may make it possible to solve this problem. Early warning systems predicting seismic intensity distributions just after the occurrence of an earthquake have already been developed in many seismically active regions of the world. Such a system also needs to be implemented in Kamchatka, where the strongest earthquakes can produce extremely high values of strong motion acceleration. As a result of the development of a system for seismological observation in Kamchatka, a unified specialized system for collection, transmission, archiving, and processing of seismic information was created. Seismological observations in Kamchatka were significantly improved with the update of the tsunami warning service in 2006–2011. As a result, a network of strong motion stations is currently operating in Kamchatka and can serve as a basis for creating a quasi-real-time seismic early warning system under the auspices the Kamchatka Branch of the Geophysical Survey, Russian Academy of Sciences (KB GS RAS). It uses data from strong motion stations to estimate the instrumental seismic intensity in quasi-real-time mode and visualizes the results. During the operational period while the service is being intensively used in the framework of the Seismic Early Warning Reports Tsunami Warning Service in the Kamchatka and Sakhalin branches of the GS RAS for real-time warning of interested parties about the shaking intensities at observation points, the technology implemented in this service has proved highly informative. In total, 75 messages on instrumental intensity in various places of Kamchatka krai and the northern Kuril Islands (Paramushir Islands) have been sent since the service was commissioned at the end of 2014. The currently operating version of the service has proved its informativeness and applicability for special departments of the Emergency Situations Ministry. In addition, real-time warning has improved coordination between the departments of KB GS RAS, and the results of this system are being used in a number of basic research projects. Further development of the service is related to the creation of denser instrumental networks to record strong ground motions and the transition to automatic decision-making and message sending.

The data from the seismic networks of the Kamchatka Branch of the Geophysical Survey of the Russian Academy of Sciences are used for calculating the cross correlations of seismic noise for the stationary digital stations over 2013 and for radio telemetric stations (RTS) in the region of the Klyuchevskoy volcano over the period from January 1, 2009 to May 31, 2013. Four hundred and two correlations overall are calculated. The fundamental-mode group velocities of the Rayleigh waves are calculated in the periods ranging from 5 to 50 s. The calculations for the region of the Klyuchevskaya group of volcanoes are based on the RTS data and cover the periods from 2 to 8 s. The two-dimensional (2D) maps of group velocity distributions in different periods are constructed with the use of the algorithm of surface wave tomography (Barmin, 2001). The velocity sections for the selected Kamchatka regions are reconstructed by the dispersion curve inversion technique (Mordret, 2014). For each region, the structure of the Earth's crust and upper mantle down to a depth of 50 km was obtained.