Figure - available from: Journal of Geophysical Research: Solid Earth
This content is subject to copyright. Terms and conditions apply.
Source publication
The Klyuchevskoy Volcanic Group is a cluster of the world's most active subduction volcanoes, situated on the Kamchatka Peninsula, Russia. The volcanoes lie in an unusual off‐arc position within the Central Kamchatka Depression (CKD), a large sedimentary basin whose origin is not fully understood. Many gaps also remain in the knowledge of the crust...
Citations
... Data processing was mainly car- Based on the KISS experiment data, a number of scientific results have been obtained and published to date. Ambient-noise cross-correlation tomography (Egorushkin et al., 2020;Green et al., 2020) has provided more detailed information about the near-surface part of the Earth's crust in the KVG region and the surrounding sedimentary basins. New structural models of the deep parts of the crust and upper mantle were constructed based on seismic body-wave tomography using combined KISS + KAGSR data Gordeev et al., 2020a;2020b;. ...
... The information about the locations, instruments, and operation period of the stations is presented in Fig. 1. More details about the KISS project are described in (Shapiro et al., 2017a;Green et al., 2020;Koulakov et al., 2020). The waveforms are available from the GEO-FON data site https://geofon.gfzpotsdam.de/under ...
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.
... Its origin is related to the unique tectonic setting at the corner between the Kuril-Kamchatka and Aleutian trenches. The enhanced supply of the melt from the mantle might be caused by the around-slab-edge asthenospheric flow (Levin et al., 2002;Yogodzinski et al., 2001) and related crustal extension (Green et al., 2020;Koulakov et al., 2020) or by fluids released from the thick, highly hydrated Hawaiian-Emperor crust subducted beneath this corner (Dorendorf et al., 2000). There is also evidence that the distinct volcanoes of KVG interact with each other on various time scales, affecting their steady state regimes and magma output (Coppola et al., 2021). ...
Volcanoes produce a variety of seismic signals and, therefore, continuous seismograms provide crucial information for monitoring the state of a volcano. According to their source mechanism and signal properties, seismo‐volcanic signals can be categorized into distinct classes, which works particularly well for short transients. Applying classification approaches to long‐duration continuous signals containing volcanic tremors, characterized by varying signal characteristics, proves challenging due to the complex nature of these signals. That makes it difficult to attribute them to a single volcanic process and questions the feasibility of classification. In the present study, we consider the whole seismic time series as valuable information about the plumbing system (the combination of plumbing structure and activity distribution). The considered data are year‐long seismograms recorded at individual stations near the Klyuchevskoy Volcanic Group (Kamchatka, Russia). With a scattering network and a Uniform Manifold Approximation and Projection (UMAP), we transform the continuous data into a two‐dimensional representation (a seismogram atlas), which helps us to identify sudden and continuous changes in the signal properties. We observe an ever‐changing seismic wavefield that we relate to a continuously evolving plumbing system. Through additional data, we can relate signal variations to various state changes of the volcano including transitions from deep to shallow activity, deep reactivation, weak signals during quiet times, and eruptive activity. The atlases serve as a visual tool for analyzing extensive seismic time series, allowing us to associate specific atlas areas, indicative of similar signal characteristics, with distinct volcanic activities and variations in the volcanic plumbing system.
... Phase velocities for Rayleigh waves are obtained from stacked Z-component cross correlations with the zero-crossing method (Aki, 1957;Ekström et al., 2009;Kästle et al., 2016). The pre-processing and phase-velocity extraction steps are slightly modified from Kästle et al. (2022) and Magrini et al. (2022) by including the idea of Green et al. (2020) in using monthly stacks. A more detailed description can be found in the supplement to this article. ...
The eastern Alpine crust has been shaped by the continental collision of the European and Adriatic plates beginning at 35 Ma and was affected by a major reorganization after 20 Ma. To better understand how the eastern Alpine surface structures link with deep seated processes, we analyze the depth‐dependent seismic anisotropy based on Rayleigh wave propagation. Ambient noise recordings are evaluated to extract Rayleigh wave phase dispersion measurements. These are inverted in a two step approach for the azimuthally anisotropic shear velocity structure. Both steps are performed with a reversible jump Markov chain Monte Carlo (rj‐McMC) approach that estimates data errors and propagates the modeled uncertainties from the phase velocity maps into the depth inversion. A two layer structure of azimuthal anisotropy is imaged in the Alpine crust, with an orogen‐parallel upper crust and approximately orogen‐perpendicular layer in the lower crust and the uppermost mantle. In the upper layer, the anisotropy tends to follow major fault lines and may thus be an apparent, structurally driven anisotropy. The main foliation and fold axis orientations might contribute to the anisotropy. In the lower crust, the N‐S orientation of the fast axis is mostly confined to regions north of the Periadriatic Fault and may be related to European subduction. Outside the orogen, no clearly layered structure is identified. The anisotropy pattern in the northern Alpine foreland is found to be similar compared to SKS studies which is an indication of very homogeneous fast axis directions throughout the crust and the upper mantle.
... Giv en their different yet complementar y sensitivities, integ rating the two methods could provide a more robust interpretation of subsurface features, particularly in heterogeneous settings such as volcanic environments Zhang et al. 2020 ;Chen et al. 2021 ). Previous studies where both methods have been performed separately have been done at Avacha, Russia Bushenkova et al. 2019 ), Katla, Iceland (Jeddi et al. 2016(Jeddi et al. , 2017, Toba, Indonesia (Stankiewicz et al. 2010 ;Jaxybulatov et al. 2014 ;Koulakov et al. 2016 ) and Kl yuche vsk oy, Russia (Koulak ov et al. 2011 ;Green et al. 2020 ;Koulakov et al. 2020b ;Egorushkin et al. 2021 ) volcanoes. The y hav e been used to study complex topog raphy (Har tzell et al. 2014 ), basin environments (Lehujeur et al. 2021 ), industrial seismic hazard assessments (Parolai et al. 2001 ) and also volcanic systems . ...
... Ho wever , we do see similarities to suggest that the proposed concept within this study holds true. Similarities between ambient noise and local earthquake imaging methods are observed in volcanic environments at Avacha Bushenkova et al. 2019 ), Katla (Jeddi et al. 2016(Jeddi et al. , 2017, Toba (Stankiewicz et al. 2010 ;Koulakov et al. 2016 ), Kl yuche vsko y (Koulako v et al. 2011 ;Green et al. 2020 ) and also in other tectonic environments (e.g. Lehujeur et al. 2021 ) where comparable velocity anomalies were found. ...
Irazú and Turrialba are a twin volcanic complex that marks a distinct stop in volcanism along the Central America volcanic arc. We present a new travel-time velocity model of the crust beneath Irazú and Turrialba volcanoes, Costa Rica, and interpret it considering the results of previous ambient noise tomographic inversions. Data were acquired by a temporary seismic network during a period of low activity of the Irazú-Turrialba volcanic complex in 2018-2019. Beneath the Irazú volcano, we observe low P-wave velocities (VP = 5 km s-1) and low velocity ratios (VP/VS = 1.6). In contrast, below the Turrialba volcano, we observe a low S-wave velocities (VS = 3 km s-1) and a high VP/VS (= 1.85) anomaly. We found that locations of low VP and VS anomalies (- 15 per cent) correspond well with shear wave velocity anomalies retrieved from ambient noise tomography. At shallower depths, we observe high VP and VS anomalies (+ 15 per cent) located between the summits of the volcanoes. Sub-vertical velocity anomalies are also observed at greater depths, with high VP and VS anomalies appearing at the lower limits of our models. We propose a complex structure of an intermediate magmatic reservoir, presenting multi-phase fluid states of a liquid-to-gas transition beneath Irazú and a juvenile store of magmatic fluid beneath Turrialba, while shallow fluid transport provides evidence of magmatic-hydrothermal interactions.
... Several goals can be achieved based on this methodology. Ambient Noise Tomography (ANT) (Obermann et al., 2016;Green et al., 2020) can be used to image velocity structure, while velocity variations (dv/v) (Sens-Schönfelder and Wegler, 2006;Lecocq et al., 2014) can be used to track changes in the velocity structure. Seismic signal detection (Soubestre et al., 2018) and source locations can be determined using smoothed cross-correlation functions (Soubestre et al., 2019(Soubestre et al., , 2021Caudron et al., 2022;González-Vidal et al., 2022). ...
... However, the isotope composition of the magmas seems to contradict these findings (Dorendorf et al., 2000;Kayzar et al., 2014). The deeper magma reservoir feeds several complex systems of smaller shallow magma chambers at about 5 km depth and, in the case of Klyuchevskoy, directly into the volcano (Green et al., 2020;Journeau et al., 2022;Shapiro et al., 2017). Koulakov et al. (2021) proposed the existence of a separate gas-filled chamber responsible for Bezymianny's explosive activity. ...
... Sens-Schönfelder, & Campillo, 2013), assuming a surface wave velocity of 1 km s (Green et al., 2020). Surface waves of the lowest analyzed frequency of 0.5 Hz are sensitive to velocity changes down to about 1,300 m. ...
... For Kamchatka, tomography revealed high ratios in shallow regions below the KVG, indicating a high water content in sediments of the CKD and in the volcanic edifices (Green et al., 2020;Ivanov et al., 2016;Koulakov et al., , 2021. Also, the presence of hydrothermal reservoirs is well documented (e.g., Kiryukhin et al., 2012;Taran, 2009), for which a higher sensitivity to ground shaking was observed (e.g., Caudron et al., 2022;Chaves & Schwartz, 2016;Taira & Brenguier, 2016). ...
Volcanic inflation and deflation often precede eruptions and can lead to seismic velocity changes (dv/v $dv/v$) in the subsurface. Recently, interferometry on the coda of ambient noise‐cross‐correlation functions yielded encouraging results in detecting these changes at active volcanoes. Here, we analyze seismic data recorded at the Klyuchevskoy Volcanic Group in Kamchatka, Russia, between summer of 2015 and summer of 2016 to study signals related to volcanic activity. However, ubiquitous volcanic tremors introduce distortions in the noise wavefield that cause artifacts in the dv/v $dv/v$ estimates masking the impact of physical mechanisms. To avoid such instabilities, we propose a new technique called time‐segmented passive image interferometry. In this technique, we employ a hierarchical clustering algorithm to find periods in which the wavefield can be considered stationary. For these periods, we perform separate noise interferometry studies. To further increase the temporal resolution of our results, we use an AI‐driven approach to find stations with similar dv/v $dv/v$ responses and apply a spatial stack. The impacts of snow load and precipitation dominate the resulting dv/v $dv/v$ time series, as we demonstrate with the help of a simple model. In February 2016, we observe an abrupt velocity drop due to the M7.2 Zhupanov earthquake. Shortly after, we register a gradual velocity increase of about 0.3% at Bezymianny Volcano coinciding with surface deformation observed using remote sensing techniques. We suggest that the inflation of a shallow reservoir related to the beginning of Bezymianny's 2016/2017 eruptive cycle could have caused this local velocity increase and a decorrelation of the correlation function coda.
... The only region of Kamchatka where systematic multiscale seismic tomography studies were performed is the Klyuchevskoy group of volcanoes (KGV) and surroundings (e.g., Green et al., 2020;Koulakov et al., 2020;Koulakov, 2022). This group includes 13 densely distributed active and dormant stratovolcanoes (Fedotov et al., 2010). ...
https://authors.elsevier.com/c/1hyXk1LkU3g7nO
The area of Central Kamchatka limited by latitudes of 52.5 and 54 degrees includes six active volcanoes (Avacha, Koryaksky, Zhupanovsky, Mutnovsky, Gorely and Opala), as well as a number of dormant and extinct stratovolcanoes, monogenic cones and large calderas. Furthermore, it contains the Malko-Petropavlovsk fracture zone (MPZ), which marks the boundary between two distinct subduction regimes to the south and to the north. We present a new seismic tomography model for this area, which was constructed based on the joint use of data of the Kamchatkan permanent seismic stations and a temporary network installed in the region in 2019-2020. A series of synthetic tests have demonstrated fair resolution of the derived seismic velocity structures in the crust and in the mantle wedge down to ~150 km. The distributions of the P and S wave velocities, and especially the Vp/Vs ratio, clearly highlight the connection between the volcanic centers in Central Kamchatka and the subducting slab. At depths below 40 km depth, we observe two large low-velocity anomalies centered below Zhupanovsky and Mutnovsky volcanoes and covering all other volcanoes in the area. In the vertical sections, the corresponding anomalies of high Vp/Vs ratio have mushroom shapes with the heads spreading along the bottom of the crust, which probably represent the underplating of magma material that feeds the volcanoes of the groups. The tomography results also reveal some important tectonic features, such as a V-shaped fault system in the Avacha Graben, which is the part of the MPZ.
... During recent decades, three volcanoes erupted: Klyuchevskoy, Bezymianny and Tolbachik ( Figure 6.1a). Seismic tomography (Koulakov et al., 2017(Koulakov et al., , 2020 highlights the existence of a ⇠30 km deep magmatic reservoir that is possibly connected to active volcanoes through a rifting zone developed after a recent subduction reconfiguration (Green et al., 2020;Koulakov et al., 2020). This large scale faulting structure can channel fluids and transfer pressure resulting in LP seismicity observed beneath the KVG at a broad range of depths Frank et al., 2018). ...
... S-wave velocity model slightly modified from Green et al. (2020) as shown in Figure 6.7. ...
... The length t should be long enough so that the wave has time to propagate through the entire seismic network. The largest inter-station distance in our network is close to 100 km and the slowest seismic velocity in the media is ⇠2 km/sec (Green et al., 2020). This implies that t should not be significantly smaller than 50s. ...
The occurrence and style of volcanic eruptions are largely controlled by the mechanisms ofmagma storage and transport from the deep roots of the system to the surface. This functioning often remains misunderstood, in particular for the deep part.In this thesis, we use geophysical measurements from seismic and GNSS networks to constrain the sources activated before and during the eruptions of Piton de la Fournaise (La Réunion, France) and the Klyuchevskoy Volcanic Group (Kamchatka, Russia).The dense networks of instruments present in these two contexts and their strong volcanic activity make them ideal natural laboratories for testing new sophisticated methods aimed atexploring and extracting information on magma transport.We thus applied seismic network methods to analyze in detail the large seismic tremor database associated with the 23 eruptions of Piton de la Fournaise that took place in the 2014-2022 time period. The mechanism of generation of this tremor is directly linked to the shallow magma degassing close to the eruptive sites. The monitoring of its different properties (frequency, amplitude, state) and the comparison with other observables available at the OVPF-IPGP allows a better constraint of the eruptive dynamics of the Piton de la Fournaise dominated by the interactions between the magma liquid and gas phases.The analysis of continuous data from the GNSS network at Piton de la Fournaise allowedus to image the pressure sources active in its deep and shallow system. We also performeda comparison of these pressure sources with the associated seismicity for the pre-eruptive and co-eruptive periods.At the Klyuchevskoy Volcanic Group, the analysis of the tremor over the 2015-2016 timeperiod reveals a wide spatial distribution of its sources spanning the entire crust and connecting different volcanoes of the group. Tremor activity is characterized by rapid vertical and lateral migrations explained by fast pressure transients and dynamic permeability
... The rejection of such extreme V SV models from the individual chains results in more stable posterior mean models. Similar findings have been reported recently by Green et al. (2020) for unrealistic layers above the Moho. ...
We present a new 3D shear-wave velocity model and Moho map of Scandinavia, which is based on the inversion of the merged phase dispersion curves from ambient noise and earthquake-generated Rayleigh waves. A classic two step inversion scheme is used where first maps of phase velocities at different periods are derived, and then a 1D transdimensional Bayesian method is applied to determine the VSV-depth structure. We assess the question of what compensates for the unusual high Scandes mountains and aim to identify the different tectonic domains of the adjacent continental lithosphere (Baltic Shield).
While the southern Scandes lacks a pronounced crustal root, we observe a crustal root below the northern Scandes that is decreasing towards the central Scandes. A ∼10 km thick high-density lower crustal layer is present below the northern Scandes and generally thickening to the east below the Baltic Shield. The lithosphere-asthenosphere boundary (LAB) below the Scandes is deepening as well from west to east with a sharp step and a strong VSV decrease with depth of 9% in the north and of 5.5% in the south. The LAB of the thinner lithosphere is at 150 km depth in the north and varies from 90 to 120 km depth in the south. Both LAB steps coincide with the mountain front. The central area shows rather smoothly varying structures (170 km LAB depth, −4% VSV with depth) towards the east and no clear spatial match with the front. We infer therefore distinct uplift mechanisms along the Scandes. The southern Scandes might sustain their topography due to dynamic support from the mantle, while the northern Scandes experience both crustal and mantle lithosphere isostasy. In both cases, we suspect a dynamic support from small-scale edge-driven convection that developed at the sharp lithospheric steps.
Beneath the Archean Karelia craton in northern Finland, we find low-velocity areas below 150 km depth while a 250 km deep lithospheric keel is imaged below the Paleoproterozic southern Finland. The Norrbotten craton in northern Sweden can be identified at mantle depths as a unit different from the Karelia craton, Scandes and Paleoproterozic central Sweden.
... This work was followed by ANT of many other volcanoes identifying shear wave velocity anomalies. Low-velocity anomalies are related to different kinds of volcanic features such as crustal shallow magma reservoirs (e. g., Masterlark et al., 2010;Stankiewicz et al., 2010;Nagaoka et al., 2012;Spica et al., 2015Spica et al., , 2017Fallahi et al., 2017;Chen et al., 2018;Huang et al., 2018;Obermann et al., 2019;Green et al., 2020), hydrothermal systems (e.g., Jay et al., 2012;Spica et al., 2015;Huang et al., 2017Huang et al., , 2018Wang et al., 2017;Calò et al., 2021) and caldera-related structures (e.g., Masterlark et al., 2010;Koulakov et al., 2014;Benediktsdóttir et al., 2017;Jeddi et al., 2017;Huang et al., 2018). Conversely, high-velocity anomalies are typically interpreted as consolidated dike complexes (e.g., Mordret et al., 2015;Huang et al., 2017), cooled igneous intrusions (e.g., Brenguier et al., 2007;Mordret et al., 2015;Wang et al., 2017;Huang et al., 2018) or solidified magma chambers (Mordret et al., 2015). ...
... The first application of the transdimensional approach to ANT was carried out on the Australian continent by Bodin et al. (2012). More recently, some transdimensional ANT allowed imaging sedimentary basins of the British Isles and the East Irish Sea (Galetti et al., 2017), a sedimentary basin and a deep crustal structure in southeast Australia (Crowder et al., 2019), magmatic and sedimentary structures beneath the Klyuchevskoy volcanic group in Kamchatka (Green et al., 2020), a deep crustal structure and extension in the North Sea (Crowder et al., 2021), as well as crustal-scale structures of the western Alps (Nouibat et al., 2022). ...
To better understand the recent internal structure of Misti volcano, we determined a 3D S-wave velocity model applying Ambient Noise Tomography (ANT). We used data from 23 broadband and short-period seismic stations temporarily installed at Misti volcano between March and December 2011. This dataset allowed us to obtain empirical Green's functions by cross-correlating seismic ambient noise signals. Then, we retrieved 104 dispersion curves using the frequency-time analysis (FTAN) and, through a non-linear multiscale inversion, we obtained nine 2-D Rayleigh waves group velocity maps for periods in the range 0.7 s - 2 s. Finally, we carried out the depth inversion through a Bayesian transdimensional inversion to obtain a 3-D S-wave velocity model down to 3 km depth. Our study highlights five relevant seismic velocity anomalies. We observed the presence of three high-velocity zones located in the west-northwest, southwest and southeast parts of the crater, that could be related to intrusive bodies possibly associated with the formation of Misti volcano. We also observed two low-velocity anomalies in the volcano's western and central parts, which coincide with previous studies' findings and are related to fractured and weakened materials associated with the external caldera collapse and recent eruption episodes.
