We analyse daily cross-correlation computed from continuous records by permanent stations operating in vicinity of the Klyuchevskoy group of volcanoes (Kamchatka). Seismic waves generated by volcanic tremors are clearly seen on the cross-correlations between some pairs of stations as strong signals at frequencies between 0.2 and 2 Hz and with traveltimes typically shorter than those corresponding to interstation propagation. First, we develop a 2-D sourcescanning algorithm based on summation of the envelops of cross-correlations to detect seismic tremors and to determine locations from which the strong seismic energy is continuously emitted. In an alternative approach, we explore the distinctive character of the cross-correlation waveforms corresponding to tremors emitted by different volcanoes and develop a phasematching method for detecting volcanic tremors. Application of these methods allows us to detect and to distinguish tremors generated by the Klyuchevskoy and the Tolbachik, volcanoes and to monitor evolution of their intensity in time.
S U M M A R YThe Kamchatka Peninsula lies over a vigorous subduction zone where Pacific and North American plates converge at a rate of almost 80 mm yr −1 . Earthquakes associated with the subduction process provide an excellent source of seismic data for the study of anisotropic properties of the upper mantle and crust overlying the downgoing lithospheric slab. We collected a large set of shear waves from events within the slab recorded by a variety of seismic stations in Kamchatka. Data from permanent and temporary networks cover the entire ∼700 km length of the subduction zone, with 50-200 km spacing between observing sites, resulting in an unprecedented coverage of the supraslab mantle wedge. We estimated shear wave splitting in selected S waves using two techniques and applied quality tests to ensure measurement stability. Fast directions vary from station to station, and they can vary with backazimuth at individual stations and with direction of propagation for individual sources. In over 350 measurements we recovered meaningful splitting delays, up to 1 s, with most delays in the 0.2-0.6 s range. Additionally, in nearly 200 measurements splitting could not be resolved, yielding 'NULL' observations. Anisotropic properties of the Kamchatka supraslab mantle wedge vary greatly along the volcanic arc and forearc of the subduction zone. Observed anisotropic indicators in the arc and forearc correlate spatially with some tectonic features (e.g. volcanoes). Inland of the volcanic arc most splitting values indicate trench-parallel fast polarization. We do not observe depth dependence in local S-wave splitting delays, consistent with a shallow coherence of anisotropic texture. In the vicinity of Petropavlovsk-Kamchatsky observed anisotropic indicators are coherently trench-normal, and thus consistent with 2-D corner flow. However, splitting above the fragmented slab edge near the Klyuchevskoy volcanic centre is variable and trench-oblique. Birefringence between Petropavlovsk and Klyuchevskoy is weak. Overall, our observations are incompatible with a regional slab-driven corner flow regime.
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.
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.
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